CN113766229B - Encoding method, decoding method, device, equipment and readable storage medium - Google Patents
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Abstract
The application discloses an encoding method, a decoding method, a device, equipment and a readable storage medium, which relate to the technical field of image processing and are used for improving processing performance. The method comprises the following steps: clustering point cloud data to be processed of the current frame to obtain a plurality of sub point clouds; generating a generalized Laplace matrix for any target sub-point cloud in the plurality of sub-point clouds according to Euclidean distances between a plurality of point pairs in the target sub-point cloud and Euclidean distances between a target point in the target sub-point cloud and a corresponding point of the target point; performing inter-frame prediction and graph Fourier residual transformation on the target sub-point cloud by using the generalized Laplacian matrix; respectively quantizing and encoding the transformed multiple sub-point clouds to obtain an encoded code stream; the corresponding point is located in a reference point cloud of the target sub-point cloud, and the reference point cloud is located in a reference frame of the current frame. The embodiment of the application can improve the processing performance.
Description
Technical Field
The present disclosure relates to the field of image processing technologies, and in particular, to an encoding method, a decoding method, an apparatus, a device, and a readable storage medium.
Background
With the development of computer hardware and algorithms, the acquisition of three-dimensional point cloud data is more convenient, and the data volume of the point cloud data is also larger. The point cloud data is composed of a large number of three-dimensional unordered points, each of which includes position information (X, Y, Z) and several attribute information (colors, normal vectors, etc.).
In order to facilitate storage and transmission of point cloud data, point cloud compression technology is becoming a focus of attention. The prior art provides a scheme for selectively encoding one or more 3D point clouds using inter-coding (e.g., motion compensation) techniques of previously encoded/decoded frames. However, such a scheme has poor processing performance such as encoding.
Disclosure of Invention
The embodiment of the application provides an encoding method, a decoding method, a device, equipment and a readable storage medium, so as to improve processing performance.
In a first aspect, an embodiment of the present application provides an encoding method, which is applied to an encoding device, including:
clustering point cloud data to be processed of the current frame to obtain a plurality of sub point clouds;
generating a generalized Laplace matrix for any target sub-point cloud in the plurality of sub-point clouds according to Euclidean distances between a plurality of point pairs in the target sub-point cloud and Euclidean distances between a target point in the target sub-point cloud and a corresponding point of the target point;
performing inter-frame prediction and graph Fourier residual transformation on the target sub-point cloud by using the generalized Laplacian matrix;
respectively quantizing and encoding the transformed multiple sub-point clouds to obtain an encoded code stream;
the corresponding point is located in a reference point cloud of the target sub-point cloud, and the reference point cloud is located in a reference frame of the current frame.
In a second aspect, embodiments of the present application further provide a decoding method, applied to a decoding device, where the method includes:
acquiring a coded code stream;
performing graph Fourier inverse transformation based on Euclidean distance weight on the coded code stream to obtain a transformation result;
based on the transformation result, obtaining a decoded code stream;
the coding code stream is obtained by coding the results of inter-frame prediction and graph Fourier residual transformation of the sub-point cloud by coding equipment.
In a third aspect, embodiments of the present application further provide an encoding apparatus, including:
the first acquisition module is used for clustering the point cloud data to be processed of the current frame to obtain a plurality of sub point clouds;
the first generation module is used for generating a generalized Laplace matrix for any target sub-point cloud in the multiple sub-point clouds according to Euclidean distances between multiple point pairs in the target sub-point cloud and Euclidean distances between a target point in the target sub-point cloud and a corresponding point of the target point;
the first transformation module is used for carrying out inter-frame prediction and graph Fourier residual transformation on the target sub-point cloud by utilizing the generalized Laplace matrix;
the first coding module is used for respectively quantizing and coding the transformed multiple sub-point clouds to obtain a coded code stream;
the corresponding point is located in a reference point cloud of the target sub-point cloud, and the reference point cloud is located in a reference frame of the current frame.
In a fourth aspect, embodiments of the present application further provide a decoding apparatus, including:
the first acquisition module is used for acquiring the coded code stream;
the first transformation module is used for carrying out graph Fourier inverse transformation based on Euclidean distance weight on the coded code stream to obtain a transformation result;
the first decoding module is used for obtaining a decoding code stream based on the transformation result;
the coding code stream is obtained by coding the results of inter-frame prediction and graph Fourier residual transformation of the sub-point cloud by coding equipment.
In a fifth aspect, embodiments of the present application further provide an electronic device, including: a memory, a processor and a program stored on the memory and executable on the processor, which processor implements the steps in the encoding method or decoding method as described above when executing the program.
In a sixth aspect, embodiments of the present application also provide a readable storage medium having a program stored thereon, which when executed by a processor, implements steps in an encoding method or a decoding method as described above.
In the embodiment of the application, the point cloud data to be processed of the current frame are clustered to obtain a plurality of sub-point clouds, and for any target sub-point cloud, a generalized Laplace matrix is generated according to Euclidean distances between a plurality of point pairs in the target sub-point clouds and Euclidean distances between target points in the target sub-point clouds and corresponding points of the target points, and the generalized Laplace matrix is utilized to respectively perform inter-frame prediction and graph Fourier residual transformation on the plurality of sub-point clouds, so that a coding code stream is obtained based on a transformation result. Because the generalized Laplace matrix is generated by utilizing Euclidean distance between points, global correlation characteristics can be utilized in the embodiment of the application, so that the correlation between the points is more fully expressed, the similarity between the point cloud data can be removed as much as possible, and the coding performance is improved.
The performance of the encoding end is improved, and correspondingly, for the decoding end, the data to be decoded is optimized, so that the decoding efficiency and performance can be correspondingly improved.
Drawings
FIG. 1 is a flow chart of an encoding method provided by an embodiment of the present application;
FIGS. 2 and 3 are schematic illustrations of the effects of the method of the embodiments of the present application and the prior art method;
FIG. 4 is a flow chart of a decoding method provided by an embodiment of the present application;
FIG. 5 is a block diagram of an encoding apparatus provided in an embodiment of the present application;
fig. 6 is a block diagram of a decoding apparatus according to an embodiment of the present application.
Detailed Description
In the embodiment of the application, the term "and/or" describes the association relationship of the association objects, which means that three relationships may exist, for example, a and/or B may be represented: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The term "plurality" in the embodiments of the present application means two or more, and other adjectives are similar thereto.
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Referring to fig. 1, fig. 1 is a flowchart of an encoding method provided in an embodiment of the present application, which is applied to an encoding apparatus. As shown in fig. 1, the method comprises the following steps:
In the step, the point cloud data to be processed is subjected to voxel conversion to obtain point cloud voxels, and then the point cloud voxels are clustered to obtain a plurality of sub point clouds.
Specifically, a three-dimensional grid with a preset size is constructed, point cloud data to be processed are placed in the constructed three-dimensional grid to obtain coordinates of each point, and the three-dimensional grid containing the points is used as point cloud voxels to obtain a plurality of point cloud voxels. In addition, the coordinate and attribute information of each point cloud voxel can be obtained. Wherein the attribute information includes intensity, color, and the like. In the embodiment of the application, the coordinates of the point cloud voxels are specifically the coordinates of the center point of each point in the point cloud voxels; the color information of the point cloud voxel is specifically an average value of the color information of each point in the point cloud voxel. In practical application, the point cloud data to be processed can be subjected to voxel conversion in an octree mode and the like, so that a plurality of point cloud voxels are obtained.
And clustering the point cloud data by using a space uniform partitioning method. The clustering method may employ, for example, a K-means clustering method.
In the embodiment of the application, the point cloud data to be processed is divided into a plurality of sub-point clouds based on the position information, and the space is uniformly divided. Each sub-point cloud may be encoded independently.
Any one of the multiple sub-point clouds can be used as the target sub-point cloud. In practical application, the processing mode of each target sub-point cloud is the same.
Specifically, in this step, the following may be included:
s1021, obtaining a weight matrix according to Euclidean distances among a plurality of point pairs in the target sub-point cloud.
Wherein the target sub-point cloud can comprise a plurality of points, and each two points form the implementationOne point pair in the example. In the embodiment of the application, the Euclidean distance between two points in each pair of points is calculated. For example, for the ith point and the jth point in the target sub-point cloud, the euclidean distance between the ith point and the jth point is calculated. Specifically, for point i (x 1 ，x 2 ……x n ) And point j (y 1 ，y 2 ……y n ) The Euclidean distance d (i, j) between the two can be calculated according to the following formula in practical application:
wherein i is more than or equal to 1 and less than or equal to M, j is more than or equal to 1 and less than or equal to M, i, j and M are integers, and M is the total number of points included in the target sub-point cloud.
Thereafter, weights are calculated according to the following formula, and the weight matrix W is formed using the weights:
wherein ,Wij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; distance represents the Euclidean distance between the ith point and the jth point; σ is a constant that is not equal to 0, representing the adjustment parameter.
And S1022, obtaining the Laplace matrix according to the degree matrix and the weight matrix.
In this step, the difference between the availability matrix and the weight matrix is used as a laplace matrix.
Specific: l=d-W, L represents a laplace matrix, D represents a degree matrix, and W represents a weight matrix.
Wherein the diagonal element d of the degree matrix i ＝∑ j W ij The other element is 0. Wherein d i The ith diagonal element of the degree matrix, W ij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud
S1023, generating a diagonal matrix.
The diagonal matrix is generated from Euclidean distances between target points in the target sub-point cloud and corresponding points of the target points.
Specifically, a reference point cloud of the target sub-point cloud may be first determined in the reference frame. For example, motion estimation is performed in a reference frame, finding a matching reference point cloud. The target sub-point clouds and the reference points are in one-to-one correspondence. For example, using an iterative closest point algorithm, in the reference frame, a reference point cloud for the target sub-point cloud may be determined based on Euclidean distance. Then, the diagonal matrix D is generated based on Euclidean distance between each point in the target sub-point cloud and the corresponding point of each point in the reference point cloud w . Wherein the value on the ith diagonal of the diagonal matrix is the reciprocal of the Euclidean distance between the ith point and the point p, and the other elements are 0. Wherein the point p is the corresponding point of the ith point in the reference point cloud.
S1024, obtaining the generalized Laplace matrix according to the diagonal matrix and the Laplace matrix.
In this step, the sum of the diagonal matrix and the laplacian matrix is used as the generalized laplacian matrix.
Specific: lg=l+d w Wherein Lg represents a generalized Laplace matrix, L represents a Laplace matrix, and D w Representing a diagonal matrix.
And 103, performing inter-frame prediction and graph Fourier residual transformation on the target sub-point cloud by using the generalized Laplacian matrix.
In this step, the inter-prediction and graph fourier residual transform may be understood as inter-prediction and graph fourier residual transform based on euclidean distance weights, and may include the following contents:
s1031, obtaining an attribute predicted value of the reference frame on the target attribute of the current frame.
In the embodiment of the application, an inter-frame prediction method is adopted, and a reference frame is utilized to predict the attribute value of the current frame. Wherein the attributes may include color, intensity, normal vector, etc. Then the target attribute may be any attribute.
Specifically, in this step, the attribute prediction value of the target attribute of the reference frame to the current frame is obtained according to the following formula:
wherein ,
attribute predictors, x, representing target attributes of a reference frame to a current frame t-1 Attribute values representing target attributes of the reference frame, lg represents a generalized Laplacian matrix.
S1032, generating a residual error of the target attribute of the current frame according to the attribute value of the target attribute of the current frame and the attribute predicted value of the target attribute of the reference frame to the current frame.
Specifically, here, a difference between an attribute value of the target attribute of the current frame and an attribute prediction value of the target attribute of the reference frame to the current frame may be used as the residual:
wherein delta represents the residual of the target property of the current frame,
attribute predictors, x, representing target attributes of a reference frame to a current frame t An attribute value representing a target attribute of the current frame.
The residual is obtained by the inter prediction method, so that the difference between two frames can be obtained as much as possible. Since the same part between two frames does not need additional processing, the code rate can be saved by calculating the residual error.
S1033, transforming the residual error of the target attribute of the current frame based on the generalized Laplace matrix.
In this step, a transformation matrix is obtained by using the generalized laplace matrix, and then, a residual error of the target attribute of the current frame is transformed by using the transformation matrix.
Specifically, the following formula is solved to obtain a transformation matrix:
wherein Lg represents a generalized Laplace matrix,
representing the transformation matrix.
The transformation result is obtained using the following formula:
wherein ,
representing the transformation result +.>
Representing a transformation matrix, delta representing the residual of the target attribute of the current frame.
In the embodiment of the application, the concept of the generalized graph Fourier transform is introduced on the basis of the traditional graph Fourier transform, and prediction and residual transformation are performed on the inter-frame attribute of the point cloud data, so that the redundancy among the data can be further removed, and the coding efficiency is improved.
The processing mode of other sub-point clouds is the same as the processing mode of the target sub-point clouds.
And 104, respectively quantizing and encoding the transformed multiple sub-point clouds to obtain an encoded code stream.
In the step, the transformed multiple sub-point clouds are uniformly quantized and arithmetically encoded to obtain an encoded code stream.
Taking the target attribute as a color as an example, here, the color can be decomposed into three 3×1 vectors (YUV or RGB). Taking the Y component as an example, the attribute value of the current frame is predicted according to the procedure in S1031, and a residual is generated according to S1032. After that, the residual is transformed by S1033. And uniformly quantizing and arithmetic coding the transformed Y component to obtain a code stream. For each component, the processing may be performed in the same manner as for the Y component.
In the embodiment of the application, since the generalized Laplace matrix is generated by using Euclidean distance between points, global correlation characteristics can be utilized in the embodiment of the application, and the correlation between the points is more fully expressed, so that the similarity between the point cloud data can be removed as much as possible, and the coding performance is improved.
In practical applications, tests are performed on a sequence of actual point clouds. In the test, first, a test is performed on a 16-frame dynamic point cloud, and a comparison of the performance of the method of the embodiment of the present application and the performance of the RAHT (Region-Adaptive Hierarchical Transform, region adaptive level transform) NWGFT (main direction weight map fourier transform) method is shown in fig. 2. In order to quantify the gain, a comparison of the data with the RAHT method was made in the experiment, as shown in fig. 3.
As can be seen from fig. 2 and fig. 3, in the embodiment of the present application, based on the conventional graph fourier transform, the concept of the generalized graph fourier transform is introduced, and prediction and residual transformation are performed on the inter-frame attribute of the point cloud, so that redundancy between data can be further removed, and coding efficiency is improved. Experimental results show that the method can improve subjective and objective performance and can be applied to compression, transmission and storage systems of actual point clouds.
Referring to fig. 4, fig. 4 is a flowchart of a decoding method provided in an embodiment of the present application, which is applied to an encoding apparatus. As shown in fig. 4, the method comprises the following steps:
The coding code stream is obtained by coding a result of inter-frame prediction and graph Fourier residual transformation of the sub-point cloud by using a generalized Laplace matrix by coding equipment.
And step 402, performing graph Fourier transform based on Euclidean distance weight on the coded code stream to obtain a transform result.
At the decoding end, after entropy decoding is carried out on the coded code stream, inverse quantization is carried out on the coded code stream. And then, performing graph Fourier inverse transformation based on Euclidean distance weight on the code stream after inverse quantization to obtain a transformation result.
Specifically, the following formula may be used to perform the graph fourier transform based on the euclidean distance weight on the dequantized encoded code stream:
wherein ,
representing the inverse transform residual value,/->
Representing a transformation matrix->
The quantized residual value representing the target property of the current frame, epsilon represents the dequantization coefficient.
And step 403, obtaining a decoding code stream based on the transformation result.
In the embodiment of the application, since the generalized Laplace matrix is generated by using Euclidean distance between points, global correlation characteristics can be utilized in the embodiment of the application, and the correlation between the points is more fully expressed, so that the similarity between the point cloud data can be removed as much as possible, and the coding performance is improved. The performance of the encoding end is improved, and correspondingly, for the decoding end, the data to be decoded is optimized, so that the decoding efficiency and performance can be correspondingly improved.
The embodiment of the application also provides a coding device. Referring to fig. 5, fig. 5 is a block diagram of an encoding apparatus provided in an embodiment of the present application. Since the principle of the coding device for solving the problem is similar to that of the coding method in the embodiment of the present application, the implementation of the coding device can refer to the implementation of the method, and the repetition is omitted.
As shown in fig. 5, the encoding apparatus 500 includes:
the first obtaining module 501 is configured to cluster point cloud data to be processed of a current frame to obtain multiple sub-point clouds; a first generating module 502, configured to generate, for any one of the multiple sub-point clouds, a generalized laplace matrix according to euclidean distances between multiple point pairs in the target sub-point cloud and euclidean distances between a target point in the target sub-point cloud and a corresponding point of the target point; a first transform module 503, configured to perform inter-frame prediction and graph fourier residual transform on a target sub-point cloud by using the generalized laplace matrix; a first encoding module 504, configured to quantize and encode the transformed multiple sub-point clouds respectively, to obtain an encoded code stream; the corresponding point is located in a reference point cloud of the target sub-point cloud, and the reference point cloud is located in a reference frame of the current frame.
Optionally, the first obtaining module includes: the first processing sub-module is used for carrying out voxel formation on the point cloud data to be processed to obtain point cloud voxels; and the first acquisition sub-module is used for clustering the point cloud voxels to obtain a plurality of sub-point clouds.
Optionally, the first generating module includes:
the first acquisition sub-module is used for obtaining a weight matrix according to Euclidean distances among a plurality of point pairs in the target sub-point cloud; the second acquisition sub-module is used for obtaining a Laplace matrix according to the degree matrix and the weight matrix; the first generation submodule is used for generating a diagonal matrix; and the second generation submodule is used for obtaining the generalized Laplace matrix according to the diagonal matrix and the Laplace matrix.
Specifically, the second obtaining submodule is configured to use a difference between the degree matrix and the weight matrix as a laplace matrix; the second generating submodule is used for utilizing the sum of the diagonal matrix and the Laplace matrix as the generalized Laplace matrix.
Wherein the diagonal element d of the degree matrix i ＝∑ j W ij, wherein ,di The ith diagonal element of the degree matrix, W ij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.M, i, j, M being an integer, M being the total number of points included in the target sub-point cloud;
the diagonal matrix is generated from Euclidean distances between target points in the target sub-point cloud and corresponding points of the target points.
Optionally, the first obtaining submodule includes:
the first calculation unit is used for calculating the Euclidean distance between the ith point and the jth point in the target sub-point cloud;
a first acquisition unit for calculating weights according to the following formula, and forming the weight matrix by using the weights:
wherein ,Wij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; distance represents the Euclidean distance between the ith point and the jth point; sigma is a constant not equal to 0, representing the adjustment parameter; and i is more than or equal to 1 and less than or equal to M, j is more than or equal to 1 and less than or equal to M, i, j, M is an integer, and M is the total number of points included in the target sub-point cloud.
Optionally, the first generating sub-module includes:
a first determining unit, configured to determine a reference point cloud of the target sub-point cloud in the reference frame; a first generation unit, configured to generate the diagonal matrix based on a euclidean distance between each point in a target sub-point cloud and a corresponding point of each point in the reference point cloud; the value on the ith diagonal of the diagonal matrix is the inverse of the Euclidean distance between the ith point and a point p, wherein the point p is the corresponding point of the ith point in the reference point cloud.
Optionally, the first determining unit is configured to determine, in the reference frame, a reference point cloud of the target sub-point cloud by using an iterative closest point algorithm.
Optionally, the first transformation module includes:
a first obtaining sub-module, configured to obtain an attribute prediction value of the target attribute of the current frame by using the reference frame; a first generation sub-module, configured to generate a residual error of the target attribute of the current frame according to the attribute value of the target attribute of the current frame and the attribute prediction value of the target attribute of the reference frame to the target attribute of the current frame; and the first transformation submodule is used for transforming the residual error of the target attribute of the current frame based on the generalized Laplace matrix.
Optionally, the first obtaining submodule is configured to obtain an attribute prediction value of the target attribute of the reference frame to the current frame according to the following formula:
wherein ,
attribute predictors, x, representing target attributes of a reference frame to a current frame t-1 Attribute values representing target attributes of the reference frame, lg represents a generalized Laplacian matrix.
Optionally, the first generating sub-module is configured to use, as the residual, a difference between an attribute value of the target attribute of the current frame and an attribute prediction value of the target attribute of the reference frame to the current frame.
Optionally, the first transformation submodule includes:
the first acquisition unit is used for obtaining a transformation matrix by using the generalized Laplace matrix; and the first transformation unit is used for transforming the residual error of the target attribute of the current frame by utilizing the transformation matrix.
Optionally, the first obtaining unit is configured to solve the following formula to obtain a transformation matrix:
wherein Lg represents a generalized Laplace matrix,
representing the transformation matrix.
Optionally, the first transforming unit is configured to obtain the transforming result by using the following formula:
wherein ,
representing the transformation result +.>
Representing a transformation matrix, delta representing the residual of the target attribute of the current frame.
The device provided in the embodiment of the present application may execute the above method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
The embodiment of the application also provides a decoding device. Referring to fig. 6, fig. 6 is a block diagram of a decoding apparatus provided in an embodiment of the present application. Since the principle of the decoding device for solving the problem is similar to that of the decoding method in the embodiment of the present application, the implementation of the decoding device can refer to the implementation of the method, and the repetition is omitted.
As shown in fig. 6, the decoding apparatus 600 includes:
a first obtaining module 601, configured to obtain a coded code stream; the first transformation module 602 is configured to perform inverse fourier transform on the encoded code stream based on the euclidean distance weight, to obtain a transformation result; a first decoding module 603, configured to obtain a decoded code stream based on the transformation result; the coding code stream is obtained by coding the results of inter-frame prediction and graph Fourier residual transformation of the sub-point cloud by coding equipment.
Optionally, the first transformation module includes: the first processing submodule is used for carrying out inverse quantization on the coded code stream; and the first transformation submodule is used for carrying out graph Fourier inverse transformation based on Euclidean distance weight on the code stream after inverse quantization to obtain a transformation result.
Optionally, the first transform submodule is configured to perform graph fourier transform based on the euclidean distance weight on the dequantized coded code stream by using the following formula:
wherein ,
representing the inverse transform residual value,/->
Representing a transformation matrix->
The quantized residual value representing the target property of the current frame, epsilon represents the dequantization coefficient.
The device provided in the embodiment of the present application may execute the above method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The embodiment of the application also provides electronic equipment, which comprises: a memory, a processor and a program stored on the memory and executable on the processor, which processor implements the steps in the encoding method or decoding method as described above when executing the program.
The embodiment of the present application further provides a readable storage medium, on which a program is stored, where the program, when executed by a processor, implements each process of the foregoing encoding or decoding method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here. The readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memories (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical memories (e.g., CD, DVD, BD, HVD, etc.), semiconductor memories (e.g., ROM, EPROM, EEPROM, nonvolatile memories (NAND FLASH), solid State Disks (SSD)), etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. In light of such understanding, the technical solutions of the present application may be embodied essentially or in part in the form of a software product stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and including instructions for causing a terminal (which may be a cell phone, computer, server, air conditioner, or network device, etc.) to perform the methods described in the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.
Claims (18)
1. An encoding method applied to an encoding apparatus, comprising:
clustering point cloud data to be processed of the current frame to obtain a plurality of sub point clouds;
generating a generalized Laplace matrix for any target sub-point cloud in the plurality of sub-point clouds according to Euclidean distances between a plurality of point pairs in the target sub-point cloud and Euclidean distances between a target point in the target sub-point cloud and a corresponding point of the target point;
performing inter-frame prediction and graph Fourier residual transformation on the target sub-point cloud by using the generalized Laplace matrix;
respectively quantizing and encoding the transformed multiple sub-point clouds to obtain an encoded code stream;
the corresponding point is located in a reference point cloud of the target sub-point cloud, and the reference point cloud is located in a reference frame of the current frame;
wherein the generating a generalized laplace matrix according to the euclidean distance between the plurality of point pairs in the target sub-point cloud and the euclidean distance between the target point in the target sub-point cloud and the corresponding point of the target point comprises:
obtaining a weight matrix according to Euclidean distances among a plurality of point pairs in the target sub-point cloud;
obtaining a Laplace matrix according to the degree matrix and the weight matrix;
generating a diagonal matrix;
obtaining the generalized Laplace matrix according to the diagonal matrix and the Laplace matrix;
wherein, the difference between the degree matrix and the weight matrix is used as a Laplacian matrix;
using the sum of the diagonal matrix and the Laplace matrix as the generalized Laplace matrix;
wherein the diagonal element d of the degree matrix i ＝∑ j W ij, wherein ,di The ith diagonal element of the degree matrix, W ij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.M, i, j, M being an integer, M being the total number of points included in the target sub-point cloud;
the diagonal matrix is generated from Euclidean distances between target points in the target sub-point cloud and corresponding points of the target points.
2. The method of claim 1, wherein clustering the point cloud data to be processed of the current frame to obtain a plurality of sub point clouds comprises:
performing voxel conversion on the point cloud data to be processed to obtain point cloud voxels;
and clustering the point cloud voxels to obtain a plurality of sub-point clouds.
3. The method of claim 1, wherein the obtaining a weight matrix according to euclidean distances between a plurality of point pairs in the target sub-point cloud comprises:
calculating the Euclidean distance between the ith point and the jth point in the target sub-point cloud;
calculating weights according to the following formula, and forming the weight matrix by using the weights:
wherein ,Wij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; distance represents the Euclidean distance between the ith point and the jth point; sigma is a constant not equal to 0, representing the adjustment parameter; and i is more than or equal to 1 and less than or equal to M, j is more than or equal to 1 and less than or equal to M, i, j, M is an integer, and M is the total number of points included in the target sub-point cloud.
4. The method of claim 1, wherein the generating a diagonal matrix comprises:
determining a reference point cloud of the target sub-point cloud in the reference frame;
generating the diagonal matrix based on Euclidean distance between each point in the target sub-point cloud and the corresponding point of each point in the reference point cloud;
the value on the ith diagonal of the diagonal matrix is the inverse of the Euclidean distance between the ith point and a point p, wherein the point p is the corresponding point of the ith point in the reference point cloud.
5. The method of claim 4, wherein the determining the reference point cloud of the target sub-point cloud in the reference frame comprises:
and determining a reference point cloud of the target sub-point cloud in the reference frame by using an iterative nearest point algorithm.
6. The method of claim 1, wherein performing inter-prediction and graph fourier residual transforms on the plurality of sub-point clouds using the generalized laplacian matrix, respectively, comprises:
acquiring an attribute predicted value of the reference frame on the target attribute of the current frame;
generating a residual error of the target attribute of the current frame according to the attribute value of the target attribute of the current frame and the attribute predicted value of the target attribute of the reference frame to the current frame;
and transforming the residual error of the target attribute of the current frame based on the generalized Laplace matrix.
7. The method of claim 6, wherein the obtaining the attribute prediction value of the target attribute of the reference frame to the current frame comprises:
obtaining an attribute predicted value of the target attribute of the reference frame to the current frame according to the following formula:
wherein ,
an attribute predictor representing a target attribute of the reference frame to the current frame,x t-1 attribute values representing target attributes of the reference frame, lg represents a generalized Laplacian matrix.
8. The method of claim 6, wherein generating the residual of the target attribute of the current frame based on the attribute value of the target attribute of the current frame and the attribute prediction value of the target attribute of the reference frame to the target attribute of the current frame, comprises:
and using the difference between the attribute value of the target attribute of the current frame and the attribute predicted value of the target attribute of the reference frame to the current frame as the residual error.
9. The method of claim 6, wherein transforming the residual of the target property of the current frame based on the generalized laplacian matrix comprises:
obtaining a transformation matrix by using the generalized Laplace matrix;
and transforming the residual error of the target attribute of the current frame by using the transformation matrix.
10. The method of claim 6, wherein the deriving a transformation matrix using the generalized laplacian matrix comprises:
solving the following formula to obtain a transformation matrix:
wherein Lg represents a generalized Laplace matrix,
representing the transformation matrix.
11. The method of claim 9, wherein transforming the residual of the target property of the current frame using the transformation matrix comprises:
the transformation result is obtained using the following formula:
wherein, theta represents the transformation result,
representing a transformation matrix, delta representing the residual of the target attribute of the current frame.
12. A decoding method applied to a decoding device, the method comprising:
acquiring a coded code stream;
performing graph Fourier inverse transformation based on Euclidean distance weight on the coded code stream to obtain a transformation result;
based on the transformation result, obtaining a decoded code stream;
the coding code stream is obtained by coding a result of inter-frame prediction and graph Fourier residual transformation of the sub-point cloud by using a generalized Laplace matrix by coding equipment;
the generalized Laplace matrix is generated by the following steps:
obtaining a weight matrix according to Euclidean distances among a plurality of point pairs in the target sub-point cloud;
obtaining a Laplace matrix according to the degree matrix and the weight matrix;
generating a diagonal matrix;
obtaining the generalized Laplace matrix according to the diagonal matrix and the Laplace matrix;
wherein, the difference between the degree matrix and the weight matrix is used as a Laplacian matrix;
using the sum of the diagonal matrix and the Laplace matrix as the generalized Laplace matrix;
wherein the diagonal element d of the degree matrix i ＝∑ j W ij, wherein ,di The ith diagonal element of the degree matrix, W ij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.M, i, j, M being an integer, M being the total number of points included in the target sub-point cloud;
the diagonal matrix is generated from Euclidean distances between target points in the target sub-point cloud and corresponding points of the target points.
13. The method of claim 12, wherein performing an inverse fourier transform on the encoded code stream based on euclidean distance weights to obtain a transform result comprises:
performing inverse quantization on the coded code stream;
and carrying out graph Fourier inverse transformation based on Euclidean distance weight on the code stream after inverse quantization to obtain a transformation result.
14. The method of claim 13, wherein performing an inverse fourier transform on the encoded code stream based on euclidean distance weights to obtain a transform result comprises:
performing graph Fourier transform based on Euclidean distance weight on the code stream after inverse quantization by using the following formula:
wherein ,
representing the inverse transform residual value,/->
Representing a transformation matrix->
The quantized residual value representing the target property of the current frame, epsilon represents the dequantization coefficient.
15. An encoding device, comprising:
the first acquisition module is used for clustering the point cloud data to be processed of the current frame to obtain a plurality of sub point clouds;
the first generation module is used for generating a generalized Laplace matrix for any target sub-point cloud in the multiple sub-point clouds according to Euclidean distances between multiple point pairs in the target sub-point cloud and Euclidean distances between a target point in the target sub-point cloud and a corresponding point of the target point;
the first transformation module is used for carrying out inter-frame prediction and graph Fourier residual transformation on the target sub-point cloud by utilizing the generalized Laplace matrix;
the first coding module is used for respectively quantizing and coding the transformed multiple sub-point clouds to obtain a coded code stream;
the corresponding point is located in a reference point cloud of the target sub-point cloud, and the reference point cloud is located in a reference frame of the current frame;
wherein the first generation module comprises:
the first acquisition sub-module is used for obtaining a weight matrix according to Euclidean distances among a plurality of point pairs in the target sub-point cloud; the second acquisition sub-module is used for obtaining a Laplace matrix according to the degree matrix and the weight matrix; the first generation submodule is used for generating a diagonal matrix; the second generation submodule is used for obtaining the generalized Laplace matrix according to the diagonal matrix and the Laplace matrix;
the second obtaining submodule is used for using the difference between the degree matrix and the weight matrix as a Laplacian matrix; the second generating submodule is used for utilizing the sum of the diagonal matrix and the Laplace matrix as the generalized Laplace matrix;
wherein the diagonal element d of the degree matrix i ＝∑ j W ij, wherein ,di The ith diagonal element of the degree matrix, W ij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.M, i, j, M being an integer, M being the total number of points included in the target sub-point cloud;
the diagonal matrix is generated from Euclidean distances between target points in the target sub-point cloud and corresponding points of the target points.
16. A decoding apparatus, comprising:
the first acquisition module is used for acquiring the coded code stream;
the first transformation module is used for carrying out graph Fourier inverse transformation based on Euclidean distance weight on the coded code stream to obtain a transformation result;
the first decoding module is used for obtaining a decoding code stream based on the transformation result;
the coding code stream is obtained by coding a result of inter-frame prediction and graph Fourier residual transformation of the sub-point cloud by using a generalized Laplace matrix by coding equipment;
the generalized Laplace matrix is generated by the following steps:
obtaining a weight matrix according to Euclidean distances among a plurality of point pairs in the target sub-point cloud;
obtaining a Laplace matrix according to the degree matrix and the weight matrix;
generating a diagonal matrix;
obtaining the generalized Laplace matrix according to the diagonal matrix and the Laplace matrix;
wherein, the difference between the degree matrix and the weight matrix is used as a Laplacian matrix;
using the sum of the diagonal matrix and the Laplace matrix as the generalized Laplace matrix;
wherein the diagonal element d of the degree matrix i ＝∑ j W ij, wherein ,di Ith pair of representation degree matrixAngle line element, W ij Representing the weight corresponding to the edge from the ith point to the jth point in the target sub-point cloud; 1.ltoreq.i.ltoreq.M, 1.ltoreq.j.ltoreq.M, i, j, M being an integer, M being the total number of points included in the target sub-point cloud;
the diagonal matrix is generated from Euclidean distances between target points in the target sub-point cloud and corresponding points of the target points.
17. An electronic device, comprising: a memory, a processor, and a program stored on the memory and executable on the processor; it is characterized in that the method comprises the steps of,
the processor for reading a program in a memory to implement the steps in the encoding method according to any one of claims 1 to 11; or to implement the steps in the decoding method according to any one of claims 12 to 14.
18. A readable storage medium storing a program, wherein the program when executed by a processor implements the steps of the encoding method according to any one of claims 1 to 11; or to implement the steps in the decoding method according to any one of claims 12 to 14.
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| WO2023245982A1 (en) * | 2022-06-20 | 2023-12-28 | 北京大学深圳研究生院 | Point cloud compression method and apparatus, electronic device, and storage medium |
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