WO2020192084A1 - 图像预测方法、编码器、解码器以及存储介质 - Google Patents

图像预测方法、编码器、解码器以及存储介质 Download PDF

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WO2020192084A1
WO2020192084A1 PCT/CN2019/110809 CN2019110809W WO2020192084A1 WO 2020192084 A1 WO2020192084 A1 WO 2020192084A1 CN 2019110809 W CN2019110809 W CN 2019110809W WO 2020192084 A1 WO2020192084 A1 WO 2020192084A1
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Prior art keywords
image component
current block
component
image
processing
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PCT/CN2019/110809
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English (en)
French (fr)
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万帅
霍俊彦
马彦卓
张伟
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Oppo广东移动通信有限公司
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Priority to MX2021011662A priority Critical patent/MX2021011662A/es
Priority to CN202310642221.8A priority patent/CN116634153A/zh
Priority to CN202111034246.7A priority patent/CN113766233B/zh
Priority to EP19920728.3A priority patent/EP3955571B1/en
Priority to JP2021557116A priority patent/JP2022528835A/ja
Priority to AU2019437150A priority patent/AU2019437150A1/en
Priority to EP24176095.8A priority patent/EP4391537A2/en
Priority to KR1020217032516A priority patent/KR20210139327A/ko
Priority to CN201980085344.8A priority patent/CN113228647A/zh
Publication of WO2020192084A1 publication Critical patent/WO2020192084A1/zh
Priority to US17/482,191 priority patent/US20220014765A1/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
    • H—ELECTRICITY
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    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
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    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/103—Selection of coding mode or of prediction mode
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    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/14—Coding unit complexity, e.g. amount of activity or edge presence estimation
    • H—ELECTRICITY
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    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/176—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a block, e.g. a macroblock
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Definitions

  • the embodiments of the present application relate to the technical field of video coding and decoding, and in particular, to an image prediction method, an encoder, a decoder, and a storage medium.
  • cross-component prediction In the latest video coding standard H.266/Versatile Video Coding (VVC), cross-component prediction has been allowed; among them, cross-component Linear Model Prediction (CCLM) is typical One of the cross-component prediction techniques.
  • VVC Video Coding
  • CCLM Linear Model Prediction
  • one component can be used to predict another component (or its residual), such as predicting chrominance components from luminance components, or predicting luminance components from chrominance components, or predicting chrominance components from chrominance components, etc. .
  • the embodiments of the present application provide an image prediction method, an encoder, a decoder, and a storage medium, which balance the statistical characteristics of the components before cross-component prediction, thereby not only improving the prediction efficiency, but also improving the coding and decoding efficiency of video images.
  • an embodiment of the present application provides an image prediction method applied to an encoder or a decoder, and the method includes:
  • Preprocessing at least one image component of the current block to obtain at least one image component after preprocessing
  • an embodiment of the present application provides an image prediction method applied to an encoder or a decoder, and the method includes:
  • Determining the reference value of the first image component of the current block in the image wherein the reference value of the first image component of the current block is the first image component value of the neighboring pixels of the current block;
  • the model parameters of the prediction model are calculated by using the filtered reference value, wherein the prediction model is used to map the value of the first image component of the current block to the value of the second image component of the current block ,
  • the second image component is different from the first image component.
  • an encoder which includes a first determining unit, a first processing unit, and a first building unit, wherein:
  • the first determining unit is configured to determine at least one image component of the current block in the image
  • the first processing unit is configured to preprocess at least one image component of the current block to obtain at least one image component after preprocessing;
  • the first construction unit is configured to construct a prediction model according to the at least one image component after the preprocessing; wherein the prediction model is used to perform cross-component prediction processing on at least one image component of the current block.
  • an embodiment of the present application provides an encoder.
  • the encoder includes a first memory and a first processor, where:
  • the first memory is configured to store a computer program that can run on the first processor
  • the first processor is configured to execute the method described in the first aspect or the second aspect when running the computer program.
  • an embodiment of the present application provides a decoder, which includes a second determining unit, a second processing unit, and a second building unit, wherein:
  • the second determining unit is configured to determine at least one image component of the current block in the image
  • the second processing unit is configured to preprocess at least one image component of the current block to obtain at least one image component after preprocessing
  • the second construction unit is configured to construct a prediction model according to the at least one image component after the preprocessing; wherein the prediction model is used to perform cross-component prediction processing on at least one image component of the current block.
  • an embodiment of the present application provides a decoder, the decoder includes a second memory and a second processor, wherein:
  • the second memory is used to store a computer program that can run on the second processor
  • the second processor is configured to execute the method described in the first aspect or the second aspect when the computer program is running.
  • an embodiment of the present application provides a computer storage medium that stores an image prediction program, and when the image prediction program is executed by a first processor or a second processor, it implements the first aspect or the first aspect. The method described in the two aspects.
  • the embodiments of the present application provide an image prediction method, an encoder, a decoder, and a storage medium, which determine at least one image component of a current block in an image; preprocess at least one image component of the current block to obtain at least An image component; construct a prediction model based on at least one image component after preprocessing, and the prediction model is used to perform cross-component prediction processing on at least one image component of the current block; in this way, at least one image component of the current block Before prediction, the at least one image component is preprocessed first, which can balance the statistical characteristics of each image component before cross-component prediction, thereby improving the prediction efficiency; in addition, because the prediction value of the image component predicted by the prediction model is more It is close to the true value, so that the prediction residual of the image component is small, so that the bit rate transmitted in the encoding and decoding process is small, and the encoding and decoding efficiency of the video image is also improved.
  • FIG. 1 is a schematic diagram of the composition structure of a traditional cross-component prediction architecture provided by an embodiment of this application;
  • FIG. 2 is a schematic diagram of the composition of a video encoding system provided by an embodiment of the application.
  • FIG. 3 is a schematic diagram of the composition of a video decoding system provided by an embodiment of the application.
  • FIG. 4 is a schematic flowchart of an image prediction method provided by an embodiment of the application.
  • FIG. 5 is a schematic flowchart of another image prediction method provided by an embodiment of the application.
  • FIG. 6 is a schematic diagram of the composition structure of an improved cross-component prediction architecture provided by an embodiment of this application.
  • FIG. 7 is a schematic diagram of the composition structure of another improved cross-component prediction architecture provided by an embodiment of this application.
  • FIG. 8 is a schematic diagram of the composition structure of an encoder provided by an embodiment of the application.
  • FIG. 9 is a schematic diagram of a specific hardware structure of an encoder provided by an embodiment of the application.
  • FIG. 10 is a schematic diagram of the composition structure of a decoder provided by an embodiment of the application.
  • FIG. 11 is a schematic diagram of a specific hardware structure of a decoder provided by an embodiment of the application.
  • the first image component, the second image component, and the third image component are generally used to characterize the coding block; among them, the three image components are a luminance component, a blue chrominance component, and a red chrominance component.
  • the luminance component is usually represented by the symbol Y
  • the blue chrominance component is usually represented by the symbol Cb or U
  • the red chrominance component is usually represented by the symbol Cr or V; in this way, the video image can be represented in YCbCr format or YUV. Format representation.
  • the first image component may be a luminance component
  • the second image component may be a blue chrominance component
  • the third image component may be a red chrominance component
  • H.266/VCC proposed CCLM cross-component prediction technology.
  • the cross-component prediction technology based on CCLM can not only realize the prediction of the luminance component to the chrominance component, that is, the prediction of the first image component to the second image component, or the first image component to the third image component, but also the color The prediction from the degree component to the luminance component, that is, the prediction from the second image component to the first image component, or the third image component to the first image component, and even the prediction between the chrominance component and the chrominance component, that is, the first Prediction from the second image component to the third image component, or the third image component to the second image component, etc.
  • the following will describe the prediction from the first image component to the second image component as an example, but the technical solution of the embodiment of the present application can also be applied to the prediction of other image components.
  • FIG. 1 shows a schematic diagram of the composition structure of a traditional cross-component prediction architecture provided by an embodiment of the present application.
  • the first image component for example, represented by Y component
  • the second image component for example, represented by U component
  • the video image adopts the 4:2:0 format of YUV, the Y component and U component
  • the method of using the Y component to predict the third image component for example, represented by the V component
  • the traditional cross-component prediction architecture 10 may include a Y component coding block 110, a resolution adjustment unit 120, a Y 1 component coding block 130, a U component coding block 140, a prediction model 150, and a cross component prediction unit 160.
  • the Y component of the video image is represented by a Y component coding block 110 with a size of 2N ⁇ 2N.
  • the larger bolded box here is used to highlight the Y component coding block 110, and the surrounding gray solid circle is used to indicate the Y component coding block.
  • the neighboring reference value Y(n) of 110; the U component of the video image is represented by the U-component coding block 140 of size N ⁇ N.
  • the larger block in bold here is used to highlight the U-component coding block 140, and the surrounding gray
  • the solid circle is used to indicate the adjacent reference value C(n) of the U component encoding block 140; since the Y component and the U component have different resolutions, the resolution adjustment unit 120 needs to adjust the resolution of the Y component to obtain N ⁇ N-size Y 1 component coding block 130; for Y 1 component coding block 130, the larger bolded box here is used to highlight Y 1 component coding block 130, and the surrounding gray solid circle is used to indicate Y 1 component coding block 130 adjacent reference values Y 1 (n-); and Y 1 (n-) and the U component encoding block adjacent to the reference value C (n) 140 of the prediction model can be constructed by a Y component value encoding neighboring reference blocks 130 150; According to the Y component of the Y 1 component encoding block 130, the pixel value and the prediction model 150 are reconstructed, component prediction can be performed across the component prediction unit 160, and the U component prediction value
  • an embodiment of the present application provides an image prediction method. Firstly, at least one image component of the current block in the image is determined; then at least one image component of the current block is preprocessed to obtain at least one image after preprocessing.
  • the prediction model is used to perform cross-component prediction processing on at least one image component of the current block; in this way, at least one image component of the current block is Before prediction, the at least one image component is preprocessed first, which can balance the statistical characteristics of each image component before cross-component prediction, thereby not only improving the prediction efficiency, but also improving the coding and decoding efficiency of the video image.
  • the video encoding system 20 includes a transform and quantization unit 201, an intra-frame estimation unit 202, and an intra-frame
  • the encoding unit 209 can implement header information encoding and context-based Adaptive Binary Arithmatic Coding (CABAC).
  • CABAC Sample Adaptive Offset
  • a coding block can be obtained by dividing the coding tree block (Coding Tree Unit, CTU), and then the residual pixel information obtained after intra-frame or inter-frame prediction is performed by the transform and quantization unit 201.
  • the encoding block is transformed, including transforming the residual information from the pixel domain to the transform domain, and quantizing the resulting transform coefficients to further reduce the bit rate;
  • the intra-frame estimation unit 202 and the intra-frame prediction unit 203 are used to The coding block performs intra prediction; specifically, the intra estimation unit 202 and the intra prediction unit 203 are used to determine the intra prediction mode to be used to code the coding block;
  • the motion compensation unit 204 and the motion estimation unit 205 are used to perform Inter-frame prediction coding of the received coding block with respect to one or more blocks in one or more reference frames to provide temporal prediction information;
  • the motion estimation performed by the motion estimation unit 205 is a process of generating a motion vector, the The motion vector can estimate the motion of the coded block, and then the
  • the context content can be based on adjacent coding
  • the block can be used to encode information indicating the determined intra-frame prediction mode and output the code stream of the video signal; and the decoded image buffer unit 210 is used to store reconstructed video blocks for prediction reference. As the encoding of the video image progresses, new reconstructed video blocks are continuously generated, and these reconstructed video blocks are all stored in the decoded image buffer unit 210.
  • the video decoding system 30 includes a decoding unit 301, an inverse transform and inverse quantization unit 302, and an intra-frame
  • the code stream of the video signal is output; the code stream is input into the video decoding system 30, and first passes through the decoding unit 301 to obtain the decoded transform coefficient;
  • the inverse transform and inverse quantization unit 302 performs processing to generate a residual block in the pixel domain;
  • the intra prediction unit 303 can be used to generate data based on the determined intra prediction mode and the data from the previous decoded block of the current frame or picture The prediction data of the current video block to be decoded;
  • the motion compensation unit 304 determines the prediction information for the video block to be decoded by analyzing the motion vector and other associated syntax elements, and uses the prediction information to generate the video being decoded
  • the predictive block of the block; the residual block from the inverse transform and inverse quantization unit 302 and the corresponding predictive block generated by the intra prediction unit 303 or the motion compensation unit 304 are summed to form a decoded video block;
  • the decoded video block is passed through the filtering unit 305 in order to remove the block arti
  • the embodiment of this application is mainly applied to the part of the intra prediction unit 203 shown in FIG. 2 and the part of the intra prediction unit 303 shown in FIG. 3; that is, the embodiment of this application can be applied to both video coding systems and It can be applied to a video decoding system, which is not specifically limited in the embodiment of the present application.
  • FIG. 4 shows a schematic flowchart of an image prediction method provided by an embodiment of the present application.
  • the method may include:
  • S401 Determine at least one image component of the current block in the image
  • S402 Perform preprocessing on at least one image component of the current block to obtain at least one image component after preprocessing;
  • S403 Construct a prediction model according to the at least one image component after the preprocessing; wherein the prediction model is used to perform cross-component prediction processing on at least one image component of the current block.
  • each image block currently to be encoded can be called an encoding block.
  • each coding block may include a first image component, a second image component, and a third image component; and the current block is the coding of the first image component, the second image component, or the third image component currently to be predicted in the video image. Piece.
  • the image prediction method in the embodiments of the present application can be applied to both a video encoding system and a video decoding system, and can even be applied to both a video encoding system and a video decoding system. Specific restrictions.
  • At least one image component of the current block in the image is first determined; then at least one image component of the current block is preprocessed to obtain at least one image component after preprocessing;
  • Component construct a prediction model, which is used to perform cross-component prediction processing on at least one image component of the current block; in this way, before predicting at least one image component of the current block, first perform the at least one image component Preprocessing can balance the statistical characteristics of each image component before cross-component prediction, which not only improves the prediction efficiency, but also improves the coding and decoding efficiency of the video image.
  • the method may further include:
  • the reference value of the first image component of the current block and/or the reference value of the second image component of the current block is obtained; wherein, the first image component is used when constructing the prediction model
  • the component used for prediction, the second image component is the component predicted when the prediction model is constructed.
  • At least one image component of the current block may be the first image component, the second image component, or even the first image component and the second image component.
  • the first image component is the component used for prediction when constructing the prediction model, and can also be called the image component to be referenced
  • the second image component is the component that is predicted when the prediction model is constructed, and can also be called the image component to be predicted.
  • the component used for prediction when constructing the prediction model is the brightness component, and the component predicted when constructing the prediction model is the chrominance component, that is, the first image component is the brightness Component, the second image component is the chrominance component; or, assuming that the prediction of the chrominance component to the luminance component is achieved through the prediction model, the component used for prediction when the prediction model is constructed is the chrominance component, which is predicted when the prediction model is constructed
  • the component of is the luminance component, that is, the first image component is the chrominance component, and the second image component is the luminance component.
  • the reference value of the first image component of the current block and/or the reference value of the second image component of the current block can be obtained.
  • the difference in statistical characteristics among various image components may be considered. That is, before performing cross-component prediction on at least one image component through the prediction model, the at least one image component can also be preprocessed according to the statistical characteristics of the image component, such as filtering, grouping, value correction, and quantization. Or to quantify processing and so on. Therefore, in some embodiments, for S402, the preprocessing of at least one image component of the current block to obtain at least one image component after preprocessing may include:
  • the first image component is first processed using a preset processing mode; wherein, the The preset processing mode includes at least one of the following: filtering processing, grouping processing, value correction processing, quantization processing and dequantization processing;
  • the processed value of the first image component of the current block is obtained.
  • the preset can be used The processing mode performs first processing on the first image component.
  • filtering processing may be used to perform the first processing on the first image component
  • grouping processing may be used to perform the first processing on the first image component
  • value correction processing may also be used to perform the first processing on the first image component.
  • One processing, or quantization processing can also be used to perform first processing on the first image component, or inverse quantization processing (also called dequantization processing) can also be used to perform first processing on the first image component, etc.
  • dequantization processing also be used to perform first processing on the first image component, etc.
  • the application examples are not specifically limited.
  • the processing for the first image component may be for the adjacent reference pixel value of the first image component, or for the reconstructed pixel value of the first image component, or even for the Other pixel values of the first image component are processed; in the embodiment of the present application, the setting is performed according to the actual situation of the prediction model, which is not specifically limited in the embodiment of the present application.
  • the prediction model uses the luminance component to predict the chrominance component, in order to improve the prediction efficiency, that is, to improve the accuracy of the predicted value, it is necessary to process the luminance component and/or chrominance component according to the preset processing mode, such as The reconstructed pixel value corresponding to the luminance component is processed according to the preset processing mode.
  • the preset processing mode adopts value correction processing, because the luminance component and the chrominance component have different statistical characteristics, a deviation factor can be obtained according to the difference in the statistical characteristics of the two image components; then the deviation factor is used to perform the luminance component Value correction processing (such as adding the reconstructed pixel value corresponding to the brightness component to the deviation factor) to balance the statistical characteristics of the image components before cross-component prediction, so as to obtain the processed brightness component, which is then based on the prediction
  • the predicted value of the chrominance component predicted by the model is closer to the true value of the chrominance component;
  • the preset processing mode adopts filtering processing, since the luminance component and the chrominance component have different statistical characteristics, according to the statistics of the two image components Differences in characteristics, then the luminance component can be filtered to balance the statistical characteristics of the image components before cross-component prediction, so that the processed luminance component is obtained, and then the chrominance component predicted by the prediction model The value is closer to the true value of the chromin
  • the predicted value of the chroma component predicted by the prediction model is closer to the true chroma component
  • the quantization process and inverse quantization process are involved in the process of using the prediction model to predict the chrominance component, and because the luminance component and the chrominance component have different statistical characteristics, according to the statistics of the two image components Differences in characteristics may result in differences between quantization and dequantization.
  • the preset processing mode uses quantization, then the luminance and/or chrominance components can be quantized to balance the image components before cross-component prediction If the statistical characteristics of the chrominance component are obtained, the processed luminance component and/or the processed chrominance component are obtained.
  • the predicted value of the chrominance component predicted according to the prediction model is closer to the true value of the chrominance component; if The preset processing mode adopts dequantization processing, then the luminance component and/or chrominance component can be dequantized to balance the statistical characteristics of the image components before cross-component prediction, so as to obtain the processed luminance component and/or After processing the chrominance component, the predicted value of the chrominance component predicted by the prediction model at this time is closer to the true value of the chrominance component; thus, the accuracy of the predicted value is improved, and the prediction efficiency is improved; The obtained prediction value of the chrominance component is closer to the true value, so that the prediction residual of the chrominance component is smaller, so that the bit rate transmitted in the encoding and decoding process is less, and the encoding and decoding efficiency of the video image is also improved.
  • the preset processing mode can be used to perform processing on the first image component based on the reference value of the first image component of the current block.
  • One image component is processed to balance the statistical characteristics of the image components before cross-component prediction, and then the processed value of the first image component of the current block is obtained; it can also be based on the reference value of the second image component of the current block, using preset processing
  • the mode processes the first image component to balance the statistical characteristics of the image components before cross-component prediction, and then obtains the processed value of the first image component of the current block; it can even be based on the reference value of the first image component of the current block and
  • the first image component is processed using a preset processing mode to balance the statistical characteristics of the image components before cross-component prediction, and then the processed value of the first image component of the current block is obtained;
  • the predicted value of the second image component predicted by the prediction model is closer to the real value; wherein, the prediction model can realize the second image component through the first image component Cross-component prediction.
  • the resolution of each image component is not the same.
  • the resolution of the image component also needs to be adjusted (including up-sampling or down-sampling the image component) to achieve the goal Resolution.
  • using the preset processing mode to perform the first processing and resolution adjustment on the first image component can be cascaded processing, and using the preset processing mode to perform the first processing and resolution adjustment on the first image component can also be combined processing.
  • the method may further include:
  • the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, the resolution of the first image component is adjusted; wherein, the resolution Adjustment includes up-sampling adjustment or down-sampling adjustment;
  • the reference value of the first image component of the current block is updated; wherein the adjusted resolution of the first image component and the second image component The resolution is the same.
  • resolution adjustment that is, resolution mapping
  • maps the resolution of the first image component to the adjusted resolution of the first image component here, the resolution can be achieved through up-sampling adjustment or down-sampling adjustment. Rate adjustment or resolution mapping.
  • the resolution adjustment can be performed in the first image component using the preset processing mode. prior to.
  • the resolution of the first image component can be determined Adjust the resolution of the first image component based on the adjusted resolution, and update the reference value of the first image component of the current block based on the adjusted resolution of the first image component.
  • the method may further include:
  • the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, the resolution of the first image component is adjusted; wherein, the resolution Adjustment includes up-sampling adjustment or down-sampling adjustment;
  • the processing value of the first image component of the current block is updated; wherein the adjusted resolution of the first image component and the second image component The resolution is the same.
  • the resolution adjustment can also be performed in the first image component using the preset processing mode. After processing. That is to say, after preprocessing at least one image component of the current block, if the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, the resolution of the first image component can be determined The resolution adjustment is performed at the rate, and based on the adjusted resolution of the first image component, the processing value of the first image component of the current block is updated.
  • the preprocessing at least one image component of the current block to obtain at least one image component after preprocessing may include:
  • the second processing is performed on the first image component; wherein the second processing includes correlation processing of up-sampling and preset processing mode, or correlation processing of down-sampling and preset processing mode ;
  • the processed value of the first image component of the current block is obtained; wherein the resolution of the processed first image component of the current block and the resolution of the second image component of the current block after processing The rate is the same.
  • the processing value of the first image component of the current block may be processed and resolved at the same time. Obtained after rate adjustment. That is to say, if the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, it can be based on the reference value of the first image component of the current block and/or the second image of the current block.
  • the reference value of the component, the second processing is performed on the first image component, the second processing integrates the first processing and the resolution adjustment two processing methods, the second processing can include upsampling and related processing of the preset processing mode , Or related processing of downsampling and preset processing modes, etc.; in this way, according to the result of the second processing, the processed value of the first image component of the current block can be obtained, and the resolution of the first image component of the current block after processing The rate is the same as the resolution of the second image component of the current block.
  • the prediction model uses the luminance component to predict the chrominance component.
  • the image component to be predicted is the chrominance component
  • the image component to be used is the luminance component; since the resolution of the luminance component and the chrominance component are different.
  • the resolution of the brightness component needs to be adjusted at this time, such as down-sampling the brightness component to make the adjusted
  • the resolution of the luminance component meets the target resolution; on the contrary, if the chrominance component is used to predict the luminance component, after the target resolution of the luminance component is obtained, since the resolution of the chrominance component does not meet the target resolution, color matching is required.
  • Adjust the resolution of the chrominance component such as upsampling the chrominance component, so that the resolution of the adjusted chrominance component meets the target resolution; in addition, if the blue chrominance component is used to predict the red chrominance component, After reaching the target resolution of the red chrominance component, since the resolution of the blue chrominance component meets the target resolution, there is no need to adjust the resolution of the blue chrominance component at this time. It has been ensured that the resolution of the blue chrominance component meets the target Resolution; In this way, the subsequent image component prediction can be performed at the same resolution.
  • the model parameters of the prediction model need to be determined according to the at least one image component after preprocessing, so as to construct the prediction model. Therefore, in some embodiments, for S403, the constructing a prediction model based on the at least one image component after the preprocessing may include:
  • the prediction model is constructed.
  • the prediction model in the embodiment of the present application may be a linear model, such as the cross-component prediction technology of CCLM; the prediction model may also be a non-linear model, such as multi-model CCLM (Multiple Model CCLM, MMLM) cross-component prediction Technology, it is composed of multiple linear models.
  • CCLM Multiple Model CCLM, MMLM
  • the embodiment of the present application will take the prediction model as a linear model as an example for the following description, but the image prediction method of the embodiment of the present application can also be applied to a nonlinear model.
  • the model parameters include a first model parameter (represented by ⁇ ) and a second model parameter (represented by ⁇ ).
  • ⁇ and ⁇ There are many ways to calculate ⁇ and ⁇ . It can be a preset factor calculation model constructed by the least squares method, or a preset factor calculation model constructed by the maximum and minimum values, or even other ways.
  • the preset factor calculation model is not specifically limited in the embodiment of this application.
  • the neighboring reference pixel values around the current block (such as the neighboring reference value of the first image component and the neighboring reference value of the second image component) can be used.
  • the adjacent reference value of the component and the adjacent reference value of the second image component are obtained after preprocessing) to obtain the minimized regression error, specifically, as shown in formula (1):
  • L(n) represents the adjacent reference value of the first image component corresponding to the left side and the upper side of the current block after down-sampling
  • C(n) represents the first image component corresponding to the left side and the upper side of the current block.
  • the preset factor calculation model constructed by the maximum and minimum values as an example, it provides a simplified version of the derivation method of model parameters. Specifically, it can search for the adjacent reference value of the largest first image component and the smallest first image component.
  • the adjacent reference values of image components are used to derive model parameters based on the principle of "two points determine one line", as shown in the preset factor calculation model shown in formula (2):
  • L max and L min represent the maximum and minimum values found in the adjacent reference values of the first image component corresponding to the left side and the upper side of the current block after down-sampling
  • C max and C min represent L max The adjacent reference value of the second image component corresponding to the reference pixel at the position corresponding to L min .
  • the first model parameter ⁇ and the second model parameter ⁇ can also be obtained through the calculation of formula (2).
  • a predictive model can be constructed. Specifically, based on ⁇ and ⁇ , assuming that the second image component is predicted based on the first image component, the constructed prediction model is shown in equation (3),
  • i, j represent the position coordinates of the pixel in the current block
  • i represents the horizontal direction
  • j represents the vertical direction
  • Pred C [i,j] represents the pixel corresponding to the pixel with the location coordinate [i,j] in the current block
  • Rec L [i, j] represents the reconstructed value of the first image component corresponding to the pixel with the position coordinate [i, j] in the same current block (down-sampled).
  • the method may further include:
  • the luminance component can be used to perform prediction processing on the chrominance component, so that the predicted value of the chrominance component can be obtained.
  • the image component can be predicted according to the prediction model; on the one hand, the first image component can be used to predict the second image component, for example, the luminance component can be used to predict the chrominance component.
  • the predicted value of the chrominance component on the other hand, the second image component can also be used to predict the first image component, for example, the chrominance component can be used to predict the luminance component to obtain the predicted value of the luminance component; on the other hand, the first image component can also be used
  • the second image component predicts the third image component, for example, the blue chroma component is used to predict the red chroma component, and the predicted value of the red chroma component is obtained; since the prediction model is constructed, the embodiment of the present application will perform the method for at least one image component of the current block Preprocessing is to balance the statistical characteristics of the image components before cross-component prediction, and then use the processed image components to construct a prediction model, so as to achieve the purpose of improving prediction efficiency.
  • This embodiment provides an image prediction method, which determines at least one image component of a current block in an image; preprocesses at least one image component of the current block to obtain at least one image component after preprocessing; A prediction model is constructed for one image component, and the prediction model is used to perform cross-component prediction processing on at least one image component of the current block; in this way, before predicting at least one image component of the current block, first the at least one image
  • the component preprocessing can balance the statistical characteristics of each image component before cross-component prediction, and improve the prediction efficiency; in addition, because the predicted value of the image component predicted by the prediction model is closer to the true value, the prediction residual of the image component The difference is small, so that the bit rate transmitted in the encoding and decoding process is less, and the encoding and decoding efficiency of the video image is also improved.
  • FIG. 5 shows a schematic flowchart of another image prediction method provided by an embodiment of the present application.
  • the method may include:
  • S501 Determine the reference value of the first image component of the current block in the image; wherein the reference value of the first image component of the current block is the first image component value of the neighboring pixels of the current block;
  • S502 Perform filtering processing on the reference value of the first image component of the current block to obtain a filtered reference value
  • S503 Calculate model parameters of a prediction model by using the filtered reference value, where the prediction model is used to map the value of the first image component of the current block to the value of the second image component of the current block Value, the second image component is different from the first image component.
  • each image block currently to be encoded can be called an encoding block.
  • each coding block may include a first image component, a second image component, and a third image component; and the current block is the coding of the first image component, the second image component, or the third image component currently to be predicted in the video image. Piece.
  • the image prediction method can be applied to both a video encoding system and a video decoding system, or even a video encoding system and a video decoding system at the same time, which is not specifically limited in the embodiment of the present application.
  • the reference value of the first image component of the current block in the image is first determined.
  • the reference value of the first image component of the current block is the first image component value of the neighboring pixels of the current block;
  • the reference value of an image component is filtered to obtain the filtered reference value;
  • the filtered reference value is then used to calculate the model parameters of the prediction model, where the prediction model is used to map the value of the first image component of the current block to
  • the value of the second image component of the current block is different from the first image component; in this way, before at least one image component of the current block is predicted, the at least one image component is first filtered to balance
  • the statistical characteristics of each image component before cross-component prediction not only improves the prediction efficiency, but also improves the coding and decoding efficiency of the video image.
  • the calculation of the model parameters of the component prediction model by using the filtered reference value may include:
  • the model parameters of the prediction model are calculated by using the filtered reference value and the reference value of the second image component of the current block.
  • the reference value of the second image component of the current block is obtained, and then the prediction is calculated according to the filtered reference value and the reference value of the second image component of the current block
  • the model parameters of the model are used to construct a prediction model based on the calculated model parameters.
  • the predicted value of the image component predicted by the prediction model is closer to the true value, so that the prediction residual of the image component is smaller, so that it is transmitted during the encoding and decoding process
  • the bit rate is less, and the coding and decoding efficiency of video images is also improved.
  • performing filtering processing on the reference value of the first image component of the current block to obtain the filtered reference value may include:
  • the first adjustment process is performed on the reference value of the first image component of the current block, and the current The reference value of the first image component of the block, wherein the first adjustment processing includes one of the following: down-sampling filtering, up-sampling filtering;
  • the method may also include:
  • the preset processing mode includes at least one of the following: filtering processing, grouping processing, Value correction processing, quantization processing, dequantization processing, low-pass filtering and adaptive filtering.
  • performing filtering processing on the reference value of the first image component of the current block to obtain the filtered reference value may include:
  • the second adjustment processing includes: down-sampling and smoothing filtering, or up-sampling and smoothing filtering.
  • the resolution of each image component is not the same.
  • the resolution of the image component also needs to be adjusted (including up-sampling or down-sampling the image component).
  • Reach the target resolution Specifically, resolution adjustment, that is, resolution mapping, maps the resolution of the first image component to the adjusted resolution of the first image component; here, the resolution can be achieved through up-sampling adjustment or down-sampling adjustment. Adjustment or resolution mapping.
  • the filtering processing and resolution adjustment of the first image component can be processed in cascade, for example, the resolution adjustment is performed before the filtering processing of the first image component, or after the filtering processing is performed on the first image component Perform resolution adjustment; in addition, it is also possible to perform joint processing of filtering processing and resolution adjustment of the first image component (that is, the first adjustment processing).
  • the calculation of the model parameters of the component prediction model by using the filtered reference value may include:
  • Determining the reference value of the second image component of the current block wherein the reference value of the second image component of the current block is the second image component value of the neighboring pixels of the current block;
  • the method may further include:
  • the value of the first image component of the current block is mapped to obtain the predicted value of the second image component of the current block.
  • the reference value of the second image component of the current block may be the value of the second image component of the neighboring pixels of the current block; in this way, after the reference value of the second image component is determined, the reference value and the filtered reference value The determined reference value of the second image component is used to calculate the model parameters of the prediction model, and the prediction model is constructed according to the calculated model parameters.
  • the predicted value of the image component predicted by the prediction model is closer to the true value, so that the prediction of the image component
  • the residual is small, so that the bit rate transmitted in the encoding and decoding process is less, and the encoding and decoding efficiency of the video image is also improved.
  • FIG. 6 shows a schematic diagram of the composition structure of an improved cross-component prediction architecture provided by an embodiment of the present application.
  • the improved cross-component prediction architecture 60 may also include a processing unit 610, which is mainly used to precede the cross-component prediction unit 160 Perform correlation processing on at least one image component.
  • the processing unit 610 may be located before the resolution adjustment unit 120 or after the resolution adjustment unit 120; for example, in FIG.
  • the processing unit 610 is located after the resolution adjustment unit 120, and performs related processing on the Y component, such as Filtering processing, grouping processing, value correction processing, quantization processing and inverse quantization processing, etc., so that a more accurate prediction model can be constructed so that the predicted value of the U component obtained by the prediction is closer to the true value.
  • the Y component is used to predict the U component. Since the Y component current block 110 and the U component current block 140 have different resolutions, the resolution adjustment unit 120 is required at this time.
  • the resolution of the Y component is adjusted to obtain the Y 1 component current block 130 with the same resolution as the U component current block 140; before this, the Y component can also be processed by the processing unit 610 to obtain the Y 1 component The current block 130; and then using the adjacent reference value Y 1 (n) of the current block 130 of the Y 1 component and the adjacent reference value C(n) of the current block 140 of the U component to construct the prediction model 150; according to the current Y 1 component
  • the Y component of the block 130 reconstructs the pixel value and the prediction model 150, through the cross-component prediction unit 160 to perform image component prediction to obtain the U component prediction value; since the Y component is processed before the cross-component prediction, according to the processed luminance component
  • the resolution adjustment unit 120 and the processing unit 610 may perform cascade processing on image components (for example, the resolution adjustment unit 120 first performs resolution adjustment, and then the processing unit 610 performs related processing; or The processing unit 610 performs related processing, and then the resolution adjustment unit 120 performs resolution adjustment), and the image components can be jointly processed (for example, the resolution adjustment unit 120 and the processing unit 610 are combined and then processed).
  • FIG. 7 shows a schematic diagram of the composition structure of another improved cross-component prediction architecture provided by an embodiment of the present application. Based on the improved cross-component prediction architecture 60 shown in FIG. 6, the improved cross-component prediction architecture shown in FIG.
  • the joint unit 710 includes the functions of the resolution adjustment unit 120 and the processing unit 510, which can not only implement resolution adjustment of at least one image component, but also implement related processing of at least one image component, such as filtering processing and grouping. Processing, value correction processing, quantization processing, and inverse quantization processing, etc., so that a more accurate prediction model 150 can be constructed.
  • the U component predicted value predicted by the prediction model 150 is closer to the real value, thereby improving the prediction efficiency , It also improves the coding and decoding efficiency of video images.
  • the prediction model when the image prediction method is applied to the encoder side, the prediction model can be calculated according to the reference value of the image component to be predicted in the current block and the reference value of the image component to be referenced in the current block. Model parameters, and then write the calculated model parameters into the code stream; the code stream is transmitted from the encoder side to the decoder side; correspondingly, when the image prediction method is applied to the decoder side, the code stream can be analyzed
  • the model parameters of the prediction model are obtained to construct a prediction model, and the prediction model is used to perform cross-component prediction processing on at least one image component of the current block.
  • This embodiment provides an image prediction method to determine a reference value of a first image component of a current block in an image, where the reference value of the first image component of the current block is the value of the first image component of adjacent pixels of the current block; Perform filtering processing on the reference value of the first image component of the current block to obtain the filtered reference value; use the filtered reference value to calculate the model parameters of the prediction model, which is used to take the first image component of the current block
  • the value is mapped to the value of the second image component of the current block, the second image component is different from the first image component; in this way, before at least one image component of the current block is predicted, the at least one image
  • the component preprocessing can balance the statistical characteristics of each image component before cross-component prediction, and improve the prediction efficiency; in addition, because the predicted value of the image component predicted by the prediction model is closer to the true value, the prediction residual of the image component The difference is small, so that the bit rate transmitted in the encoding and decoding process is less, and the encoding and de
  • FIG. 8 shows a schematic diagram of the composition structure of an encoder 80 provided by an embodiment of the present application.
  • the encoder 80 may include: a first determining unit 801, a first processing unit 802, and a first constructing unit 803, where
  • the first determining unit 801 is configured to determine at least one image component of the current block in the image
  • the first processing unit 802 is configured to preprocess at least one image component of the current block to obtain at least one image component after preprocessing;
  • the first construction unit 803 is configured to construct a prediction model according to the at least one image component after the preprocessing; wherein the prediction model is used to perform cross-component prediction processing on at least one image component of the current block.
  • the encoder 80 may further include a first statistics unit 804 and a first acquisition unit 805, wherein,
  • the first statistical unit 804 is configured to perform characteristic statistics on at least one image component of the current block; wherein the at least one image component includes a first image component and/or a second image component;
  • the first obtaining unit 805 is configured to obtain the reference value of the first image component of the current block and/or the reference value of the second image component of the current block according to the result of characteristic statistics; wherein, the first image component An image component is a component used for prediction when constructing the prediction model, and the second image component is a component predicted when constructing the prediction model.
  • the first processing unit 802 is further configured to use a preset processing mode based on the reference value of the first image component of the current block and/or the reference value of the second image component of the current block Perform first processing on the first image component; wherein the preset processing mode includes at least one of the following: filtering processing, grouping processing, value correction processing, quantization processing, and dequantization processing;
  • the first obtaining unit 805 is further configured to obtain the processed value of the first image component of the current block according to the result of the first processing.
  • the encoder 80 may further include a first adjustment unit 806 and a first update unit 807, where:
  • the first adjustment unit 806 is configured to perform the resolution of the first image component when the resolution of the first image component of the current block is different from the resolution of the second image component of the current block Resolution adjustment; wherein the resolution adjustment includes up-sampling adjustment or down-sampling adjustment;
  • the first update unit 807 is configured to update the reference value of the first image component of the current block based on the adjusted resolution of the first image component; wherein The resolution is the same as the resolution of the second image component.
  • the first adjustment unit 806 is further configured to: when the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, Resolution adjustment is performed on the resolution of the image components; wherein the resolution adjustment includes up-sampling adjustment or down-sampling adjustment;
  • the first update unit 807 is further configured to update the processing value of the first image component of the current block based on the adjusted resolution of the first image component; wherein, the adjusted first image component The resolution of is the same as the resolution of the second image component.
  • the first adjustment unit 806 is further configured to, when the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, based on the current block And/or the reference value of the first image component of the current block and/or the reference value of the second image component of the current block, performing a second processing on the first image component; wherein the second processing includes upsampling and preset processing Mode-related processing, or down-sampling and related processing of preset processing modes;
  • the first obtaining unit 805 is further configured to obtain the processed value of the first image component of the current block according to the result of the second processing; wherein the processed value of the first image component of the current block is the same as The resolutions of the second image components of the current block are the same.
  • the first determining unit 801 is further configured to determine the model parameter of the prediction model according to the processing value of the first image component and the reference value of the second image component;
  • the first construction unit 803 is configured to construct the prediction model according to the model parameters.
  • the encoder 80 may further include a first prediction unit 808 configured to perform cross-component prediction on the second image component of the current block according to the prediction model to obtain the second image component of the current block.
  • the predicted value of the two image components may be further included in the encoder 80.
  • a “unit” may be a part of a circuit, a part of a processor, a part of a program, or software, etc., of course, may also be a module, or may also be non-modular.
  • the various components in this embodiment may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be realized in the form of hardware or software function module.
  • the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this embodiment is essentially or It is said that the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can A personal computer, server, or network device, etc.) or a processor (processor) executes all or part of the steps of the method described in this embodiment.
  • the aforementioned storage media include: U disk, mobile hard disk, read only memory (Read Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes.
  • an embodiment of the present application provides a computer storage medium that stores an image prediction program that implements the steps of the method described in the foregoing embodiment when the image prediction program is executed by at least one processor.
  • FIG. 9 shows the specific hardware structure of the encoder 80 provided by the embodiment of the present application, which may include: a first communication interface 901, a first memory 902, and a first communication interface 901; Processor 903; the components are coupled together through the first bus system 904.
  • the first bus system 904 is used to implement connection and communication between these components.
  • the first bus system 904 also includes a power bus, a control bus, and a status signal bus.
  • various buses are marked as the first bus system 904 in FIG. 9. among them,
  • the first communication interface 901 is used for receiving and sending signals in the process of sending and receiving information with other external network elements;
  • the first memory 902 is configured to store a computer program that can run on the first processor 903;
  • the first processor 903 is configured to execute: when the computer program is running:
  • Preprocessing at least one image component of the current block to obtain at least one image component after preprocessing
  • the first memory 902 in the embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), and electrically available Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory.
  • the volatile memory may be a random access memory (Random Access Memory, RAM), which is used as an external cache.
  • RAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • DDRSDRAM Double Data Rate Synchronous Dynamic Random Access Memory
  • Enhanced SDRAM, ESDRAM Synchronous Link Dynamic Random Access Memory
  • Synchlink DRAM Synchronous Link Dynamic Random Access Memory
  • DRRAM Direct Rambus RAM
  • the first processor 903 may be an integrated circuit chip with signal processing capability. In the implementation process, the steps of the foregoing method may be completed by an integrated logic circuit of hardware in the first processor 903 or instructions in the form of software.
  • the aforementioned first processor 903 may be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (ASIC), a ready-made programmable gate array (Field Programmable Gate Array, FPGA) Or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.
  • DSP Digital Signal Processor
  • ASIC application specific integrated circuit
  • FPGA ready-made programmable gate array
  • the methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor.
  • the software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers.
  • the storage medium is located in the first memory 902, and the first processor 903 reads the information in the first memory 902, and completes the steps of the foregoing method in combination with its hardware.
  • the embodiments described in this application can be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof.
  • the processing unit can be implemented in one or more Application Specific Integrated Circuits (ASIC), Digital Signal Processing (DSP), Digital Signal Processing Equipment (DSP Device, DSPD), programmable Logic device (Programmable Logic Device, PLD), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA), general-purpose processors, controllers, microcontrollers, microprocessors, and others for performing the functions described in this application Electronic unit or its combination.
  • ASIC Application Specific Integrated Circuits
  • DSP Digital Signal Processing
  • DSP Device Digital Signal Processing Equipment
  • PLD programmable Logic Device
  • PLD programmable Logic Device
  • Field-Programmable Gate Array Field-Programmable Gate Array
  • FPGA Field-Programmable Gate Array
  • the technology described in this application can be implemented through modules (such as procedures, functions, etc.) that perform the functions described in this application.
  • the first processor 903 is further configured to execute the method described in any one of the foregoing embodiments when the computer program is running.
  • This embodiment provides an encoder, which may include a first determining unit, a first processing unit, and a first constructing unit, wherein the first determining unit is configured to determine at least one image component of the current block in the image;
  • a processing unit is configured to preprocess at least one image component of the current block to obtain at least one image component after preprocessing;
  • the first construction unit is configured to construct a prediction model based on the at least one image component after preprocessing
  • the prediction model is used to perform cross-component prediction processing on at least one image component of the current block; in this way, before at least one image component of the current block is predicted, the at least one image component is preprocessed first, which can balance The statistical characteristics of each image component before cross-component prediction, thereby improving the prediction efficiency, and at the same time improving the coding and decoding efficiency of the video image.
  • FIG. 10 shows a schematic diagram of the composition structure of a decoder 100 provided by an embodiment of the present application.
  • the decoder 100 may include a second determination unit 1001, a second processing unit 1002, and a second construction unit 1003, where:
  • the second determining unit 1001 is configured to determine at least one image component of the current block in the image
  • the second processing unit 1002 is configured to preprocess at least one image component of the current block to obtain at least one image component after preprocessing;
  • the second construction unit 1003 is configured to construct a prediction model according to the at least one image component after the preprocessing; wherein the prediction model is used to perform cross-component prediction processing on at least one image component of the current block.
  • the decoder 100 may further include a second statistics unit 1004 and a second acquisition unit 1005, where:
  • the second statistical unit 1004 is configured to perform characteristic statistics on at least one image component of the current block; wherein the at least one image component includes a first image component and/or a second image component;
  • the second acquiring unit 1005 is configured to acquire the reference value of the first image component of the current block and/or the reference value of the second image component of the current block according to the result of characteristic statistics; wherein, the first image component An image component is a component used for prediction when constructing the prediction model, and the second image component is a component predicted when constructing the prediction model.
  • the second processing unit 1002 is further configured to use a preset processing mode based on the reference value of the first image component of the current block and/or the reference value of the second image component of the current block Perform first processing on the first image component; wherein the preset processing mode includes at least one of the following: filtering processing, grouping processing, value correction processing, quantization processing, and dequantization processing;
  • the second acquiring unit 1005 is further configured to acquire the processed value of the first image component of the current block according to the result of the first processing.
  • the decoder 100 may further include a second adjustment unit 1006 and a second update unit 1007, where:
  • the second adjustment unit 1006 is configured to perform the resolution of the first image component when the resolution of the first image component of the current block is different from the resolution of the second image component of the current block Resolution adjustment; wherein the resolution adjustment includes up-sampling adjustment or down-sampling adjustment;
  • the second update unit 1007 is configured to update the reference value of the first image component of the current block based on the adjusted resolution of the first image component; wherein The resolution is the same as the resolution of the second image component.
  • the second adjustment unit 1006 is further configured to: when the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, Resolution adjustment is performed on the resolution of the image components; wherein the resolution adjustment includes up-sampling adjustment or down-sampling adjustment;
  • the second update unit 1007 is further configured to update the processing value of the first image component of the current block based on the adjusted resolution of the first image component; wherein the adjusted first image component The resolution of is the same as the resolution of the second image component.
  • the second adjustment unit 1006 is further configured to, when the resolution of the first image component of the current block is different from the resolution of the second image component of the current block, based on the current block And/or the reference value of the first image component of the current block and/or the reference value of the second image component of the current block, performing a second processing on the first image component; wherein the second processing includes upsampling and preset processing Mode-related processing, or down-sampling and related processing of preset processing modes;
  • the second obtaining unit 1005 is further configured to obtain the processed value of the first image component of the current block according to the result of the second processing; wherein the processed value of the first image component of the current block is the same as The resolutions of the second image components of the current block are the same.
  • the second construction unit 1003 is configured to analyze the code stream, and construct the prediction model according to the model parameters obtained by the analysis.
  • the decoder 100 may further include a second prediction unit 1008 configured to perform cross-component prediction on the second image component of the current block according to the prediction model to obtain the second image component of the current block.
  • the predicted value of the two image components may be further included in the decoder 100.
  • a "unit" may be a part of a circuit, a part of a processor, a part of a program, or software, etc., of course, may also be a module, or may be non-modular.
  • the various components in this embodiment may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be realized in the form of hardware or software function module.
  • the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer readable storage medium.
  • this embodiment provides a computer storage medium that stores an image prediction program, and when the image prediction program is executed by a second processor, the method described in any one of the preceding embodiments is implemented. .
  • FIG. 11 shows the specific hardware structure of the decoder 100 provided by the embodiment of the present application, which may include: a second communication interface 1101, a second memory 1102, and a second Processor 1103; the components are coupled together through the second bus system 1104.
  • the second bus system 1104 is used to implement connection and communication between these components.
  • the second bus system 1104 also includes a power bus, a control bus, and a status signal bus.
  • various buses are marked as the second bus system 1104 in FIG. 11. among them,
  • the second communication interface 1101 is used for receiving and sending signals in the process of sending and receiving information with other external network elements;
  • the second memory 1102 is configured to store a computer program that can run on the second processor 1103;
  • the second processor 1103 is configured to execute when the computer program is running:
  • Preprocessing at least one image component of the current block to obtain at least one image component after preprocessing
  • the second processor 1103 is further configured to execute the method described in any one of the foregoing embodiments when running the computer program.
  • This embodiment provides a decoder, which may include a second determining unit, a second processing unit, and a second constructing unit, wherein the second determining unit is configured to determine at least one image component of the current block in the image;
  • the second processing unit is configured to preprocess at least one image component of the current block to obtain at least one image component after preprocessing;
  • the second construction unit is configured to construct a prediction model based on the at least one image component after preprocessing
  • the prediction model is used to perform cross-component prediction processing on at least one image component of the current block; in this way, before at least one image component of the current block is predicted, the at least one image component is preprocessed first, which can balance The statistical characteristics of each image component before cross-component prediction, thereby improving the prediction efficiency, and at the same time improving the coding and decoding efficiency of the video image.
  • At least one image component of the current block in the image is first determined; then at least one image component of the current block is preprocessed to obtain at least one image component after preprocessing;
  • Component construct a prediction model, which is used to perform cross-component prediction processing on at least one image component of the current block; in this way, before predicting at least one image component of the current block, first perform the at least one image component Preprocessing can balance the statistical characteristics of each image component before cross-component prediction, thereby improving the prediction efficiency; in addition, because the predicted value of the image component predicted by the prediction model is closer to the true value, the prediction residual of the image component Smaller, so that the bit rate transmitted during the encoding and decoding process is less, and at the same time, the encoding and decoding efficiency of the video image is improved.

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Abstract

本申请实施例公开了一种图像预测方法、编码器、解码器以及存储介质，该方法包括：确定图像中当前块的至少一个图像分量；对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。

Description

图像预测方法、编码器、解码器以及存储介质 技术领域
本申请实施例涉及视频编解码的技术领域，尤其涉及一种图像预测方法、编码器、解码器以及存储介质。
背景技术
在最新的视频编码标准H.266/多功能视频编码(Versatile Video Coding，VVC)中，已经允许跨分量预测的存在；其中，跨分量线性模型预测(Cross-component Linear Model Prediction，CCLM)是典型的跨分量预测技术之一。利用跨分量预测技术，可以实现由一个分量预测另一个分量(或者其残差)，例如由亮度分量预测色度分量、或者由色度分量预测亮度分量、或者由色度分量预测色度分量等。
不同的分量具有不同的统计特性，使得各分量间的统计特性存在差异。然而在进行分量预测时，现有的跨分量预测技术考虑不全面，导致预测效率较低。
发明内容
本申请实施例提供一种图像预测方法、编码器、解码器以及存储介质，通过平衡跨分量预测前各分量的统计特性，从而不仅提高了预测效率，而且还提高了视频图像的编解码效率。
本申请实施例的技术方案可以如下实现：
第一方面，本申请实施例提供了一种图像预测方法，应用于编码器或解码器，该方法包括：
确定图像中当前块的至少一个图像分量；
对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
第二方面，本申请实施例提供了一种图像预测方法，应用于编码器或解码器，该方法包括：
确定图像中当前块的第一图像分量的参考值；其中，所述当前块的第一图像分量的参考值是所述当前块相邻像素的第一图像分量值；
对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值；
利用所述滤波后的参考值计算预测模型的模型参数，其中，所述预测模型用于将所述当前块的第一图像分量的取值映射为所述当前块的第二图像分量的取值，所述第二图像分量不同于所述第一图像分量。
第三方面，本申请实施例提供了一种编码器，该编码器包括第一确定单元、第一处理单元和第一构建单元，其中，
第一确定单元，配置为确定图像中当前块的至少一个图像分量；
第一处理单元，配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
第一构建单元，配置为根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
第四方面，本申请实施例提供了一种编码器，该编码器包括第一存储器和第一处理器，其中，
第一存储器，用于存储能够在所述第一处理器上运行的计算机程序；
第一处理器，用于在运行所述计算机程序时，执行如第一方面或第二方面所述的方法。
第五方面，本申请实施例提供了一种解码器，该解码器包括第二确定单元、第二处理单元和第二构建单元，其中，
第二确定单元，配置为确定图像中当前块的至少一个图像分量；
第二处理单元，配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
第二构建单元，配置为根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
第六方面，本申请实施例提供了一种解码器，该解码器包括第二存储器和第二处理器，其中，
第二存储器，用于存储能够在所述第二处理器上运行的计算机程序；
第二处理器，用于在运行所述计算机程序时，执行如第一方面或第二方面所述的方法。
第七方面，本申请实施例提供了一种计算机存储介质，该计算机存储介质存储有图像预测程序，所述图像预测程序被第一处理器或第二处理器执行时实现如第一方面或第二方面所述的方法。
本申请实施例提供了一种图像预测方法、编码器、解码器以及存储介质，确定图像中当前块的至少一个图像分量；对当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；根据预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，从而提高了预测效率；另外，由于利用该预测模型预测得到的图像分量的预测值更接近于真实值，使得图像分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。
附图说明
图1为本申请实施例提供的一种传统跨分量预测架构的组成结构示意图；
图2为本申请实施例提供的一种视频编码***的组成框图示意图；
图3为本申请实施例提供的一种视频解码***的组成框图示意图；
图4为本申请实施例提供的一种图像预测方法的流程示意图；
图5为本申请实施例提供的另一种图像预测方法的流程示意图；
图6为本申请实施例提供的一种改进型跨分量预测架构的组成结构示意图；
图7为本申请实施例提供的另一种改进型跨分量预测架构的组成结构示意图；
图8为本申请实施例提供的一种编码器的组成结构示意图；
图9为本申请实施例提供的一种编码器的具体硬件结构示意图；
图10为本申请实施例提供的一种解码器的组成结构示意图；
图11为本申请实施例提供的一种解码器的具体硬件结构示意图。
具体实施方式
为了能够更加详尽地了解本申请实施例的特点与技术内容，下面结合附图对本申请实施例的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本申请实施例。
在视频图像中，一般采用第一图像分量、第二图像分量和第三图像分量来表征编码块； 其中，这三个图像分量分别为一个亮度分量、一个蓝色色度分量和一个红色色度分量，具体地，亮度分量通常使用符号Y表示，蓝色色度分量通常使用符号Cb或者U表示，红色色度分量通常使用符号Cr或者V表示；这样，视频图像可以用YCbCr格式表示，也可以用YUV格式表示。
在本申请实施例中，第一图像分量可以为亮度分量，第二图像分量可以为蓝色色度分量，第三图像分量可以为红色色度分量，但是本申请实施例不作具体限定。
为了进一步提升编解码性能，H.266/VCC提出了CCLM的跨分量预测技术。其中，基于CCLM的跨分量预测技术，不仅可以实现亮度分量到色度分量的预测，即第一图像分量到第二图像分量、或者第一图像分量到第三图像分量的预测，还可以实现色度分量到亮度分量的预测，即第二图像分量到第一图像分量、或者第三图像分量到第一图像分量的预测，甚至也可以实现色度分量与色度分量之间的预测，即第二图像分量到第三图像分量、或者第三图像分量到第二图像分量的预测等。在本申请实施例中，下述将以第一图像分量到第二图像分量的预测为例进行描述，但是本申请实施例的技术方案同样也可以适用于其他图像分量的预测。
参见图1，其示出了本申请实施例提供的一种传统跨分量预测架构的组成结构示意图。如图1所示，利用第一图像分量(例如用Y分量表示)预测第二图像分量(例如用U分量表示)；假定视频图像采用YUV为4:2:0格式，则Y分量与U分量具有不同的分辨率，此时需要对Y分量进行下采样处理或者对U分量进行上采样处理，以达到待预测分量的目标分辨率，这样就可以在分量之间以相同的分辨率进行预测。本示例中，使用Y分量预测第三图像分量(例如用V分量表示)的方法与此相同。
在图1中，传统跨分量预测架构10可以包括Y分量编码块110、分辨率调整单元120、Y 1分量编码块130、U分量编码块140、预测模型150、跨分量预测单元160。其中，视频图像的Y分量用2N×2N大小的Y分量编码块110表示，这里加粗的较大方框用于突出指示Y分量编码块110，而周围的灰色实心圆圈用于指示Y分量编码块110的相邻参考值Y(n)；视频图像的U分量用N×N大小的U分量编码块140表示，这里加粗的较大方框用于突出指示U分量编码块140，而周围的灰色实心圆圈用于指示U分量编码块140的相邻参考值C(n)；由于Y分量与U分量具有不同的分辨率，需要经过分辨率调整单元120对Y分量进行分辨率调整，得到N×N大小的Y 1分量编码块130；对于Y 1分量编码块130，这里加粗的较大方框用于突出指示Y 1分量编码块130，而周围的灰色实心圆圈用于指示Y 1分量编码块130的相邻参考值Y 1(n)；然后通过Y 1分量编码块130的相邻参考值Y 1(n)和U分量编码块140的相邻参考值C(n)可以构建出预测模型150；根据Y 1分量编码块130的Y分量重建像素值和预测模型150，可以跨分量预测单元160进行分量预测，最终输出U分量预测值。
针对传统跨分量预测架构10，在进行图像分量预测时考虑不全面，比如没有考虑到各图像分量间统计特性的差异性，使得预测效率较低。为了提高预测效率，本申请实施例提供了一种图像预测方法，首先确定图像中当前块的至少一个图像分量；然后对当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；再根据预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，从而不仅提高了预测效率，而且还提高了视频图像的编解码效率。
下面将结合附图对本申请各实施例进行详细描述。
参见图2，其示出了本申请实施例提供的一种视频编码***的组成框图示例；如图2所示，该视频编码***20包括变换与量化单元201、帧内估计单元202、帧内预测单元203、运动补偿单元204、运动估计单元205、反变换与反量化单元206、滤波器控制分析单元 207、滤波单元208、编码单元209和解码图像缓存单元210等，其中，滤波单元208可以实现去方块滤波及样本自适应缩进(Sample Adaptive 0ffset，SAO)滤波，编码单元209可以实现头信息编码及基于上下文的自适应二进制算术编码(Context-based Adaptive Binary Arithmatic Coding，CABAC)。针对输入的原始视频信号，通过编码树块(Coding Tree Unit，CTU)的划分可以得到一个编码块，然后对经过帧内或帧间预测后得到的残差像素信息通过变换与量化单元201对该编码块进行变换，包括将残差信息从像素域变换到变换域，并对所得的变换系数进行量化，用以进一步减少比特率；帧内估计单元202和帧内预测单元203是用于对该编码块进行帧内预测；明确地说，帧内估计单元202和帧内预测单元203用于确定待用以编码该编码块的帧内预测模式；运动补偿单元204和运动估计单元205用于执行所接收的该编码块相对于一或多个参考帧中的一或多个块的帧间预测编码以提供时间预测信息；由运动估计单元205执行的运动估计为产生运动向量的过程，所述运动向量可以估计该编码块的运动，然后由运动补偿单元204基于由运动估计单元205所确定的运动向量执行运动补偿；在确定帧内预测模式之后，帧内预测单元203还用于将所选择的帧内预测数据提供到编码单元209，而且运动估计单元205将所计算确定的运动向量数据也发送到编码单元209；此外，反变换与反量化单元206是用于该编码块的重构建，在像素域中重构建残差块，该重构建残差块通过滤波器控制分析单元207和滤波单元208去除方块效应伪影，然后将该重构残差块添加到解码图像缓存单元210的帧中的一个预测性块，用以产生经重构建的视频块；编码单元209是用于编码各种编码参数及量化后的变换系数，在基于CABAC的编码算法中，上下文内容可基于相邻编码块，可用于编码指示所确定的帧内预测模式的信息，输出该视频信号的码流；而解码图像缓存单元210是用于存放重构建的视频块，用于预测参考。随着视频图像编码的进行，会不断生成新的重构建的视频块，这些重构建的视频块都会被存放在解码图像缓存单元210中。
参见图3，其示出了本申请实施例提供的一种视频解码***的组成框图示例；如图3所示，该视频解码***30包括解码单元301、反变换与反量化单元302、帧内预测单元303、运动补偿单元304、滤波单元305和解码图像缓存单元306等，其中，解码单元301可以实现头信息解码以及CABAC解码，滤波单元305可以实现去方块滤波以及SAO滤波。输入的视频信号经过图2的编码处理之后，输出该视频信号的码流；该码流输入视频解码***30中，首先经过解码单元301，用于得到解码后的变换系数；针对该变换系数通过反变换与反量化单元302进行处理，以便在像素域中产生残差块；帧内预测单元303可用于基于所确定的帧内预测模式和来自当前帧或图片的先前经解码块的数据而产生当前待解码的视频块的预测数据；运动补偿单元304是通过剖析运动向量和其他关联语法元素来确定用于该待解码的视频块的预测信息，并使用该预测信息以产生正被解码的视频块的预测性块；通过对来自反变换与反量化单元302的残差块与由帧内预测单元303或运动补偿单元304产生的对应预测性块进行求和，而形成经解码的视频块；经解码的视频块通过滤波单元305以便去除方块效应伪影，可以改善视频质量；然后将经解码的视频块存储于解码图像缓存单元306中，解码图像缓存单元306存储用于后续帧内预测或运动补偿的参考图像，同时也用于视频信号的输出，即得到了所恢复的原始视频信号。
本申请实施例主要应用在如图2所示的帧内预测单元203部分和如图3所示的帧内预测单元303部分；也就是说，本申请实施例既可以应用于视频编码***，也可以应用于视频解码***，本申请实施例不作具体限定。
基于上述图2或者图3的应用场景示例，参见图4，其示出了本申请实施例提供的一种图像预测方法的流程示意图，该方法可以包括：
S401：确定图像中当前块的至少一个图像分量；
S402：对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
S403：根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
需要说明的是，视频图像可以划分为多个图像块，每个当前待编码的图像块可以称为编码块。其中，每个编码块可以包括第一图像分量、第二图像分量和第三图像分量；而当前块为视频图像中当前待进行第一图像分量、第二图像分量或者第三图像分量预测的编码块。
还需要说明的是，本申请实施例的图像预测方法，既可以应用于视频编码***，又可以应用于视频解码***，甚至还可以同时应用于视频编码***和视频解码***，本申请实施例不作具体限定。
本申请实施例中，首先确定图像中当前块的至少一个图像分量；然后对当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；再根据预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，从而不仅提高了预测效率，而且还提高了视频图像的编解码效率。
进一步地，由于不同的图像分量具有不同的统计特性，而且各个图像分量间的统计特性存在差异，比如亮度分量具有丰富的纹理特性，而色度分量则更趋于均匀平坦；本申请实施例可以考虑图像分量间的统计特性的差异，以达到平衡各图像分量的统计特性的目的。因此，在一些实施例中，在所述确定图像中当前块的至少一个图像分量之后，该方法还可以包括：
对所述当前块的至少一个图像分量进行特性统计；其中，所述至少一个图像分量包括第一图像分量和/或第二图像分量；
根据特性统计的结果，获取所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值；其中，所述第一图像分量为构建所述预测模型时所用于预测的分量，所述第二图像分量为构建所述预测模型时所被预测的分量。
需要说明的是，当前块的至少一个图像分量可以是第一图像分量，也可以是第二图像分量，甚至还可以是第一图像分量和第二图像分量。其中，第一图像分量为构建预测模型时所用于预测的分量，也可以称为待参考图像分量；第二图像分量为构建预测模型时所被预测的分量，也可以称为待预测图像分量。
假定通过预测模型来实现亮度分量对色度分量的预测，那么构建预测模型时所用于预测的分量为亮度分量，构建预测模型时所被预测的分量为色度分量，即第一图像分量为亮度分量，第二图像分量为色度分量；或者，假定通过预测模型来实现色度分量对亮度分量的预测，那么构建预测模型时所用于预测的分量为色度分量，构建预测模型时所被预测的分量为亮度分量，即第一图像分量为色度分量，第二图像分量为亮度分量。
这样，通过对当前块的至少一个图像分量进行特性统计，根据特性统计的结果，可以得到当前块的第一图像分量的参考值和/或当前块的第二图像分量的参考值。
进一步地，为了提高预测效率，可以考虑各个图像分量间的统计特性差异。也就是说，在通过预测模型对至少一个图像分量进行跨分量预测之前，还可以根据图像分量的统计特性对该至少一个图像分量进行预处理，比如过滤处理、分组处理、值修正处理、量化处理或者去量化处理等。因此，在一些实施例中，对于S402来说，所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量，可以包括：
基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，利用预设处理模式对所述第一图像分量进行第一处理；其中，所述预设处理模式至少包括下述其中之一：过滤处理、分组处理、值修正处理、量化处理和去量化处理；
根据第一处理的结果，获得所述当前块的第一图像分量的处理值。
需要说明的是，根据当前块的至少一个图像分量的特性统计的结果，在获得当前块的第一图像分量的参考值和/或当前块的第二图像分量的参考值之后，可以利用预设处理模式对第一图像分量进行第一处理。具体来说，可以利用过滤处理对第一图像分量行第一处理，或者，也可以利用分组处理对第一图像分量进行第一处理，或者，还可以利用值修正处理对第一图像分量进行第一处理，或者，还可以利用量化处理对第一图像分量进行第一处理，或者，还可以利用反量化处理(也可以称为去量化处理)对第一图像分量进行第一处理等等，本申请实施例不作具体限定。
还需要说明的是，针对第一图像分量进行处理，可以是针对第一图像分量的相邻参考像素值进行处理，也可以是针对第一图像分量的重建像素值进行处理，甚至也可以是针对第一图像分量的其他像素值进行处理；在本申请实施例中，根据预测模型的实际情况进行设定，本申请实施例不作具体限定。
示例性地，假定预测模型为利用亮度分量预测色度分量，为了提高预测效率，即提高预测值的准确性，此时需要根据预设处理模式对亮度分量和/或色度分量进行处理，比如根据预设处理模式对亮度分量对应的重建像素值进行处理。如果预设处理模式采用值修正处理，由于亮度分量与色度分量具有不同的统计特性，根据两个图像分量的统计特性的差异，那么可以得到一个偏差因子；然后利用该偏差因子对亮度分量进行值修正处理(比如将亮度分量对应的重建像素值与该偏差因子进行相加处理)以平衡跨分量预测前各图像分量间的统计特性，从而所获取到处理后的亮度分量，这时候根据预测模型所预测得到的色度分量的预测值更接近于色度分量的真实值；如果预设处理模式采用过滤处理，由于亮度分量与色度分量具有不同的统计特性，根据两个图像分量的统计特性的差异，那么可以对亮度分量进行过滤处理以平衡跨分量预测前各图像分量间的统计特性，从而所获取到处理后的亮度分量，这时候根据预测模型所预测得到的色度分量的预测值更接近于色度分量的真实值；如果预设处理模式采用分组处理，由于亮度分量与色度分量具有不同的统计特性，根据两个图像分量的统计特性的差异，那么可以对亮度分量进行分组处理以平衡跨分量预测前各图像分量间的统计特性，根据分组处理后的亮度分量所构建的预测模型，该预测模型所预测得到的色度分量的预测值更接近于色度分量的真实值；除此之外，由于在利用预测模型进行色度分量预测的过程中涉及到量化处理和反量化处理，同时由于亮度分量与色度分量具有不同的统计特性，根据两个图像分量的统计特性的差异可能会导致量化处理和反量化处理存在差异性，这时候如果预设处理模式采用量化处理，那么可以对亮度分量和/或色度分量进行量化处理以平衡跨分量预测前各图像分量间的统计特性，从而所获取到处理后的亮度分量和/或处理后的色度分量，这时候根据预测模型所预测得到的色度分量的预测值更接近于色度分量的真实值；如果预设处理模式采用去量化处理，那么可以对亮度分量和/或色度分量进行去量化处理以平衡跨分量预测前各图像分量间的统计特性，从而所获取到处理后的亮度分量和/或处理后的色度分量，这时候根据预测模型所预测得到的色度分量的预测值更接近于色度分量的真实值；从而提高了预测值的准确性，也就提高了预测效率；由于预测得到的色度分量的预测值更接近于真实值，使得色度分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。
这样，在获取到当前块的第一图像分量的参考值和/或当前块的第二图像分量的参考值之后，可以基于当前块的第一图像分量的参考值，利用预设处理模式对第一图像分量进行处理以平衡跨分量预测前各图像分量间的统计特性，然后得到当前块的第一图像分量的处理值；也可以基于当前块的第二图像分量的参考值，利用预设处理模式对第一图像分量进行处理以平衡跨分量预测前各图像分量间的统计特性，然后得到当前块的第一图像分量的处理值；甚至还可以基于当前块的第一图像分量的参考值和当前块的第二图像分量的参考值，利用预设处理模式对第一图像分量进行处理以平衡跨分量预测前各图像分量间的统计特性，然后得到当前块的第一图像分量的处理值；根据当前块的第一图像分量的处理值， 利用预测模型所预测得到的第二图像分量的预测值更接近于真实值；其中，该预测模型可以实现通过第一图像分量对第二图像分量的跨分量预测。
进一步地，各图像分量的分辨率并不是相同的，为了方便构建预测模型，还需要对图像分量的分辨率进行调整(包括对图像分量进行上采样或者对图像分量进行下采样)，以达到目标分辨率。具体来说，利用预设处理模式对第一图像分量进行第一处理和分辨率调整可以级联处理，利用预设处理模式对第一图像分量进行第一处理和分辨率调整也可以联合处理，下面将对其分别进行描述。
可选地，在一些实施例中，在所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量之前，该方法还可以包括：
当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的参考值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
需要说明的是，分辨率调整，即分辨率映射，将第一图像分量的分辨率映射为调整后的第一图像分量的分辨率；这里，具体可以通过上采样调整或下采样调整来实现分辨率调整或分辨率映射。
还需要说明的是，当利用预设处理模式对第一图像分量进行第一处理和分辨率调整可以级联处理时，分辨率调整可以在利用预设处理模式对第一图像分量进行第一处理之前。也就是说，对当前块的至少一个图像分量进行预处理之前，如果当前块的第一图像分量的分辨率与当前块的第二图像分量的分辨率不同，那么可以对第一图像分量的分辨率进行分辨率调整，并且基于调整后的所述第一图像分量的分辨率，更新当前块的第一图像分量的参考值。
可选地，在一些实施例中，在所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量之后，该方法还可以包括：
当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的处理值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
还需要说明的是，当利用预设处理模式对第一图像分量进行第一处理和分辨率调整可以级联处理时，分辨率调整也可以在利用预设处理模式对第一图像分量进行第一处理之后。也就是说，对当前块的至少一个图像分量进行预处理之后，如果当前块的第一图像分量的分辨率与当前块的第二图像分量的分辨率不同，那么可以对第一图像分量的分辨率进行分辨率调整，并且基于调整后的所述第一图像分量的分辨率，更新当前块的第一图像分量的处理值。
可选地，在一些实施例中，所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量，可以包括：
当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，对所述第一图像分量进行第二处理；其中，所述第二处理包括上采样及预设处理模式的相关处理、或者下采样及预设处理模式的相关处理；
根据第二处理的结果，获得所述当前块的第一图像分量的处理值；其中，处理后的所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率相同。
还需要说明的是，当利用预设处理模式对第一图像分量进行第一处理和分辨率调整可 以联合处理时，对于当前块的第一图像分量的处理值可以是同时经过第一处理和分辨率调整之后得到的。也就是说，如果当前块的第一图像分量的分辨率与当前块的第二图像分量的分辨率不同，那么可以根据当前块的第一图像分量的参考值和/或当前块的第二图像分量的参考值，对第一图像分量进行第二处理，该第二处理集成了第一处理和分辨率调整两种处理方式，该第二处理可以包括有上采样及预设处理模式的相关处理、或者下采样及预设处理模式的相关处理等等；这样，根据第二处理的结果，能够获得当前块的第一图像分量的处理值，而且处理后的当前块的第一图像分量的分辨率与当前块的第二图像分量的分辨率相同。
示例性地，仍然假定预测模型为利用亮度分量预测色度分量，这时候待预测图像分量为色度分量，待使用图像分量为亮度分量；由于亮度分量和色度分量的分辨率是不同的，在获取到色度分量的目标分辨率之后，由于亮度分量的分辨率不符合目标分辨率，这时候需要对亮度分量的分辨率进行调整，比如对亮度分量进行下采样处理，可以使得调整后的亮度分量的分辨率符合目标分辨率；反之，如果利用色度分量预测亮度分量，在获取到亮度分量的目标分辨率之后，由于色度分量的分辨率不符合目标分辨率，这时候需要对色度分量的分辨率进行调整，比如对色度分量进行上采样处理，可以使得调整后的色度分量的分辨率符合目标分辨率；另外，如果利用蓝色色度分量预测红色色度分量，在获取到红色色度分量的目标分辨率之后，由于蓝色色度分量的分辨率符合目标分辨率，这时候不需要对蓝色色度分量的分辨率进行调整，已经保证蓝色色度分量的分辨率符合目标分辨率；这样，后续可以按照相同分辨率进行图像分量的预测。
进一步地，在得到预处理后的至少一个图像分量之后，还需要根据预处理后的至少一个图像分量来确定预测模型的模型参数，以构建预测模型。因此，在一些实施例中，对于S403来说，所述根据所述预处理后的至少一个图像分量，构建预测模型，可以包括：
根据所述第一图像分量的处理值和所述第二图像分量的参考值，确定所述预测模型的模型参数；
根据所述模型参数，构建所述预测模型。
需要说明的是，本申请实施例中的预测模型可以是线性模型，比如CCLM的跨分量预测技术；该预测模型也可以是非线性模型，比如多模型CCLM(Multiple Model CCLM，MMLM)的跨分量预测技术，它是由多个线性模型构成的。本申请实施例将以预测模型为线性模型为例进行如下描述，但是本申请实施例的图像预测方法同样可以适用于非线性模型。
具体地，模型参数包括第一模型参数(用α表示)和第二模型参数(用β表示)。而针对α和β的计算具有多种方式，可以是以最小二乘法构造的预设因子计算模型，也可以是以最大值与最小值构造的预设因子计算模型，甚至还可以是其他方式构造的预设因子计算模型，本申请实施例不作具体限定。
以最小二乘法构造的预设因子计算模型为例，可以通过当前块周围的相邻参考像素值(比如第一图像分量相邻参考值和第二图像分量相邻参考值，这里的第一图像分量相邻参考值和第二图像分量相邻参考值是经过预处理后得到的)的最小化回归误差进行推导得到，具体地，如式(1)所示：
Figure PCTCN2019110809-appb-000001
其中，L(n)表示经过下采样的当前块左侧边和上侧边所对应的第一图像分量相邻参考值，C(n)表示当前块左侧边和上侧边所对应的第二图像分量相邻参考值，N为第二图像分 量当前块的边长，n＝1,2,...,2N。通过式(1)的计算，可以得到第一模型参数α和第二模型参数β。
以最大值与最小值构造的预设因子计算模型为例，它提供了一种简化版模型参数的推导方法，具体地，可以通过搜索最大的第一图像分量相邻参考值和最小的第一图像分量相邻参考值，根据“两点确定一线”原则来推导出模型参数，如式(2)所示的预设因子计算模型：
Figure PCTCN2019110809-appb-000002
其中，L max和L min表示经过下采样的当前块左侧边和上侧边所对应的第一图像分量相邻参考值中搜索得到的最大值和最小值，C max和C min表示L max和L min对应位置的参考像素点所对应的第二图像分量相邻参考值。根据L max和L min以及C max和C min，通过式(2)的计算，也可以得到第一模型参数α和第二模型参数β。
在得到第一模型参数α和第二模型参数β之后，可以构建预测模型。具体地，基于α和β，假设根据第一图像分量预测第二图像分量，那么所构建的预测模型如式(3)所示，
Pred C[i,j]＝α·Rec L[i,j]+β                      (3)
其中，i，j表示当前块中像素点的位置坐标，i表示水平方向，j表示竖直方向，Pred C[i,j]表示当前块中位置坐标为[i,j]的像素点对应的第二图像分量预测值，Rec L[i,j]表示同一当前块中(经过下采样的)位置坐标为[i,j]的像素点对应的第一图像分量重建值。
进一步地，在一些实施例中，对于S403来说，在所述构建预测模型之后，该方法还可以包括：
根据所述预测模型对所述当前块的第二图像分量进行跨分量预测，得到所述当前块的第二图像分量的预测值。
需要说明的是，根据式(3)所示的预测模型，可以利用亮度分量对色度分量进行预测处理，从而可以得到色度分量的预测值。
具体地，针对当前块，在构建出预测模型之后，可以根据该预测模型进行图像分量的预测；一方面，可以利用第一图像分量预测第二图像分量，比如利用亮度分量预测色度分量，得到了色度分量的预测值；另一方面，还可以利用第二图像分量预测第一图像分量，比如利用色度分量预测亮度分量，得到了亮度分量的预测值；再一方面，也可以利用第二图像分量预测第三图像分量，比如利用蓝色色度分量预测红色色度分量，得到了红色色度分量的预测值；由于预测模型构建之前，本申请实施例会针对当前块的至少一个图像分量进行预处理以平衡跨分量预测前各图像分量间的统计特性，然后利用处理后的图像分量来构建预测模型，从而可以达到提高预测效率的目的。
本实施例提供了一种图像预测方法，确定图像中当前块的至少一个图像分量；对当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；根据预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，提高了预测效率；另外，由于利用该预测模型预测得到的图像分量的预测值更接近于真实值，使得图像分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。
基于上述图2或者图3的应用场景示例，参见图5，其示出了本申请实施例提供的另一种图像预测方法的流程示意图，该方法可以包括：
S501：确定图像中当前块的第一图像分量的参考值；其中，所述当前块的第一图像分量的参考值是所述当前块相邻像素的第一图像分量值；
S502：对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值；
S503：利用所述滤波后的参考值计算预测模型的模型参数，其中，所述预测模型用于将所述当前块的第一图像分量的取值映射为所述当前块的第二图像分量的取值，所述第二图像分量不同于所述第一图像分量。
需要说明的是，视频图像可以划分为多个图像块，每个当前待编码的图像块可以称为编码块。其中，每个编码块可以包括第一图像分量、第二图像分量和第三图像分量；而当前块为视频图像中当前待进行第一图像分量、第二图像分量或者第三图像分量预测的编码块。
还需要说明的是，该图像预测方法既可以应用于视频编码***，又可以应用于视频解码***，甚至还可以同时应用于视频编码***和视频解码***，本申请实施例不作具体限定。
本申请实施例中，首先确定图像中当前块的第一图像分量的参考值，当前块的第一图像分量的参考值是当前块相邻像素的第一图像分量值；然后对当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值；再利用滤波后的参考值计算预测模型的模型参数，其中，预测模型用于将当前块的第一图像分量的取值映射为当前块的第二图像分量的取值，第二图像分量不同于第一图像分量；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行滤波处理，可以平衡跨分量预测前各图像分量的统计特性，从而不仅提高了预测效率，而且还提高了视频图像的编解码效率。
进一步地，在一些实施例中，对于S503来说，所述利用所述滤波后的参考值计算分量预测模型的模型参数，可以包括：
对所述图像的至少一个图像分量或所述当前块的至少一个图像分量进行特性统计，其中，所述至少一个图像分量包含所述第一图像分量和/所述第二图像分量；
根据特性统计的结果，获取所述当前块的第二图像分量的参考值；其中，所述当前块的第二图像分量的参考值是所述当前块相邻像素的第二图像分量值；
利用所述滤波后的参考值和所述当前块的第二图像分量的参考值，计算所述预测模型的模型参数。
需要说明的是，由于不同的图像分量具有不同的统计特性，而且各个图像分量间的统计特性存在差异，比如亮度分量具有丰富的纹理特性，而色度分量则更趋于均匀平坦；本申请实施例考虑了图像分量间统计特性的差异，从而可以达到平衡各图像分量的统计特性的目的。
还需要说明的是，在考虑图像分量间统计特性的差异之后，得到当前块的第二图像分量的参考值，然后根据滤波后的参考值和当前块的第二图像分量的参考值来计算预测模型的模型参数，根据计算得到的模型参数构建预测模型，该预测模型预测得到的图像分量的预测值更接近于真实值，使得图像分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。
进一步地，在一些实施例中，对于S502来说，所述对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值，可以包括：
当所述图像的第二图像分量的分辨率与所述图像的第一图像分量的分辨率不同时，对所述当前块的第一图像分量的参考值进行第一调整处理，更新所述当前块的第一图像分量的参考值，其中，所述第一调整处理包括以下之一：下采样滤波，上采样滤波；
对所述当前块的第一图像分量的参考值进行所述滤波处理，得到所述滤波后的参考值。
进一步地，该方法还可以包括：
根据所述当前块的第一图像分量的参考值，利用预设处理模式对所述参考值进行滤波 处理；其中，所述预设处理模式至少包括下述其中之一：过滤处理、分组处理、值修正处理、量化处理、去量化处理、低通滤波和自适应滤波。
进一步地，在一些实施例中，对于S502来说，所述对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值，可以包括：
当所述图像的第二图像分量的分辨率与所述图像的第一图像分量的分辨率不同时，对所述当前块的第二图像分量的参考值进行第二调整处理，更新所述当前块的第一图像分量的第一参考值，其中，所述第二调整处理包括：下采样和平滑滤波，或者，上采样和平滑滤波。
需要说明的是，各图像分量的分辨率并不是相同的，为了方便构建预测模型，还需要对图像分量的分辨率进行调整(包括对图像分量进行上采样或者对图像分量进行下采样)，以达到目标分辨率。具体来说，分辨率调整，即分辨率映射，将第一图像分量的分辨率映射为调整后的第一图像分量的分辨率；这里，具体可以通过上采样调整或下采样调整来实现分辨率调整或分辨率映射。
还需要说明的是，对第一图像分量进行滤波处理和分辨率调整可以级联处理，比如在对第一图像分量进行滤波处理之前进行分辨率调整，或者在对第一图像分量进行滤波处理之后进行分辨率调整；除此之外，也可以将对第一图像分量进行滤波处理和分辨率调整进行联合处理(即第一调整处理)。
进一步地，在一些实施例中，对于S503来说，所述利用所述滤波后的参考值计算分量预测模型的模型参数，可以包括：
确定所述当前块的第二图像分量的参考值；其中，所述当前块的第二图像分量的参考值是所述当前块相邻像素的第二图像分量值；
利用所述滤波后的参考值和所述当前块的第二图像分量的参考值计算所述分量预测模型的模型参数。
进一步地，在一些实施例中，在S503之后，该方法还可以包括：
根据所述预测模型，对所述当前块的第一图像分量的值进行映射，得到所述当前块的第二图像分量的预测值。
需要说明的是，当前块的第二图像分量的参考值可以是当前块相邻像素的第二图像分量值；这样，在确定出第二图像分量的参考值之后，根据滤波后的参考值和所确定的第二图像分量的参考值来计算预测模型的模型参数，根据计算得到的模型参数构建预测模型，该预测模型预测得到的图像分量的预测值更接近于真实值，使得图像分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。
示例性地，参见图6，其示出了本申请实施例提供的一种改进型跨分量预测架构的组成结构示意图。如图6所示，在图1所示的传统跨分量预测架构10的基础上，改进型跨分量预测架构60还可以包括处理单元610，该处理单元610主要用于在跨分量预测单元160之前对至少一个图像分量进行相关处理。其中，处理单元610可以位于分辨率调整单元120之前，也可以位于分辨率调整单元120之后；比如在图6中，处理单元610位于分辨率调整单元120之后，通过对Y分量进行相关处理，比如过滤处理、分组处理、值修正处理、量化处理和反量化处理等，这样可以构建出更准确的预测模型，使得预测得到的U分量预测值更接近于真实值。
基于图6所示的改进型跨分量预测架构60，假定以Y分量来预测U分量，由于Y分量当前块110与U分量当前块140具有不同的分辨率，此时需要通过分辨率调整单元120对Y分量进行分辨率调整，从而得到与U分量当前块140具有相同分辨率的Y 1分量当前块130；在此之前，还可以通过处理单元610对Y分量进行相关处理，从而得到Y 1分量当前块130；然后利用Y 1分量当前块130的相邻参考值Y 1(n)和U分量当前块140的相邻参考值C(n)，可以构建出预测模型150；根据Y 1分量当前块130的Y分量重建像素值和预 测模型150，通过跨分量预测单元160进行图像分量预测，得到U分量预测值；由于在跨分量预测之前对Y分量进行了相关处理，根据处理后的亮度分量构建出的预测模型150，利用该预测模型150所预测得到的U分量预测值更接近于真实值，从而提高了预测效率，同时还提高了视频图像的编解码效率。
在本申请实施例中，分辨率调整单元120和处理单元610既可以对图像分量进行级联处理(比如先通过分辨率调整单元120进行分辨率调整，然后通过处理单元610进行相关处理；或者先通过处理单元610进行相关处理，然后通过分辨率调整单元120进行分辨率调整)，又可以对图像分量进行联合处理(比如将分辨率调整单元120和处理单元610结合之后进行处理)。如图7所示，其示出了本申请实施例提供的另一种改进型跨分量预测架构的组成结构示意图。在图6所示的改进型跨分量预测架构60的基础上，图7所示的改进型跨分量预测架构还可以包括联合单元710，但是可以省略分辨率调整单元120和处理单元610；也就是说，联合单元710包括了分辨率调整单元120和处理单元510的功能，不仅可以实现对至少一个图像分量的分辨率调整，而且还可以实现对至少一个图像分量的相关处理，比如过滤处理、分组处理、值修正处理、量化处理和反量化处理等，这样也可以构建出更准确的预测模型150，利用该预测模型150所预测得到的U分量预测值更接近于真实值，从而提高了预测效率，同时还提高了视频图像的编解码效率。
另外，在本申请实施例中，当该图像预测方法应用于编码器侧时，可以根据当前块的待预测图像分量的参考值和当前块的待参考图像分量的参考值来计算得到预测模型的模型参数，然后将计算得到的模型参数写入码流中；该码流由编码器侧传输到解码器侧；对应地，当该图像预测方法应用于解码器侧时，可以通过解析码流来获得预测模型的模型参数，从而构建出预测模型，利用该预测模型对当前块的至少一个图像分量进行跨分量预测处理。
本实施例提供了一种图像预测方法，确定图像中当前块的第一图像分量的参考值，当前块的第一图像分量的参考值是所述当前块相邻像素的第一图像分量值；对当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值；利用滤波后的参考值计算预测模型的模型参数，该预测模型用于将当前块的第一图像分量的取值映射为当前块的第二图像分量的取值，所述第二图像分量不同于所述第一图像分量；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，提高了预测效率；另外，由于利用该预测模型预测得到的图像分量的预测值更接近于真实值，使得图像分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。
基于前述实施例相同的发明构思，参见图8，其示出了本申请实施例提供的一种编码器80的组成结构示意图。该编码器80可以包括：第一确定单元801、第一处理单元802和第一构建单元803，其中，
所述第一确定单元801，配置为确定图像中当前块的至少一个图像分量；
所述第一处理单元802，配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
所述第一构建单元803，配置为根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
在上述方案中，参见图8，编码器80还可以包括第一统计单元804和第一获取单元805，其中，
所述第一统计单元804，配置为对所述当前块的至少一个图像分量进行特性统计；其中，所述至少一个图像分量包括第一图像分量和/或第二图像分量；
所述第一获取单元805，配置为根据特性统计的结果，获取所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值；其中，所述第一图像分量为构建所 述预测模型时所用于预测的分量，所述第二图像分量为构建所述预测模型时所被预测的分量。
在上述方案中，所述第一处理单元802，还配置为基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，利用预设处理模式对所述第一图像分量进行第一处理；其中，所述预设处理模式至少包括下述其中之一：过滤处理、分组处理、值修正处理、量化处理和去量化处理；
所述第一获取单元805，还配置为根据第一处理的结果，获得所述当前块的第一图像分量的处理值。
在上述方案中，参见图8，编码器80还可以包括第一调整单元806和第一更新单元807，其中，
所述第一调整单元806，配置为当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
所述第一更新单元807，配置为基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的参考值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
在上述方案中，所述第一调整单元806，还配置为当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
所述第一更新单元807，还配置为基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的处理值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
在上述方案中，所述第一调整单元806，还配置为当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，对所述第一图像分量进行第二处理；其中，所述第二处理包括上采样及预设处理模式的相关处理、或者下采样及预设处理模式的相关处理；
所述第一获取单元805，还配置为根据第二处理的结果，获得所述当前块的第一图像分量的处理值；其中，处理后的所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率相同。
在上述方案中，所述第一确定单元801，还配置为根据所述第一图像分量的处理值和所述第二图像分量的参考值，确定所述预测模型的模型参数；
所述第一构建单元803，配置为根据所述模型参数，构建所述预测模型。
在上述方案中，参见图8，编码器80还可以包括第一预测单元808，配置为根据所述预测模型对所述当前块的第二图像分量进行跨分量预测，得到所述当前块的第二图像分量的预测值。
可以理解地，在本申请实施例中，“单元”可以是部分电路、部分处理器、部分程序或软件等等，当然也可以是模块，还可以是非模块化的。而且在本实施例中的各组成部分可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
所述集成的单元如果以软件功能模块的形式实现并非作为独立的产品进行销售或使用时，可以存储在一个计算机可读取存储介质中，基于这样的理解，本实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台 计算机设备(可以是个人计算机，服务器，或者网络设备等)或processor(处理器)执行本实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
因此，本申请实施例提供了一种计算机存储介质，该计算机存储介质存储有图像预测程序，所述图像预测程序被至少一个处理器执行时实现前述实施例所述方法的步骤。
基于上述编码器80的组成以及计算机存储介质，参见图9，其示出了本申请实施例提供的编码器80的具体硬件结构，可以包括：第一通信接口901、第一存储器902和第一处理器903；各个组件通过第一总线***904耦合在一起。可理解，第一总线***904用于实现这些组件之间的连接通信。第一总线***904除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见，在图9中将各种总线都标为第一总线***904。其中，
第一通信接口901，用于在与其他外部网元之间进行收发信息过程中，信号的接收和发送；
第一存储器902，用于存储能够在第一处理器903上运行的计算机程序；
第一处理器903，用于在运行所述计算机程序时，执行：
确定图像中当前块的至少一个图像分量；
对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
可以理解，本申请实施例中的第一存储器902可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本申请描述的***和方法的第一存储器902旨在包括但不限于这些和任意其它适合类型的存储器。
而第一处理器903可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过第一处理器903中的硬件的集成逻辑电路或者软件形式的指令完成。上述的第一处理器903可以是通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于第一存储器902，第一处理器903读取第一存储器902中的信息，结合其硬件完成上述方法的步骤。
可以理解的是，本申请描述的这些实施例可以用硬件、软件、固件、中间件、微码或 其组合来实现。对于硬件实现，处理单元可以实现在一个或多个专用集成电路(Application Specific Integrated Circuits，ASIC)、数字信号处理器(Digital Signal Processing，DSP)、数字信号处理设备(DSP Device，DSPD)、可编程逻辑设备(Programmable Logic Device，PLD)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)、通用处理器、控制器、微控制器、微处理器、用于执行本申请所述功能的其它电子单元或其组合中。对于软件实现，可通过执行本申请所述功能的模块(例如过程、函数等)来实现本申请所述的技术。软件代码可存储在存储器中并通过处理器执行。存储器可以在处理器中或在处理器外部实现。
可选地，作为另一个实施例，第一处理器903还配置为在运行所述计算机程序时，执行前述实施例中任一项所述的方法。
本实施例提供了一种编码器，该编码器可以包括第一确定单元、第一处理单元和第一构建单元，其中，第一确定单元配置为确定图像中当前块的至少一个图像分量；第一处理单元配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；第一构建单元配置为根据所述预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，从而提高了预测效率，同时还提高了视频图像的编解码效率。
基于前述实施例相同的发明构思，参见图10，其示出了本申请实施例提供的一种解码器100的组成结构示意图。该解码器100可以包括第二确定单元1001、第二处理单元1002和第二构建单元1003，其中，
所述第二确定单元1001，配置为确定图像中当前块的至少一个图像分量；
所述第二处理单元1002，配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
所述第二构建单元1003，配置为根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
在上述方案中，参见图10，解码器100还可以包括第二统计单元1004和第二获取单元1005，其中，
所述第二统计单元1004，配置为对所述当前块的至少一个图像分量进行特性统计；其中，所述至少一个图像分量包括第一图像分量和/或第二图像分量；
所述第二获取单元1005，配置为根据特性统计的结果，获取所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值；其中，所述第一图像分量为构建所述预测模型时所用于预测的分量，所述第二图像分量为构建所述预测模型时所被预测的分量。
在上述方案中，所述第二处理单元1002，还配置为基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，利用预设处理模式对所述第一图像分量进行第一处理；其中，所述预设处理模式至少包括下述其中之一：过滤处理、分组处理、值修正处理、量化处理和去量化处理；
所述第二获取单元1005，还配置为根据第一处理的结果，获得所述当前块的第一图像分量的处理值。
在上述方案中，参见图10，解码器100还可以包括第二调整单元1006和第二更新单元1007，其中，
所述第二调整单元1006，配置为当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
所述第二更新单元1007，配置为基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的参考值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
在上述方案中，所述第二调整单元1006，还配置为当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
所述第二更新单元1007，还配置为基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的处理值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
在上述方案中，所述第二调整单元1006，还配置为当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，对所述第一图像分量进行第二处理；其中，所述第二处理包括上采样及预设处理模式的相关处理、或者下采样及预设处理模式的相关处理；
所述第二获取单元1005，还配置为根据第二处理的结果，获得所述当前块的第一图像分量的处理值；其中，处理后的所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率相同。
在上述方案中，所述第二构建单元1003，配置为解析码流，根据解析得到的模型参数，构建所述预测模型。
在上述方案中，参见图10，解码器100还可以包括第二预测单元1008，配置为根据所述预测模型对所述当前块的第二图像分量进行跨分量预测，得到所述当前块的第二图像分量的预测值。
可以理解地，在本实施例中，“单元”可以是部分电路、部分处理器、部分程序或软件等等，当然也可以是模块，还可以是非模块化的。而且在本实施例中的各组成部分可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
所述集成的单元如果以软件功能模块的形式实现并非作为独立的产品进行销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本实施例提供了一种计算机存储介质，该计算机存储介质存储有图像预测程序，所述图像预测程序被第二处理器执行时实现前述实施例中任一项所述的方法。
基于上述解码器100的组成以及计算机存储介质，参见图11，其示出了本申请实施例提供的解码器100的具体硬件结构，可以包括：第二通信接口1101、第二存储器1102和第二处理器1103；各个组件通过第二总线***1104耦合在一起。可理解，第二总线***1104用于实现这些组件之间的连接通信。第二总线***1104除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见，在图11中将各种总线都标为第二总线***1104。其中，
第二通信接口1101，用于在与其他外部网元之间进行收发信息过程中，信号的接收和发送；
第二存储器1102，用于存储能够在第二处理器1103上运行的计算机程序；
第二处理器1103，用于在运行所述计算机程序时，执行：
确定图像中当前块的至少一个图像分量；
对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
可选地，作为另一个实施例，第二处理器1103还配置为在运行所述计算机程序时，执行前述实施例中任一项所述的方法。
可以理解，第二存储器1102与第一存储器902的硬件功能类似，第二处理器1103与第一处理器903的硬件功能类似；这里不再详述。
本实施例提供了一种解码器，该解码器可以包括第二确定单元、第二处理单元和第二构建单元，其中，第二确定单元配置为确定图像中当前块的至少一个图像分量；第二处理单元配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；第二构建单元配置为根据所述预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，从而提高了预测效率，同时还提高了视频图像的编解码效率。
需要说明的是，在本申请中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
本申请所提供的几个方法实施例中所揭露的方法，在不冲突的情况下可以任意组合，得到新的方法实施例。
本申请所提供的几个产品实施例中所揭露的特征，在不冲突的情况下可以任意组合，得到新的产品实施例。
本申请所提供的几个方法或设备实施例中所揭露的特征，在不冲突的情况下可以任意组合，得到新的方法实施例或设备实施例。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。
工业实用性
本申请实施例中，首先确定图像中当前块的至少一个图像分量；然后对当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；再根据预处理后的至少一个图像分量，构建预测模型，该预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理；这样，在对当前块的至少一个图像分量进行预测之前，首先对该至少一个图像分量进行预处理，可以平衡跨分量预测前各图像分量的统计特性，从而提高了预测效率；另外，由于利用该预测模型预测得到的图像分量的预测值更接近于真实值，使得图像分量的预测残差较小，这样在编解码过程中所传输的比特率少，同时还提高了视频图像的编解码效率。

Claims (20)

  1. 一种图像预测方法，应用于编码器或解码器，所述方法包括：
    确定图像中当前块的至少一个图像分量；
    对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
    根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
  2. 根据权利要求1所述的方法，其中，在所述确定图像中当前块的至少一个图像分量之后，所述方法还包括：
    对所述当前块的至少一个图像分量进行特性统计；其中，所述至少一个图像分量包括第一图像分量和/或第二图像分量；
    根据特性统计的结果，获取所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值；其中，所述第一图像分量为构建所述预测模型时所用于预测的分量，所述第二图像分量为构建所述预测模型时所被预测的分量。
  3. 根据权利要求2所述的方法，其中，所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量，包括：
    基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，利用预设处理模式对所述第一图像分量进行第一处理；其中，所述预设处理模式至少包括下述其中之一：过滤处理、分组处理、值修正处理、量化处理和去量化处理；
    根据第一处理的结果，获得所述当前块的第一图像分量的处理值。
  4. 根据权利要求2所述的方法，其中，在所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量之前，所述方法还包括：
    当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
    基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的参考值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
  5. 根据权利要求3所述的方法，其中，在所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量之后，所述方法还包括：
    当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，对所述第一图像分量的分辨率进行分辨率调整；其中，所述分辨率调整包括上采样调整或下采样调整；
    基于调整后的所述第一图像分量的分辨率，更新所述当前块的第一图像分量的处理值；其中，调整后的所述第一图像分量的分辨率与所述第二图像分量的分辨率相同。
  6. 根据权利要求2所述的方法，其中，所述对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量，包括：
    当所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率不同时，基于所述当前块的第一图像分量的参考值和/或所述当前块的第二图像分量的参考值，对所述第一图像分量进行第二处理；其中，所述第二处理包括上采样及预设处理模式的相关处理、或者下采样及预设处理模式的相关处理；
    根据第二处理的结果，获得所述当前块的第一图像分量的处理值；其中，处理后的所述当前块的第一图像分量的分辨率与所述当前块的第二图像分量的分辨率相同。
  7. 根据权利要求3、5或6任一项所述的方法，其中，所述根据所述预处理后的至少一个图像分量，构建预测模型，包括：
    根据所述第一图像分量的处理值和所述第二图像分量的参考值，确定所述预测模型的模型参数；
    根据所述模型参数，构建所述预测模型。
  8. 根据权利要求7所述的方法，其中，在所述构建预测模型之后，所述方法还包括：
    根据所述预测模型对所述当前块的第二图像分量进行跨分量预测，得到所述当前块的第二图像分量的预测值。
  9. 一种图像预测方法，应用于编码器或解码器，所述方法包括：
    确定图像中当前块的第一图像分量的参考值；其中，所述当前块的第一图像分量的参考值是所述当前块相邻像素的第一图像分量值；
    对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值；
    利用所述滤波后的参考值计算预测模型的模型参数，其中，所述预测模型用于将所述当前块的第一图像分量的取值映射为所述当前块的第二图像分量的取值，所述第二图像分量不同于所述第一图像分量。
  10. 根据权利要求9所述的方法，其中，所述利用所述滤波后的参考值计算分量预测模型的模型参数，包括：
    对所述图像的至少一个图像分量或所述当前块的至少一个图像分量进行特性统计，其中，所述至少一个图像分量包含所述第一图像分量和/所述第二图像分量；
    根据特性统计的结果，获取所述当前块的第二图像分量的参考值；其中，所述当前块的第二图像分量的参考值是所述当前块相邻像素的第二图像分量值；
    利用所述滤波后的参考值和所述当前块的第二图像分量的参考值，计算所述预测模型的模型参数。
  11. 根据权利要求9所述的方法，其中，所述对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值，包括：
    当所述图像的第二图像分量的分辨率与所述图像的第一图像分量的分辨率不同时，对所述当前块的第一图像分量的参考值进行第一调整处理，更新所述当前块的第一图像分量的参考值，其中，所述第一调整处理包括以下之一：下采样滤波，上采样滤波；
    对所述当前块的第一图像分量的参考值进行所述滤波处理，得到所述滤波后的参考值。
  12. 根据权利要求9或11所述的方法，其中，所述方法还包括：
    根据所述当前块的第一图像分量的参考值，利用预设处理模式对所述参考值进行滤波处理；其中，所述预设处理模式至少包括下述其中之一：过滤处理、分组处理、值修正处理、量化处理、去量化处理、低通滤波和自适应滤波。
  13. 根据权利要求9所述的方法，其中，所述对所述当前块的第一图像分量的参考值进行滤波处理，得到滤波后的参考值，包括：
    当所述图像的第二图像分量的分辨率与所述图像的第一图像分量的分辨率不同时，对所述当前块的第二图像分量的参考值进行第二调整处理，更新所述当前块的第一图像分量的第一参考值，其中，所述第二调整处理包括：下采样和平滑滤波，或者，上采样和平滑滤波。
  14. 根据权利要求9所述的方法，其中，所述利用所述滤波后的参考值计算分量预测模型的模型参数，包括：
    确定所述当前块的第二图像分量的参考值；其中，所述当前块的第二图像分量的参考值是所述当前块相邻像素的第二图像分量值；
    利用所述滤波后的参考值和所述当前块的第二图像分量的参考值计算所述分量预测模型的模型参数。
  15. 根据权利要求9所述的方法，其中，在所述利用所述滤波后的参考值计算预测模型的模型参数之后，还包括：
    根据所述预测模型，对所述当前块的第一图像分量的值进行映射，得到所述当前块的第二图像分量的预测值。
  16. 一种编码器，所述编码器包括第一确定单元、第一处理单元和第一构建单元，其中，
    所述第一确定单元，配置为确定图像中当前块的至少一个图像分量；
    所述第一处理单元，配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
    所述第一构建单元，配置为根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
  17. 一种编码器，所述编码器包括第一存储器和第一处理器，其中，
    所述第一存储器，用于存储能够在所述第一处理器上运行的计算机程序；
    所述第一处理器，用于在运行所述计算机程序时，执行如权利要求1至15任一项所述的方法。
  18. 一种解码器，所述解码器包括第二确定单元、第二处理单元和第二构建单元，其中，
    所述第二确定单元，配置为确定图像中当前块的至少一个图像分量；
    所述第二处理单元，配置为对所述当前块的至少一个图像分量进行预处理，得到预处理后的至少一个图像分量；
    所述第二构建单元，配置为根据所述预处理后的至少一个图像分量，构建预测模型；其中，所述预测模型用于对所述当前块的至少一个图像分量进行跨分量预测处理。
  19. 一种解码器，所述解码器包括第二存储器和第二处理器，其中，
    所述第二存储器，用于存储能够在所述第二处理器上运行的计算机程序；
    所述第二处理器，用于在运行所述计算机程序时，执行如权利要求1至15任一项所述的方法。
  20. 一种计算机存储介质，其中，所述计算机存储介质存储有图像预测程序，所述图像预测程序被第一处理器或第二处理器执行时实现如权利要求1至15任一项所述的方法。
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