CN111526371B - Video intra-frame coding rapid algorithm based on Gabor features and gray level co-occurrence matrix - Google Patents

Video intra-frame coding rapid algorithm based on Gabor features and gray level co-occurrence matrix Download PDF

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CN111526371B
CN111526371B CN202010366824.6A CN202010366824A CN111526371B CN 111526371 B CN111526371 B CN 111526371B CN 202010366824 A CN202010366824 A CN 202010366824A CN 111526371 B CN111526371 B CN 111526371B
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gabor
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CN111526371A (en
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陈婧
欧健珊
曾焕强
朱建清
蔡灿辉
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Huaqiao University
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    • H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
    • 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
    • H04N19/102—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
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    • 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
    • H04N19/134—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
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Abstract

The invention relates to a video intra-frame coding fast algorithm based on Gabor characteristics and gray level co-occurrence matrixes, and belongs to the field of video coding. The method comprises the steps of setting the partition depth of a threshold pre-judging Coding module by utilizing the relation between the number of nonzero values of a gray level co-occurrence matrix and the partition depth of a Coding Tree Unit (CTU) to obtain depth intervals corresponding to different thresholds; then, coding Units (CUs) in the depth range are classified by using Gabor characteristics, flat blocks and complex blocks are divided, and finally different Coding schemes are selected according to different CU types. The Gabor feature and gray level co-occurrence matrix-based fast intra-frame coding algorithm for the screen content video can reduce the calculation overhead of a coder, and reduce the coding time under the condition of keeping the video quality basically unchanged.

Description

Video intra-frame coding rapid algorithm based on Gabor features and gray level co-occurrence matrix
Technical Field
The invention relates to the field of video coding and decoding, in particular to a Gabor characteristic and gray level co-occurrence matrix-based screen content video intra-frame coding rapid algorithm suitable for screen content video coding.
Background
With the rapid development of cloud computing and mobile internet technologies, screen Content (SC) -based video applications such as 3D games, 3D cartoons, remote video education, and remote video conferences are becoming more and more widespread. The screen content video is different from the conventional natural video in that it may include both text, diagrams, graphic regions directly generated by a computer and natural image regions. Therefore, the screen content video is characterized by a large area of flat area, less capture noise, repetitive patterns and characters, colors of limited color types, high image contrast, sharp edges, and the like. Therefore, several new tools are added to the HEVC-SCC Coding standard for Screen Content Coding (SCC), namely Intra Block Copy (IBC), palette Mode (PLT), adaptive Color Transform (ACT), and Adaptive Motion Vector Resolution (AMVR). The intra-frame block copying tool is mainly suitable for a large number of repeated areas (such as repeated text areas) contained in screen content, and breaks the limitation that intra-frame prediction can only be predicted from adjacent blocks; the palette mode tool has a remarkable effect on coding a graphic area with complex textures but limited color number; considering the correlation between color components, the adaptive color transform tool can effectively improve the coding efficiency, because the color space conversion can help eliminate the redundancy of color components between frames; the self-adaptive motion vector decomposition is mainly used for solving the problem of integer values in some screen contents, and effectively saves bit overhead. These new coding tools can significantly improve the coding efficiency of screen content, but also have the problems of insufficient parallelism, high coding complexity, and the like. Therefore, a fast algorithm is provided for the problems of high coding complexity and high time cost of HEVC-SCC, the coding complexity and the time cost are reduced under the condition of keeping the coding performance basically unchanged, and the method has certain research significance and practical value.
Disclosure of Invention
The invention aims to reduce the time cost of the existing HEVC-SCC coding technology, and provides a rapid algorithm for screen content video intra-frame coding based on Gabor characteristics and gray level co-occurrence matrixes.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a fast algorithm for screen content video intra-frame coding based on Gabor characteristics and gray level co-occurrence matrix comprises the following steps:
step S1, counting the relation between the segmentation depth of the CTU and the number of nonzero values in a gray level co-occurrence matrix, setting three threshold values T0, T1 and T2 according to the condition of the gray level co-occurrence matrix generated under the condition of the CTU type segmentation depth, and carrying out interval division on the segmentation depth, wherein T0 is more than T1 and less than T2;
s2, classifying natural contents and screen contents of the CUs in the divided different-depth intervals by using Gabor features, selecting Gabor filters in the horizontal direction and the vertical direction, and convolving the image block with the filters in the horizontal direction and the vertical direction to obtain the Gabor features of the image block:
a horizontal direction filter:
Figure BDA0002476746220000021
vertical filter:
Figure BDA0002476746220000022
Figure BDA0002476746220000023
when the temperature is higher than the set temperature
Figure BDA0002476746220000024
If so, judging the content to be the screen content CU, otherwise, judging the content to be the natural content CU, wherein,
Figure BDA0002476746220000025
the image block is a block of an image,
Figure BDA0002476746220000026
is the Gabor characteristic, th, of the image block 1 Is a set threshold value;
s3, judging the texture complexity of the screen content CU;
s31: if it is
Figure BDA0002476746220000027
The coding block belongs to a simple block, and a DC, planar, horizontal or vertical mode is selected; wherein Th 2 Is a set threshold value;
s32: otherwise, the coding block is a complex block, and an IBC or PLT mode is selected.
The invention has the following beneficial effects:
1. the optimal CU segmentation depth and size are judged in advance through the number of non-zero values of the gray level co-occurrence matrix, so that the calculation and judgment processes of other CU sizes are omitted.
2. According to the characteristic that the screen content image has sharp edges, the Gabor characteristics can be used for distinguishing natural content and the screen content area, and the corresponding processing scheme is selected according to different CU types, so that the calculation time of the prediction process can be effectively shortened.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a graph comparing the method of the present invention with the original method.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and embodiments, but the fast algorithm for video intra-frame coding of screen content based on Gabor features and gray level co-occurrence matrix is not limited to the embodiments.
Referring to fig. 1, in order to solve the problems of high computational complexity and high time cost of the conventional HEVC-SCC standard, the present invention provides a fast algorithm for intra-frame coding of screen content video based on Gabor features and gray level co-occurrence matrices, which specifically includes the following steps:
step 1, firstly, counting the relation between the segmentation depth of each CTU and the number of nonzero values in the gray level co-occurrence matrix thereof, setting three thresholds T0, T1 and T2 according to the gray level co-occurrence matrix generated under the condition of the segmentation depth of four types of CTUs (the sizes of the CTUs are 4: 64 × 64, 32 × 32, 16 × 16 and 8 × 8), obtaining T0=6, T1=32 and T2=49 through experiments, meeting the rule that T0 is less than T1 and less than T2, carrying out interval division on the segmentation depths of depth0, depth1, depth2 and depth3 corresponding to the four types of CTUs, namely judging the segmentation depth of the CTU in advance according to different intervals of non-zero number distribution of the gray level co-occurrence matrix of the CTU, and selectively skipping part of intra-frame prediction modes.
And 2, secondly, classifying natural contents and screen contents of the CUs in the divided different depth intervals by using Gabor characteristics, and only selecting Gabor filters in the horizontal direction and the vertical direction in the method in consideration of efficiency and calculation complexity, wherein the formulas (1) and (2) are as follows:
horizontal direction:
Figure BDA0002476746220000041
vertical direction:
Figure BDA0002476746220000042
convolving the image block with filters in horizontal and vertical directions to obtain the Gabor characteristics of the image block,
Figure BDA0002476746220000043
when in use
Figure BDA0002476746220000044
If so, judging the content CU as the screen content CU, otherwise, judging the content CU as the natural content CU. Wherein the content of the first and second substances,
Figure BDA0002476746220000045
the image block is a block of an image,
Figure BDA0002476746220000046
is the Gabor feature of the image block; th 1 Is a set threshold value;
and step 3, finally, judging the texture complexity of the screen content CU. If it is not
Figure BDA0002476746220000047
When the coding block belongs to the simple block, selecting one of DC, planar, horizontal and vertical modes as the best mode; otherwise, selecting IBC and PLT modes and further dividing the same, th 2 Is a set threshold.
Wherein Th 1 And Th 2 Is a threshold parameter statistically derived through multiple experiments based on Gabor characteristics.
In the embodiment of the invention, rate-distortion curves of test sequences 'WebBrowsing' and 'MissionControlClip 2' are given, and the rate-distortion curves of the method provided by the invention and the original method of HEVC-SCC are compared.
For example, fig. 2 (a) is a rate-distortion curve of the test sequence "WebBrowsing", and fig. 2 (b) is a rate-distortion curve of the test sequence "missioncontroclip 2". The abscissa Bitrate represents a code rate, the ordinate PSNR represents a peak signal-to-noise ratio, and the SCM-8.3 represents an HEVC-SCC standard test platform.
As can be seen from the above figures, the rate-distortion curve of the proposed method is very close to that of the HEVC-SCC test model algorithm. This means that the method proposed by the present invention can effectively reduce the computational complexity of the encoder, and the video quality loss is negligible.
The above-described embodiments are merely illustrative of the present invention and are not intended to limit the present invention, and variations, modifications and the like of the above-described embodiments may be made within the scope of the claims of the present invention as long as they are in accordance with the technical spirit of the present invention.

Claims (1)

1. A fast algorithm for video intra coding based on Gabor features and gray level co-occurrence matrix is characterized by comprising the following steps:
step S1, counting the relation between the segmentation depth of the CTU and the number of nonzero values in a gray level co-occurrence matrix, setting three threshold values T0, T1 and T2 according to the situation of the gray level co-occurrence matrix generated under the situation of the CTU type segmentation depth, and carrying out interval segmentation on the segmentation depth, wherein T0 is less than T1 and less than T2;
s2, classifying natural contents and screen contents of the CUs in the divided different-depth intervals by using Gabor features, selecting Gabor filters in the horizontal direction and the vertical direction, and convolving the image block with the filters in the horizontal direction and the vertical direction to obtain the Gabor features of the image block:
a horizontal direction filter:
Figure FDA0003815899580000011
vertical filter:
Figure FDA0003815899580000012
Figure FDA0003815899580000013
when in use
Figure FDA0003815899580000014
If so, judging the content to be the screen content CU, otherwise, judging the content to be the natural content CU, wherein,
Figure FDA0003815899580000015
is an image block of a picture,
Figure FDA0003815899580000016
is Gabor characteristic, th, of the image block 1 Is a set threshold;
s3, judging the texture complexity of the screen content CU;
s31: if it is
Figure FDA0003815899580000017
The coding block belongs to a simple block, and DC, planar, horizontal or vertical mode is selected; wherein Th 2 Is a set threshold value;
s32: otherwise, the coding block is a complex block, and an IBC or PLT mode is selected.
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