Decoding method and apparatus, encoding method and apparatus, and devices therefor

Abstract

The present application provides a decoding method and apparatus, an encoding method and apparatus, and devices therefor. The decoding method comprises: decoding a bitstream corresponding to a current image block to obtain a coefficient hyperparameter feature; on the basis of the coefficient hyperparameter feature, determining a probability distribution parameter, and on the basis of the probability distribution parameter, decoding a bitstream corresponding to the current image block to obtain an initial reconstruction feature; on the basis of the initial reconstruction feature, determining an initial reconstructed image block corresponding to the current image block, wherein the initial reconstructed image block comprises an initial luminance reconstructed image block and an initial chrominance reconstructed image block; and performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block. The technical solution of the present application can improve encoding performance and decoding performance.

Description

A decoding and encoding method, device and equipment thereof Technical Field

[0001] The present application relates to the field of coding and decoding technology, and in particular to a decoding and encoding method, device and equipment thereof. Background Art

[0002] To save space, video images are encoded before transmission. Complete video encoding involves prediction, transformation, quantization, entropy coding, and filtering. Prediction can include intra-frame prediction and inter-frame prediction. Inter-frame prediction leverages temporal correlations in the video to predict the current pixel using pixels from adjacent coded images, effectively reducing temporal redundancy. Intra-frame prediction leverages spatial correlations in the video to predict the current pixel using pixels from coded blocks in the current frame, reducing spatial redundancy.

[0003] With the rapid development of deep learning, it has achieved success in many high-level computer vision problems, such as image classification and object detection. Deep learning is also gradually being applied to codecs, enabling the use of neural networks to encode and decode images. Although neural network-based codecs demonstrate significant performance potential, they still suffer from issues such as poor encoding and decoding performance and high complexity.

[0004] Summary of the Invention

[0005] In view of this, the present application provides a decoding and encoding method, apparatus and device thereof to improve encoding and decoding performance.

[0006] In a first aspect, the present application provides an image decoding method, the method comprising: decoding a code stream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; and performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block.

[0007] In one possible implementation, after determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features, the method further includes: performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block; and generating a target reconstructed image block based on the target luminance reconstructed image block and the target chroma reconstructed image block.

[0008] In one possible implementation, performing brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block includes: decoding a code stream corresponding to the current image block to obtain image domain enhancement parameters corresponding to the current image block; and performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block.

[0009] In one possible implementation, the image domain enhancement parameters include multiple image enhancement segmentation intensity values ​​and multiple image enhancement segmentation thresholds, the multiple image enhancement segmentation thresholds constitute multiple image enhancement threshold intervals, and the multiple image enhancement threshold intervals correspond one-to-one to the multiple image enhancement segmentation intensity values; the brightness edge enhancement of the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain the target brightness reconstructed image block corresponding to the current image block includes: obtaining a target probability distribution channel map based on the probability distribution parameters; if the target probability distribution channel map includes multiple probability distribution values, then for each probability distribution value, determining the image enhancement segmentation intensity value corresponding to the probability distribution value based on the image enhancement threshold interval corresponding to the probability distribution value; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image enhancement segmentation intensity value corresponding to each probability distribution value to obtain the target brightness reconstructed image block corresponding to the current image block.

[0010] In one possible implementation, obtaining a target probability distribution channel map based on the probability distribution parameters includes: decoding the code stream corresponding to the current image block to obtain an important channel identifier corresponding to the current image block; if the probability distribution parameters include multiple probability distribution channel maps, selecting a probability distribution channel map corresponding to the important channel identifier as an important probability distribution channel map from the multiple probability distribution channel maps included in the probability distribution parameters; and upsampling the important probability distribution channel map to obtain a target probability distribution channel map.

[0011] In one possible implementation, the brightness edge enhancement is performed on the initial brightness reconstructed image block based on the image enhancement segmented intensity value corresponding to each probability distribution value to obtain the target brightness reconstructed image block corresponding to the current image block, including: generating a high-frequency detail image based on the initial brightness reconstructed image block; for each eigenvalue in the high-frequency detail image, performing brightness edge enhancement on the eigenvalue based on the image enhancement segmented intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an image enhancement eigenvalue; and determining the target brightness reconstructed image block based on the image enhancement eigenvalue corresponding to each eigenvalue.

[0012] In one possible implementation, generating a high-frequency detail image based on the initial brightness reconstructed image block includes: performing a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blur image; and subtracting the Gaussian blur image from the initial brightness reconstructed image block to obtain the high-frequency detail image; wherein, for each value in the Gaussian blur convolution kernel, the denominator of the value is a power of 2.

[0013] In one possible implementation, the Gaussian blur convolution kernel is an N*N convolution kernel, where N is a positive integer. If the Gaussian blur convolution kernel is a 3*3 convolution kernel, the Gaussian blur convolution kernel includes:

[0014] Where A is a positive integer, B is a positive integer, and M is a power of 2.

[0015] In a possible implementation, the Gaussian blur convolution kernel is:

[0016] In one possible implementation, performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block includes: performing adaptive upsampling based on the initial chroma reconstructed image block and the initial luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, where the upsampled chroma reconstructed image block has the same resolution as the initial luminance reconstructed image block; and performing a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

[0017] In a possible implementation, the filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block includes: performing cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and performing nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and performing cross-channel filtering on the nonlinear chroma filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0018] In one possible implementation, performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block includes: performing adaptive upsampling based on the initial chroma reconstructed image block and the target luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, where the upsampled chroma reconstructed image block has the same resolution as the target luminance reconstructed image block; and performing a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

[0019] In a possible implementation, the filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block includes: performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and performing nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and performing cross-channel filtering on the nonlinear chroma filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0020] In a possible implementation, the cross-channel filtering is performed on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block, including: performing wavelet transform on the upsampled chroma reconstructed image block and the initial luminance reconstructed image block; connecting the upsampled chroma reconstructed image block after wavelet transform and the initial luminance reconstructed image block after wavelet transform in the channel dimension; inputting the channel dimension connection features into the residual block network, performing inverse wavelet transform on the features output by the residual block network, and obtaining the cross-channel filtered chroma reconstructed image block. reconstructing an image block; performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, including: performing wavelet transform on the upsampled chroma reconstructed image block, and performing wavelet transform on the target luminance reconstructed image block; connecting the upsampled chroma reconstructed image block after wavelet transform and the target luminance reconstructed image block after wavelet transform in the channel dimension; inputting the features after the channel dimension connection into the residual block network, and performing inverse wavelet transform on the features output by the residual block network to obtain the cross-channel filtered chroma reconstructed image block.

[0021] In one possible implementation, determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features includes: inputting the initial reconstruction features into a synthesis transformation network to obtain the initial reconstructed image block; or decoding the code stream corresponding to the current image block to obtain feature domain enhancement parameters corresponding to the current image block; performing feature domain enhancement on the initial reconstruction features based on the feature domain enhancement parameters and the probability distribution parameters to obtain enhanced reconstruction features; and inputting the enhanced reconstruction features into a synthesis transformation network to obtain the initial reconstructed image block.

[0022] In a possible implementation, the method further includes: decoding the code stream corresponding to the current image block to obtain an important channel identifier corresponding to the current image block; wherein the initial reconstruction feature includes C feature channel maps, the probability distribution parameters include C probability distribution channel maps, the C probability distribution channel maps correspond to the C feature channel maps one-to-one, C is a positive integer, the feature channel map corresponding to the important channel identifier is used as the important feature channel map, the remaining feature channel maps in the C feature channel maps are used as non-important feature channel maps, the probability distribution channel map corresponding to the important feature channel map is used as the important probability distribution channel map, and the remaining feature channel maps in the C probability distribution channel maps are used as the non-important feature channel maps. The probability distribution channel map is used as a non-important probability distribution channel map; the initial reconstruction feature is enhanced in the feature domain based on the feature domain enhancement parameters and the probability distribution parameters to obtain an enhanced reconstruction feature, including: performing feature-adaptive edge enhancement on the important feature channel map based on the feature domain enhancement parameters and the important probability distribution channel map to obtain a first reconstruction feature after feature-adaptive edge enhancement; performing feature-adaptive stretching on the non-important feature channel map based on the feature domain enhancement parameters and the non-important probability distribution channel map to obtain a second reconstruction feature after feature-adaptive stretching; and generating the enhanced reconstruction feature based on the first reconstruction feature and the second reconstruction feature.

[0023] In a second aspect, the present application provides an image coding method, the method comprising: encoding coefficient hyperparameter features corresponding to a current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and encoding initial image features corresponding to the current image block based on the probability distribution parameters; determining an initial reconstructed image block corresponding to the current image block based on initial reconstruction features corresponding to the initial image features; wherein the initial reconstructed image block comprises an initial luminance reconstructed image block and an initial chrominance reconstructed image block; for each candidate image domain enhancement parameter, performing luminance edge enhancement on the initial luminance reconstructed image block based on the candidate image domain enhancement parameter and the probability distribution parameters to obtain a target luminance reconstructed image block; determining a cost value corresponding to the candidate image domain enhancement parameter based on the target luminance reconstructed image block; selecting an image domain enhancement parameter corresponding to the current image block from all candidate image domain enhancement parameters based on the cost value corresponding to each candidate image domain enhancement parameter, and encoding the image domain enhancement parameter.

[0024] In a third aspect, the present application provides an image decoding device, comprising: a decoding module for decoding a code stream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; a determination module for determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; and an enhancement module for performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block.

[0025] In a fourth aspect, the present application provides an image encoding device, which includes: an encoding module for encoding coefficient hyperparameter features corresponding to a current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and encoding the initial image features corresponding to the current image block based on the probability distribution parameters; a determination module for determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction features corresponding to the initial image features; the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; an enhancement module for performing luminance edge enhancement on the initial luminance reconstructed image block based on the candidate image domain enhancement parameter and the probability distribution parameters for each candidate image domain enhancement parameter to obtain a target luminance reconstructed image block; the determination module is also used to determine a cost value corresponding to the candidate image domain enhancement parameter based on the target luminance reconstructed image block; and selecting an image domain enhancement parameter corresponding to the current image block from all candidate image domain enhancement parameters based on the cost value corresponding to each candidate image domain enhancement parameter; and the encoding module is also used to encode the image domain enhancement parameter.

[0026] In a fifth aspect, the present application provides an image decoding method, the method comprising: decoding a code stream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial brightness reconstructed image block; obtaining image domain enhancement parameters corresponding to the current image block; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block; and generating a target reconstructed image block corresponding to the current image block based on the target brightness reconstructed image block.

[0027] In one possible implementation, the image domain enhancement parameters include multiple image enhancement segmentation intensity values ​​and multiple image enhancement segmentation thresholds, the multiple image enhancement segmentation thresholds constitute multiple image enhancement threshold intervals, and the multiple image enhancement threshold intervals correspond one-to-one to the multiple image enhancement segmentation intensity values; the brightness edge enhancement of the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain the target brightness reconstructed image block corresponding to the current image block includes: obtaining a target probability distribution channel map based on the probability distribution parameters; if the target probability distribution channel map includes multiple probability distribution values, then for each probability distribution value, determining the image enhancement segmentation intensity value corresponding to the probability distribution value based on the image enhancement threshold interval corresponding to the probability distribution value; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image enhancement segmentation intensity value corresponding to each probability distribution value to obtain the target brightness reconstructed image block corresponding to the current image block.

[0028] In one possible implementation, obtaining a target probability distribution channel map based on the probability distribution parameters includes: decoding a code stream corresponding to a current image block to obtain an important channel identifier corresponding to the current image block; if the probability distribution parameters include multiple probability distribution channel maps, selecting a probability distribution channel map corresponding to the important channel identifier as an important probability distribution channel map from the multiple probability distribution channel maps of the probability distribution parameters; and upsampling the important probability distribution channel map to obtain the target probability distribution channel map.

[0029] In one possible implementation, the brightness edge enhancement is performed on the initial brightness reconstructed image block based on the image enhancement segmented intensity value corresponding to each probability distribution value to obtain the target brightness reconstructed image block corresponding to the current image block, including: generating a high-frequency detail image based on the initial brightness reconstructed image block; for each eigenvalue in the high-frequency detail image, performing brightness edge enhancement on the eigenvalue based on the image enhancement segmented intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an image enhancement eigenvalue; and determining the target brightness reconstructed image block based on the image enhancement eigenvalue corresponding to each eigenvalue.

[0030] In one possible implementation, generating a high-frequency detail image based on the initial brightness reconstructed image block includes: performing a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blur image; and subtracting the Gaussian blur image from the initial brightness reconstructed image block to obtain the high-frequency detail image; wherein, for each value in the Gaussian blur convolution kernel, the denominator of the value is a power of 2.

[0031] In a sixth aspect, the present application provides an image encoding method, the method comprising: encoding coefficient hyperparameter features corresponding to a current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and encoding initial image features corresponding to the current image block based on the probability distribution parameters; determining an initial reconstructed image block corresponding to the current image block based on initial reconstruction features corresponding to the initial image features; wherein the initial reconstructed image block includes an initial brightness reconstructed image block; obtaining image domain enhancement parameters corresponding to the current image block; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block; and generating a target reconstructed image block corresponding to the current image block based on the target brightness reconstructed image block.

[0032] In a seventh aspect, the present application provides an image decoding device, comprising: a decoding module for decoding a code stream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; a determination module for determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial brightness reconstructed image block; an enhancement module for obtaining image domain enhancement parameters corresponding to the current image block; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block; and a generation module for generating a target reconstructed image block corresponding to the current image block based on the target brightness reconstructed image block.

[0033] In an eighth aspect, the present application provides an image encoding device, comprising: an encoding module for encoding coefficient hyperparameter features corresponding to a current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and encoding the initial image features corresponding to the current image block based on the probability distribution parameters; a determination module for determining an initial reconstructed image block corresponding to the current image block based on initial reconstruction features corresponding to the initial image features; the initial reconstructed image block includes an initial brightness reconstructed image block; an enhancement module for obtaining image domain enhancement parameters corresponding to the current image block; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block; and a generation module for generating a target reconstructed image block corresponding to the current image block based on the target brightness reconstructed image block.

[0034] In a ninth aspect, the present application provides an image decoding method, the method comprising: decoding a code stream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block comprises an initial luminance reconstructed image block and an initial chrominance reconstructed image block; performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block; performing chroma filtering enhancement on the initial chrominance reconstructed image block to obtain a target chrominance reconstructed image block; and generating a target reconstructed image block based on the target luminance reconstructed image block and the target chrominance reconstructed image block.

[0035] In one possible implementation, performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block includes: performing adaptive upsampling based on the initial chroma reconstructed image block and the initial luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, where the upsampled chroma reconstructed image block has the same resolution as the initial luminance reconstructed image block; and performing a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

[0036] In a possible implementation, the filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block includes: performing cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and performing nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and performing cross-channel filtering on the nonlinear chroma filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0037] In one possible implementation, performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block includes: performing adaptive upsampling based on the initial chroma reconstructed image block and the target luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, where the upsampled chroma reconstructed image block has the same resolution as the target luminance reconstructed image block; and performing a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

[0038] In a possible implementation, the filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block includes: performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and performing nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or performing nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and performing cross-channel filtering on the nonlinear chroma filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0039] In a possible implementation, the cross-channel filtering is performed on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block, including: performing wavelet transform on the upsampled chroma reconstructed image block and the initial luminance reconstructed image block; connecting the upsampled chroma reconstructed image block after wavelet transform and the initial luminance reconstructed image block after wavelet transform in the channel dimension; inputting the channel dimension connection features into the residual block network, performing inverse wavelet transform on the features output by the residual block network, and obtaining the cross-channel filtered chroma reconstructed image block. reconstructing an image block; performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, including: performing wavelet transform on the upsampled chroma reconstructed image block, and performing wavelet transform on the target luminance reconstructed image block; connecting the upsampled chroma reconstructed image block after wavelet transform and the target luminance reconstructed image block after wavelet transform in the channel dimension; inputting the features after the channel dimension connection into the residual block network, and performing inverse wavelet transform on the features output by the residual block network to obtain the cross-channel filtered chroma reconstructed image block.

[0040] In a tenth aspect, the present application provides an image coding method, the method comprising: encoding coefficient hyperparameter features corresponding to a current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and encoding initial image features corresponding to the current image block based on the probability distribution parameters; determining an initial reconstructed image block corresponding to the current image block based on initial reconstruction features corresponding to the initial image features; wherein the initial reconstructed image block comprises an initial luminance reconstructed image block and an initial chrominance reconstructed image block; performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block; performing chroma filtering enhancement on the initial chrominance reconstructed image block to obtain a target chrominance reconstructed image block; and generating a target reconstructed image block based on the target luminance reconstructed image block and the target chrominance reconstructed image block.

[0041] In the eleventh aspect, the present application provides an image decoding device, which includes: a decoding module, used to decode the code stream corresponding to the current image block to obtain the coefficient hyperparameter features corresponding to the current image block; determine the probability distribution parameters based on the coefficient hyperparameter features, and decode the code stream corresponding to the current image block based on the probability distribution parameters to obtain the initial reconstruction features corresponding to the current image block; a determination module, used to determine the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; an enhancement module, used to perform luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block; perform chroma filtering enhancement on the initial chrominance reconstructed image block to obtain a target chrominance reconstructed image block; and a generation module, used to generate a target reconstructed image block based on the target luminance reconstructed image block and the target chrominance reconstructed image block.

[0042] In a twelfth aspect, the present application provides an image encoding device, which includes: an encoding module for encoding coefficient hyperparameter features corresponding to a current image block; determining probability distribution parameters based on the coefficient hyperparameter features, and encoding the initial image features corresponding to the current image block based on the probability distribution parameters; a determination module for determining an initial reconstructed image block corresponding to the current image block based on an initial reconstruction feature corresponding to the initial image feature; the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; an enhancement module for performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block; performing chroma filtering enhancement on the initial chrominance reconstructed image block to obtain a target chrominance reconstructed image block; and a generation module for generating a target reconstructed image block based on the target luminance reconstructed image block and the target chrominance reconstructed image block.

[0043] In a thirteenth aspect, the present application provides an image decoding method, the method comprising: determining an initial reconstructed image block corresponding to a current image block; wherein the initial reconstructed image block comprises an initial brightness reconstructed image block; performing a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blurred image; wherein the Gaussian blur convolution kernel is an N*N convolution kernel, and if the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel comprises: Wherein, A is a positive integer, B is a positive integer, and M is a power of 2; the Gaussian blurred image is subtracted from the initial brightness reconstructed image block to obtain a high-frequency detail image; a target brightness reconstructed image block corresponding to the current image block is determined based on the high-frequency detail image; and a target reconstructed image block corresponding to the current image block is generated based on the target brightness reconstructed image block.

[0044] In a possible implementation, the Gaussian blur convolution kernel is:

[0045] In one possible implementation, determining the target brightness reconstructed image block corresponding to the current image block based on the high-frequency detail image includes: obtaining image domain enhancement parameters corresponding to the current image block; performing brightness edge enhancement on the high-frequency detail image based on the image domain enhancement parameters and the probability distribution parameters corresponding to the current image block to obtain the target brightness reconstructed image block corresponding to the current image block.

[0046] In one possible implementation, the image domain enhancement parameters include multiple image enhancement segmentation intensity values ​​and multiple image enhancement segmentation thresholds, the multiple image enhancement segmentation thresholds constitute multiple image enhancement threshold intervals, and the multiple image enhancement threshold intervals correspond one-to-one to the multiple image enhancement segmentation intensity values; the brightness edge enhancement of the high-frequency detail image based on the image domain enhancement parameters and the probability distribution parameters corresponding to the current image block to obtain the target brightness reconstructed image block corresponding to the current image block includes: obtaining a target probability distribution channel map based on the probability distribution parameters; if the target probability distribution channel map includes multiple probability distribution values, then for each probability distribution value, determining the image enhancement segmentation intensity value corresponding to the probability distribution value based on the image enhancement threshold interval corresponding to the probability distribution value; for each eigenvalue in the high-frequency detail image, performing brightness edge enhancement on the eigenvalue based on the image enhancement segmentation intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an image enhancement eigenvalue; and determining the target brightness reconstructed image block based on the image enhancement eigenvalue corresponding to each eigenvalue.

[0047] In one possible implementation, obtaining a target probability distribution channel map based on the probability distribution parameters includes: decoding a code stream corresponding to a current image block to obtain an important channel identifier corresponding to the current image block; if the probability distribution parameters include multiple probability distribution channel maps, selecting a probability distribution channel map corresponding to the important channel identifier as an important probability distribution channel map from the multiple probability distribution channel maps of the probability distribution parameters; and upsampling the important probability distribution channel map to obtain the target probability distribution channel map.

[0048] In one possible implementation, determining the initial reconstructed image block corresponding to the current image block includes: decoding the code stream corresponding to the current image block to obtain coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; and determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features.

[0049] In a fourteenth aspect, the present application provides an image encoding method, the method comprising: determining an initial reconstructed image block corresponding to a current image block; wherein the initial reconstructed image block comprises an initial brightness reconstructed image block; performing a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blurred image; wherein the Gaussian blur convolution kernel is an N*N convolution kernel, and if the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel comprises: Wherein, A is a positive integer, B is a positive integer, and M is a power of 2; the Gaussian blurred image is subtracted from the initial brightness reconstructed image block to obtain a high-frequency detail image; a target brightness reconstructed image block corresponding to the current image block is determined based on the high-frequency detail image; and a target reconstructed image block corresponding to the current image block is generated based on the target brightness reconstructed image block.

[0050] In a fifteenth aspect, the present application provides an image decoding device, comprising: a determination module for determining an initial reconstructed image block corresponding to a current image block; wherein the initial reconstructed image block includes an initial brightness reconstructed image block; performing a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blurred image; wherein the Gaussian blur convolution kernel is an N*N convolution kernel, and if the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel includes: Wherein, A is a positive integer, B is a positive integer, and M is a power of 2; the Gaussian blurred image is subtracted from the initial brightness reconstructed image block to obtain a high-frequency detail image; a target brightness reconstructed image block corresponding to the current image block is determined based on the high-frequency detail image; and a generation module is used to generate a target reconstructed image block corresponding to the current image block based on the target brightness reconstructed image block.

[0051] In a sixteenth aspect, the present application provides an image encoding device, comprising: a determination module for determining an initial reconstructed image block corresponding to a current image block; wherein the initial reconstructed image block includes an initial brightness reconstructed image block; performing a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blurred image; wherein the Gaussian blur convolution kernel is an N*N convolution kernel, and if the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel includes: Wherein, A is a positive integer, B is a positive integer, and M is a power of 2; the Gaussian blurred image is subtracted from the initial brightness reconstructed image block to obtain a high-frequency detail image; a target brightness reconstructed image block corresponding to the current image block is determined based on the high-frequency detail image; and a generation module is used to generate a target reconstructed image block corresponding to the current image block based on the target brightness reconstructed image block.

[0052] In the seventeenth aspect, the present application provides an image decoding device, which includes: a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the image decoding method of any of the above aspects.

[0053] In aspect 18, the present application provides an image encoding device, wherein the image decoding device comprises: a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the image encoding method of any of the above aspects.

[0054] In the nineteenth aspect, the present application provides an electronic device comprising: a processor and a machine-readable storage medium, the machine-readable storage medium storing machine-executable instructions that can be executed by the processor; the processor is used to execute the machine-executable instructions to implement the image decoding method of any of the above aspects; or, the processor is used to execute the machine-executable instructions to implement the image encoding method of any of the above aspects.

[0055] In the twentieth aspect, the present application provides a machine-readable storage medium, on which a number of computer instructions are stored. When the computer instructions are executed by a processor, the image decoding method of any of the above aspects is implemented; or, the image encoding method of any of the above aspects is implemented.

[0056] In a twenty-first aspect, the present application provides a computer program, which is configured to, when executed by a processor having a memory, implement the image decoding method of any of the above aspects; or implement the image encoding method of any of the above aspects.

[0057] In aspect 22, the present application provides a computer program product comprising computer program instructions, wherein the computer program instructions are configured to, when executed by a processor having a memory, implement the image decoding method of any of the above aspects; or, implement the image encoding method of any of the above aspects.

[0058] It can be seen from the above technical solutions that in the embodiment of the present application, an end-to-end video image compression method is proposed, which can realize the encoding and decoding of video images based on a neural network, and achieve the purpose of improving the encoding efficiency and decoding efficiency by combining image domain enhancement parameters and probability distribution parameters. Combined with the network structure design and the header information code stream (such as the third code stream), the neural network can effectively ensure the quality of the reconstructed image block while maintaining low complexity, thereby achieving the purpose of improving the encoding performance and decoding performance and reducing the complexity. The image quality of the features is enhanced using the image domain enhancement parameters and the probability distribution parameters. The encoding end does not directly change the feature information, but encodes the image domain enhancement parameters into the header information code stream. The decoding end enhances the reconstructed image block through the image domain enhancement parameters, thereby improving the encoding and decoding performance and improving the quality of the reconstructed image. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] FIG1 is a schematic diagram of a three-dimensional feature matrix in one embodiment of the present application;

[0060] FIG2 is a schematic flow chart of a decoding method in one embodiment of the present application;

[0061] FIG3 is a schematic flow chart of an encoding method in one embodiment of the present application;

[0062] FIG4 is a schematic diagram of a processing process of an encoding end in one embodiment of the present application;

[0063] FIG5 is a schematic diagram of a processing process of a decoding end in one embodiment of the present application;

[0064] FIG6A is a schematic diagram showing the position of a feature domain enhancement module in one embodiment of the present application;

[0065] FIG6B is a schematic diagram showing the positions of a feature domain enhancement module and an image domain enhancement module in one embodiment of the present application;

[0066] FIG6C is a schematic diagram showing the position of an image domain enhancement module in one embodiment of the present application;

[0067] FIG6D is a schematic diagram of cross-channel filtering in one embodiment of the present application;

[0068] FIG6E is a schematic diagram of nonlinear chroma filtering in one embodiment of the present application;

[0069] FIG7A is a hardware structure diagram of a decoding end device in one embodiment of the present application;

[0070] FIG7B is a hardware structure diagram of an encoding terminal device in one embodiment of the present application. DETAILED DESCRIPTION

[0071] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, rather than for limiting the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application and the claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to any or all possible combinations of one or more associated listed items. It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, these information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information, depending on the context. In addition, the word "if" used can be interpreted as "at the time of...", or "when...", or "in response to determination".

[0072] In the embodiments of the present application, a decoding method and an encoding method are proposed, which may involve the following concepts:

[0073] Entropy encoding: Entropy encoding is a method of encoding that does not lose any information during the encoding process according to the entropy principle. Information entropy is the average amount of information in the source (a measure of uncertainty). Entropy coding methods include, but are not limited to, Shannon coding, Huffman coding, and arithmetic coding.

[0074] Neural Network (NN): A neural network refers to an artificial neural network. A NN is a computational model composed of a large number of interconnected nodes (or neurons). In a NN, neuronal processing units can represent different objects, such as features, letters, concepts, or some meaningful abstract patterns. Processing units in a NN can be divided into three categories: input units, output units, and hidden units. Input units receive signals and data from the external world; output units output the processed results; and hidden units are located between the input and output units and cannot be observed from outside the system. The connection weights between neurons reflect the strength of the connections between units, and the representation and processing of information is reflected in the connections between processing units. A NN is a non-programmed, brain-like information processing method. Its essence is to achieve parallel and distributed information processing capabilities through the transformations and dynamics of the neural network, mimicking the information processing capabilities of the human brain to varying degrees and levels. In the field of video processing, commonly used NNs include, but are not limited to, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully-connected neural networks (FCNNs).

[0075] Convolutional Neural Network (CNN): A convolutional neural network (CNN) is a feedforward neural network and one of the most representative network structures in deep learning technology. Its artificial neurons can respond to surrounding cells within a certain coverage area, making it an excellent choice for processing large images. The basic structure of a CNN consists of two layers. The first is the feature extraction layer (also called the convolution layer). Each neuron's input is connected to the local receptive field of the previous layer, extracting local features. Once a local feature is extracted, its positional relationship with other features is determined. The second is the feature mapping layer (also called the activation layer). Each computational layer of the neural network consists of multiple feature maps. Each feature map is a plane where all neurons have equal weights. The feature map structure can use Sigmoid, ReLU, Leaky-ReLU, PReLU, GDN, and other activation functions. Furthermore, since neurons within a mapping plane share weights, the number of free parameters in the network is reduced.

[0076] For example, one of the advantages of convolutional neural networks over image processing algorithms is that they avoid complex pre-processing of images (such as extracting artificial features) and can directly input the original image for end-to-end learning. One of the advantages of convolutional neural networks over ordinary neural networks is that ordinary neural networks use a fully connected approach, that is, all neurons from the input layer to the hidden layer are connected. This will result in a huge number of parameters, making network training time-consuming or even difficult to train. Convolutional neural networks avoid this difficulty through methods such as local connections and weight sharing.

[0077] Deconvolution layer: Deconvolution layer is also called transposed convolution layer. The working process of deconvolution layer and convolution layer is very similar. The main difference is that deconvolution layer uses padding to make the output larger than the input (of course, it can remain the same). If the stride is 1, it means that the output size is equal to the input size. If the stride is N, it means that the width of the output feature is N times the width of the input feature, and the height of the output feature is N times the height of the input feature.

[0078] Generalization Ability: Generalization ability refers to the ability of a machine learning algorithm to adapt to new samples. The purpose of learning is to learn the patterns implicit in the data pairs. For data outside the learning set with the same patterns, the trained network can also give appropriate outputs. This ability can be called generalization ability.

[0079] Feature: The features referred to in this application are three-dimensional feature matrices or tensors of the form C*W*H. Figure 1 shows a schematic diagram of a three-dimensional feature matrix. In this matrix, C represents the number of channels, H represents the feature height, and W represents the feature width. A three-dimensional feature matrix can be either the input or the output of a neural network.

[0080] Rate-Distortion Optimization (RDO) principle: Coding efficiency is evaluated by two main metrics: bit rate and Peak Signal to Noise Ratio (PSNR). A smaller bitrate results in a higher compression ratio, and a higher PSNR indicates better reconstructed image quality. When selecting a mode, the discriminant formula is essentially a comprehensive evaluation of these two metrics. For example, the cost of a mode is: J(mode) = D + λ*R, where D represents distortion. This is typically measured using the Sum of Squared Errors (SSE), which is the mean squared sum of the differences between the reconstructed image block and the source image. To account for this cost, the Sum of Absolute Differences (SAD) can also be used. SAD is the sum of the absolute differences between the reconstructed image block and the source image. λ is the Lagrange multiplier, and R is the actual number of bits required to encode the image block in that mode, including the bits required for coding mode information, motion information, and residuals. When selecting a mode, if the rate-distortion optimization principle is used to compare and decide on the encoding mode, the best encoding performance can usually be guaranteed.

[0081] Numerous coding tools have been proposed for various modules on the encoding side, and each tool often has multiple modes. Different coding tools often produce the best coding performance for different video sequences. Therefore, during the encoding process, Rate-Distortion Optimization (RDO) is often used to compare the coding performance of different tools or modes to select the optimal mode. After determining the optimal tool or mode, the decision-making information for the tool or mode is conveyed by encoding marker information in the bitstream. While this approach introduces higher coding complexity, it can adaptively select the optimal mode combination for different content, achieving optimal coding performance. The decoding side can obtain relevant mode information by directly parsing the marker information, with minimal complexity impact.

[0082] The general end-to-end image coding framework mainly consists of a feature main information component and a super-prior side information component. The feature main information component includes an analysis network, quantization, normal entropy coding, normal entropy decoding, and a synthesis network, while the super-prior side information component includes a super-prior analysis network, quantization, factored entropy coding, factored entropy decoding, and a super-prior synthesis network. The image component x is compressed and encoded by the analysis network and reconstructed and restored by the synthesis network of the feature main information component, respectively. The super-prior side information component is mainly used to model the probability of the feature main information and guide the entropy encoding and decoding of the feature main information. However, the general end-to-end image coding framework does not fully utilize the super-prior side information, and the image reconstruction quality is not fully improved.

[0083] In response to these findings, this embodiment leverages the characteristics of an end-to-end image coding framework. The decoder uses probability distribution parameters to enhance the quality of the reconstructed image. The encoder does not directly alter the primary information. Instead, it encodes the image domain enhancement parameters into the header bitstream (the third bitstream). The decoder then uses these image domain enhancement parameters to enhance the reconstructed image.

[0084] The following describes in detail the decoding method and encoding method in the embodiments of the present application in combination with several specific embodiments.

[0085] Example 1: A decoding method is proposed in the embodiment of the present application. See Figure 2, which is a flow chart of the decoding method. The method can be applied to a decoding end (also called a video decoder). The method may include the following steps 201-204.

[0086] Step 201: Decode the code stream corresponding to the current image block to obtain the coefficient hyperparameter features corresponding to the current image block.

[0087] Step 202: Determine probability distribution parameters based on the coefficient hyperparameter feature, and decode the code stream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block.

[0088] Step 203: Determine an initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature; wherein the initial reconstructed image block may include an initial luminance reconstructed image block and an initial chrominance reconstructed image block.

[0089] Step 204: Perform brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block.

[0090] Exemplarily, the coefficient hyperparameter feature may refer to a feature obtained by inputting the image feature into a hyperparameter encoding network (ie, a neural network) and performing a coefficient hyperparameter feature transformation on the image feature by the hyperparameter encoding network.

[0091] For example, after determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features, chroma filtering enhancement can be performed on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block. For example, the chroma filtering enhancement and the luminance edge enhancement are synchronized and parallel. The chroma filtering enhancement and the luminance edge enhancement can be performed simultaneously, or the luminance edge enhancement can be performed first and then the chroma filtering enhancement, or the chroma filtering enhancement can be performed first and then the luminance edge enhancement. There is no specific limitation on the order of luminance and chroma enhancement.

[0092] Exemplarily, after obtaining the target luminance reconstructed image block and the target chrominance reconstructed image block, a target reconstructed image block may be generated based on the target luminance reconstructed image block and the target chrominance reconstructed image block.

[0093] Exemplarily, in order to obtain the target chromaticity reconstructed image block, chromaticity filtering enhancement can be performed based on the initial luminance reconstructed image block and the initial chromaticity reconstructed image block to obtain the target chromaticity reconstructed image block, or chromaticity filtering enhancement can be performed based on the target luminance reconstructed image block and the initial chromaticity reconstructed image block to obtain the target chromaticity reconstructed image block.

[0094] Exemplarily, performing brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block may include but is not limited to: decoding the code stream corresponding to the current image block to obtain image domain enhancement parameters corresponding to the current image block; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block.

[0095] Exemplarily, the image domain enhancement parameters may include multiple image enhancement segmentation intensity values ​​and multiple image enhancement segmentation thresholds, the multiple image enhancement segmentation thresholds constitute multiple image enhancement threshold intervals, and the multiple image enhancement threshold intervals correspond one-to-one to the multiple image enhancement segmentation intensity values. Based on this, the initial brightness reconstructed image block is subjected to brightness edge enhancement based on the image domain enhancement parameters and the probability distribution parameters to obtain the target brightness reconstructed image block corresponding to the current image block, which may include but is not limited to: obtaining a target probability distribution channel map based on the probability distribution parameters; if the target probability distribution channel map includes multiple probability distribution values, then for each probability distribution value, determining the image enhancement segmentation intensity value corresponding to the probability distribution value based on the image enhancement threshold interval corresponding to the probability distribution value; performing brightness edge enhancement on the initial brightness reconstructed image block based on the image enhancement segmentation intensity value corresponding to each probability distribution value to obtain the target brightness reconstructed image block corresponding to the current image block.

[0096] Exemplarily, obtaining a target probability distribution channel map based on probability distribution parameters may include, but is not limited to: decoding the code stream corresponding to the current image block to obtain an important channel identifier corresponding to the current image block; if the probability distribution parameters include multiple probability distribution channel maps, selecting a probability distribution channel map corresponding to the important channel identifier from the multiple probability distribution channel maps as the important probability distribution channel map; and upsampling the important probability distribution channel map to obtain a target probability distribution channel map.

[0097] Exemplarily, performing brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block may include but is not limited to: decoding the code stream corresponding to the current image block to obtain an important channel identifier corresponding to the current image block; if the probability distribution parameters include multiple probability distribution channel maps, selecting a probability distribution channel map corresponding to the important channel identifier from the multiple probability distribution channel maps included in the probability distribution parameters as the important probability distribution channel map; upsampling the important probability distribution channel map to obtain a target probability distribution channel map; and performing brightness edge enhancement on the initial brightness reconstructed image block according to the target probability distribution channel map to obtain a target brightness reconstructed image block corresponding to the current image block.

[0098] Exemplarily, the initial brightness reconstructed image block is subjected to brightness edge enhancement based on the image enhancement segmented intensity value corresponding to each probability distribution value to obtain a target brightness reconstructed image block corresponding to the current image block, which may include but is not limited to: generating a high-frequency detail image based on the initial brightness reconstructed image block; for each eigenvalue in the high-frequency detail image, the eigenvalue is subjected to brightness edge enhancement based on the image enhancement segmented intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an image enhancement eigenvalue; and the target brightness reconstructed image block is determined based on the image enhancement eigenvalue corresponding to each eigenvalue.

[0099] For example, generating a high-frequency detail image based on the initial luminance reconstructed image block may include, but is not limited to: convolving the initial luminance reconstructed image block with a Gaussian blur convolution kernel to obtain a Gaussian blur image; and subtracting the Gaussian blur image from the initial luminance reconstructed image block to obtain a high-frequency detail image. For each value in the Gaussian blur convolution kernel, the denominator of the value may be a power of 2, such as 2, 4, 8, 16, 32, 64, 128, etc.

[0100] For example, the Gaussian blur convolution kernel may be an N*N convolution kernel, where N may be a positive integer. If the value of N is 3, that is, the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel may include but is not limited to:

[0101] A may be a positive integer, B may be a positive integer, and M may be a power of 2, such as 2, 4, 8, 16, 32, 64, or 128. For example, A may be 5, B may be 24, and M may be 64.

[0102] Exemplarily, for step 204, the initial chroma reconstructed image block is subjected to chroma filtering enhancement to obtain a target chroma reconstructed image block, which may include but is not limited to: the initial chroma reconstructed image block may be subjected to chroma filtering enhancement based on the target luminance reconstructed image block or the initial luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0103] For example, adaptive upsampling can be performed based on the initial chroma reconstructed image block and the initial luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, where the resolution of the upsampled chroma reconstructed image block and the initial luminance reconstructed image block can be the same. A filtering operation is performed on the upsampled chroma reconstructed image block to obtain a target chroma reconstructed image block; the filtering operation may include, but is not limited to, cross-channel filtering and / or nonlinear chroma filtering. For example, cross-channel filtering can be performed on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a target chroma reconstructed image block; or, nonlinear chroma filtering can be performed on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a target chroma reconstructed image block; or, cross-channel filtering can be performed on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and nonlinear chroma filtering can be performed on the cross-channel filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a target chroma reconstructed image block; or, nonlinear chroma filtering can be performed on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and cross-channel filtering can be performed on the nonlinear chroma filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a target chroma reconstructed image block.

[0104] For example, adaptive upsampling can be performed based on the initial chroma reconstructed image block and the target luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, where the resolution of the upsampled chroma reconstructed image block and the target luminance reconstructed image block can be the same. A filtering operation is performed on the upsampled chroma reconstructed image block to obtain a target chroma reconstructed image block; the filtering operation may include but is not limited to cross-channel filtering and / or nonlinear chroma filtering. For example, cross-channel filtering can be performed on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, nonlinear chroma filtering can be performed on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, cross-channel filtering can be performed on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block, and nonlinear chroma filtering can be performed on the cross-channel filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, nonlinear chroma filtering can be performed on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the nonlinear chroma filtered chroma reconstructed image block, and cross-channel filtering can be performed on the nonlinear chroma filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0105] Exemplarily, determining the initial reconstructed image block corresponding to the current image block based on the initial reconstructed features may include, but is not limited to: inputting the initial reconstructed features into a synthesis transformation network to obtain the initial reconstructed image block corresponding to the current image block. Alternatively, decoding the bitstream corresponding to the current image block to obtain feature domain enhancement parameters corresponding to the current image block; performing feature domain enhancement on the initial reconstructed features based on the feature domain enhancement parameters and probability distribution parameters to obtain enhanced reconstruction features; and inputting the enhanced reconstruction features into a synthesis transformation network to obtain the initial reconstructed image block corresponding to the current image block.

[0106] Exemplarily, the code stream corresponding to the current image block can also be decoded to obtain the important channel identifier corresponding to the current image block; wherein, the initial reconstructed features include C feature channel maps, the probability distribution parameters include C probability distribution channel maps, the C probability distribution channel maps correspond one-to-one to the C feature channel maps, C is a positive integer, the feature channel map corresponding to the important channel identifier is used as the important feature channel map, the remaining feature channel maps in the C feature channel maps are used as non-important feature channel maps, the probability distribution channel map corresponding to the important feature channel map is used as the important probability distribution channel map, and the remaining probability distribution channel maps in the C probability distribution channel maps are used as non-important probability distribution channel maps.

[0107] The initial reconstructed feature is enhanced in the feature domain based on the feature domain enhancement parameters and the probability distribution parameters to obtain an enhanced reconstructed feature, which may include but is not limited to: performing feature-adaptive edge enhancement on the important feature channel map based on the feature domain enhancement parameters and the important probability distribution channel map to obtain a first reconstructed feature after feature-adaptive edge enhancement; performing feature-adaptive stretching on the unimportant feature channel map based on the feature domain enhancement parameters and the unimportant probability distribution channel map to obtain a second reconstructed feature after feature-adaptive stretching; and generating an enhanced reconstructed feature based on the first reconstructed feature and the second reconstructed feature.

[0108] Exemplarily, the feature domain enhancement parameters may include multiple edge enhancement segmentation strength values ​​and multiple edge enhancement segmentation thresholds, the multiple edge enhancement segmentation thresholds may constitute multiple edge enhancement threshold intervals, and the multiple edge enhancement threshold intervals correspond one-to-one to the multiple edge enhancement segmentation strength values. On this basis, based on the feature domain enhancement parameters and the important probability distribution channel map, feature adaptive edge enhancement is performed on the important feature channel map to obtain the first reconstructed feature after feature adaptive edge enhancement, which may include but is not limited to: if the important probability distribution channel map includes multiple probability distribution values, then, for each probability distribution value, the edge enhancement segmentation strength value corresponding to the probability distribution value can be determined based on the edge enhancement threshold interval corresponding to the probability distribution value; then, based on the edge enhancement segmentation strength value corresponding to each probability distribution value, feature adaptive edge enhancement is performed on the important feature channel map to obtain the first reconstructed feature after feature adaptive edge enhancement.

[0109] Exemplarily, feature-adaptive edge enhancement is performed on the important feature channel map based on the edge enhancement segmentation intensity value corresponding to each probability distribution value to obtain the first reconstructed feature after feature-adaptive edge enhancement, which may include but is not limited to: normalizing the important feature channel map to obtain a normalized feature map; generating a high-frequency detail image based on the important feature channel map and the normalized feature map; for each eigenvalue in the high-frequency detail image, edge enhancement is performed on the eigenvalue based on the edge enhancement segmentation intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an edge enhanced eigenvalue; an edge enhancement feature map is determined based on the edge enhancement eigenvalue corresponding to each eigenvalue; and the edge enhancement feature map is denormalized to obtain the first reconstructed feature.

[0110] Exemplarily, the feature domain enhancement parameter may include a stretching parameter value, and the feature domain enhancement parameter and the non-important probability distribution channel map are subjected to feature adaptive stretching to obtain a second reconstructed feature after feature adaptive stretching, which may include but is not limited to: if the non-important feature channel map may include multiple eigenvalues, and the non-important probability distribution channel map may include multiple probability distribution values, then, for each eigenvalue in the non-important feature channel map, the stretched eigenvalue corresponding to the eigenvalue may be determined based on the eigenvalue, the stretching parameter value and the probability distribution value corresponding to the eigenvalue; then, the second reconstructed feature is determined based on the stretched eigenvalue corresponding to each eigenvalue in the non-important feature channel map.

[0111] Exemplarily, the code stream that can be decoded to obtain the coefficient hyperparameter features corresponding to the current image block can be called the first code stream corresponding to the current block, the code stream that can be decoded to obtain the initial reconstruction features corresponding to the current image block can be called the second code stream corresponding to the current block, and the code stream that can be decoded to obtain the image domain enhancement parameters and / or feature domain enhancement functions and / or important channel identifiers corresponding to the current image block can be called the third code stream corresponding to the current block. The third code stream corresponding to the current block can also be called the header information code stream corresponding to the current block.

[0112] For example, the above execution order is only for the convenience of describing the examples given. In actual applications, the execution order between the steps can also be changed, and this execution order is not limited. Moreover, in other embodiments, the steps of the corresponding method are not necessarily executed in the order shown and described in this specification, and the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0113] As can be seen from the above technical solutions, in the embodiment of the present application, after obtaining the initial luminance reconstructed image block and the initial chrominance reconstructed image block corresponding to the current image block, the initial luminance reconstructed image block can be subjected to luminance edge enhancement to obtain a target luminance reconstructed image block, the initial chrominance reconstructed image block can be subjected to chrominance filtering enhancement to obtain a target chrominance reconstructed image block, and the target reconstructed image block is generated based on the target luminance reconstructed image block and the target chrominance reconstructed image block, thereby proposing an end-to-end video image compression method, which can realize the encoding and decoding of video images based on a neural network, and achieve the purpose of improving the encoding efficiency and decoding efficiency by combining image domain enhancement parameters and probability distribution parameters. Combined with the network structure design and the header information code stream (such as the third code stream), the neural network effectively guarantees the quality of the reconstructed image block while maintaining low complexity, thereby achieving the purpose of improving the encoding performance and decoding performance, and reducing complexity. The image domain enhancement parameters and probability distribution parameters are used to enhance the image quality of the features. The encoding end does not directly change the feature information, but encodes the image domain enhancement parameters into the header information code stream. The decoding end enhances the reconstructed image block through the image domain enhancement parameters, thereby improving the encoding and decoding performance and improving the quality of the reconstructed image.

[0114] Example 2: A coding method is proposed in the embodiment of the present application. See Figure 3, which is a flow chart of the coding method. The method can be applied to the coding end (also called a video encoder), and the method can include steps 301-305.

[0115] Step 301: Encode the coefficient hyperparameter features corresponding to the current image block.

[0116] Step 302: Determine probability distribution parameters based on the coefficient hyperparameter feature, and encode initial image features corresponding to the current image block based on the probability distribution parameters.

[0117] Step 303: Determine an initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature corresponding to the initial image feature; wherein the initial reconstructed image block may include an initial luminance reconstructed image block and an initial chrominance reconstructed image block.

[0118] Step 304: For each candidate image domain enhancement parameter, perform brightness edge enhancement on the initial brightness reconstructed image block based on the candidate image domain enhancement parameter and the probability distribution parameter to obtain a target brightness reconstructed image block; and determine a cost value corresponding to the candidate image domain enhancement parameter based on the target brightness reconstructed image block.

[0119] Exemplarily, the initial chroma reconstructed image block can be subjected to chroma filtering enhancement to obtain a target chroma reconstructed image block, a target reconstructed image block is generated based on the target luminance reconstructed image block and the target chroma reconstructed image block, and the cost value corresponding to the candidate image domain enhancement parameter is determined based on the target reconstructed image block.

[0120] Step 305: Select an image domain enhancement parameter corresponding to the current image block from all candidate image domain enhancement parameters based on the cost value corresponding to each candidate image domain enhancement parameter, and encode the image domain enhancement parameter.

[0121] Exemplarily, the processing process of the encoding end is similar to the processing process of the decoding end, and the similarities are not repeated here. The processing process of the decoding end can be applied to the encoding end, that is, the encoding end adopts the same processing method.

[0122] Exemplarily, the code stream obtained by encoding the coefficient hyperparameter features corresponding to the current image block can be called the first code stream corresponding to the current image block, the code stream obtained by encoding the initial image features corresponding to the current image block can be called the second code stream corresponding to the current image block, and the code stream obtained by encoding the image domain enhancement parameters and / or feature domain enhancement functions and / or important channel identifiers corresponding to the current image block can be called the third code stream corresponding to the current image block. The third code stream corresponding to the current block can also be called the header information code stream corresponding to the current block.

[0123] For example, the above execution order is only for the convenience of describing the examples given. In actual applications, the execution order between the steps can also be changed, and this execution order is not limited. Moreover, in other embodiments, the steps of the corresponding method are not necessarily executed in the order shown and described in this specification, and the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0124] As can be seen from the above technical solutions, in the embodiment of the present application, after obtaining the initial luminance reconstructed image block and the initial chrominance reconstructed image block corresponding to the current image block, the initial luminance reconstructed image block can be subjected to luminance edge enhancement to obtain a target luminance reconstructed image block, the initial chrominance reconstructed image block can be subjected to chrominance filtering enhancement to obtain a target chrominance reconstructed image block, and the target reconstructed image block is generated based on the target luminance reconstructed image block and the target chrominance reconstructed image block, thereby proposing an end-to-end video image compression method, which can realize the encoding and decoding of video images based on a neural network, and achieve the purpose of improving the encoding efficiency and decoding efficiency by combining image domain enhancement parameters and probability distribution parameters. Combined with the network structure design and the header information code stream (such as the third code stream), the neural network effectively guarantees the quality of the reconstructed image block while maintaining low complexity, thereby achieving the purpose of improving the encoding performance and decoding performance, and reducing complexity. The image domain enhancement parameters and probability distribution parameters are used to enhance the image quality of the features. The encoding end does not directly change the feature information, but encodes the image domain enhancement parameters into the header information code stream. The decoding end enhances the reconstructed image block through the image domain enhancement parameters, thereby improving the encoding and decoding performance and improving the quality of the reconstructed image.

[0125] Example 3: For Example 1 and Example 2, the processing process of the encoding end can be seen in Figure 4. Of course, Figure 4 is only an example of the processing process of the encoding end and does not limit the processing process of this encoding end.

[0126] After obtaining the current image block x (which can be the original image block x, i.e., the input image block), the encoder can perform an analysis transformation on the current image block x using an analysis transformation network (i.e., a neural network) to obtain the image features y corresponding to the current image block x. Performing feature transformation on the current image block x using the analysis transformation network means transforming the current image block x into image features y in the latent domain, thereby facilitating all subsequent processes to operate in the latent domain.

[0127] The image can be divided into one image block or multiple image blocks. If the image is divided into one image block, the current image block x can also be an image, that is, the encoding and decoding process for the image block can also be directly used for the image.

[0128] After obtaining the image feature y, the encoding end performs a coefficient hyperparameter feature transformation on the image feature y to obtain a coefficient hyperparameter feature z. For example, the image feature y can be input into a hyperparameter encoding network (i.e., a neural network), and the hyperparameter encoding network performs a coefficient hyperparameter feature transformation on the image feature y to obtain a coefficient hyperparameter feature z. The hyperparameter encoding network can be a trained neural network, and there is no restriction on the training process of this hyperparameter encoding network. It only needs to be able to perform a coefficient hyperparameter feature transformation on the image feature y. The image feature y in the latent domain obtains the super-prior latent information z after passing through the hyperparameter encoding network.

[0129] After obtaining the coefficient hyperparameter feature z, the encoding end can quantize the coefficient hyperparameter feature z to obtain the hyperparameter quantization feature corresponding to the coefficient hyperparameter feature z, that is, the Q operation in Figure 4 is the quantization process. After obtaining the hyperparameter quantization feature corresponding to the coefficient hyperparameter feature z, the hyperparameter quantization feature is encoded to obtain the Bitstream#1 (also called the first code stream) corresponding to the current image block, that is, the AE operation in Figure 4 represents the encoding process, such as the entropy encoding process. Alternatively, the encoding end can also directly encode the coefficient hyperparameter feature z to obtain the Bitstream#1 corresponding to the current image block. Among them, the hyperparameter quantization feature or the coefficient hyperparameter feature z carried in Bitstream#1 is mainly used to obtain the parameters of the mean and probability distribution model.

[0130] After obtaining the Bitstream#1 corresponding to the current image block, the encoder can send the Bitstream#1 corresponding to the current image block to the decoder. For the processing process of the Bitstream#1 corresponding to the current image block by the decoder, please refer to the subsequent embodiments.

[0131] After obtaining Bitstream#1 corresponding to the current image block, the encoder can also decode Bitstream#1 to obtain the super-parameter quantization feature. That is, AD in Figure 4 represents the decoding process. Then, the super-parameter quantization feature is dequantized to obtain the coefficient super-parameter feature z_hat. The coefficient super-parameter feature z_hat can be the same as or different from the coefficient super-parameter feature z. The IQ operation in Figure 4 is the dequantization process. Alternatively, after obtaining Bitstream#1 corresponding to the current image block, the encoder can also decode Bitstream#1 to obtain the coefficient super-parameter feature z_hat without involving the dequantization process of the coefficient super-parameter feature z_hat.

[0132] For the encoding process of Bitstream#1, an encoding method of a fixed probability density model can be used. For the decoding process of Bitstream#1, a decoding method of a fixed probability density model can be used. There is no restriction on this encoding and decoding process.

[0133] After obtaining the coefficient hyperparameter feature z_hat, the encoding end can perform context-based prediction based on the coefficient hyperparameter feature z_hat of the current image block and the reconstruction feature y_hat of the previous image block (see the subsequent embodiments for the determination process of the reconstruction feature y_hat) to obtain the prediction value mu (i.e., mean mu) corresponding to the current image block. For example, the coefficient hyperparameter feature z_hat and the reconstruction feature y_hat are input to the mean prediction network, and the mean prediction network determines the prediction value mu based on the coefficient hyperparameter feature z_hat and the reconstruction feature y_hat. There is no restriction on this prediction process. Among them, for the context-based prediction process, the input includes the coefficient hyperparameter feature z_hat and the decoded reconstruction feature y_hat, and the two are jointly input to obtain a more accurate prediction value mu. The prediction value mu is used to obtain the residual r_hat by subtracting from the original feature and adding it to the decoded residual to obtain the reconstructed feature y_hat.

[0134] It should be noted that the mean prediction network is an optional neural network, that is, the mean prediction network may not be present, that is, the predicted value mu does not need to be determined through the mean prediction network. The dotted box in Figure 4 indicates that the mean prediction network is optional.

[0135] After obtaining the image feature y, the encoding end can determine the residual feature r based on the image feature y and the predicted value mu, such as taking the difference between the image feature y and the predicted value mu as the residual feature r. Then, the residual feature r is feature processed to obtain the image feature s. There is no restriction on this feature processing process, and it can be any feature processing method. In this case, it is necessary to deploy a mean prediction network, and the mean prediction network provides the predicted value mu. Alternatively, after obtaining the image feature y, the encoding end can perform feature processing on the image feature y to obtain the image feature s. There is no restriction on this feature processing process, and it can be any feature processing method. In this case, there is no need to deploy a mean prediction network, and the dotted box indicates that the residual process is an optional process.

[0136] After obtaining the image feature s, the encoder can quantize the image feature s to obtain the image quantization feature corresponding to the image feature s. That is, the Q operation in Figure 4 represents the quantization process. After obtaining the image quantization feature corresponding to the image feature s, the encoder can encode the image quantization feature to obtain Bitstream#2 (also called the second code stream) corresponding to the current image block. That is, the AE operation in Figure 4 represents the encoding process, such as the entropy encoding process. Alternatively, the encoder can directly encode the image feature s to obtain Bitstream#2 corresponding to the current image block without involving the quantization process of the image feature s.

[0137] After obtaining Bitstream#2 corresponding to the current image block, the encoder can send Bitstream#2 corresponding to the current image block to the decoder. For details about the processing of Bitstream#2 corresponding to the current image block by the decoder, see the subsequent embodiments.

[0138] After obtaining Bitstream#2 corresponding to the current image block, the encoder can also decode Bitstream#2 to obtain image quantization features. In other words, AD in Figure 4 represents the decoding process. The encoder can then dequantize the image quantization features to obtain image features s'. Image features s' can be the same as or different from image features s. The IQ operation in Figure 4 represents the dequantization process. Alternatively, after obtaining Bitstream#2 corresponding to the current image block, the encoder can also decode Bitstream#2 to obtain image features s' without involving the dequantization process of the image quantization features.

[0139] After obtaining the image feature s', the encoder can perform feature recovery on the image feature s' (i.e., the inverse process of feature processing). There is no restriction on this feature recovery process and it can be any feature recovery method to obtain the residual feature r_hat. The residual feature r_hat and the residual feature r can be the same or different. After obtaining the residual feature r_hat, the encoder determines the image feature y_hat (i.e., the reconstructed feature) based on the residual feature r_hat and the prediction value mu. The image feature y_hat and the image feature y can be the same or different. For example, the sum of the residual feature r_hat and the prediction value mu is used as the image feature y_hat. In this case, it is necessary to deploy a mean prediction network, which provides the prediction value mu. Alternatively, after obtaining the image feature s', the encoder performs feature recovery on the image feature s' (i.e., the inverse process of feature processing) to obtain the image feature y_hat. The image feature y_hat and the image feature y can be the same or different. In this case, there is no need to deploy a mean prediction network. The dotted box indicates that the residual process is an optional process.

[0140] After obtaining the image feature y_hat, the encoder can perform a synthesis transformation on the image feature y_hat to obtain the reconstructed image block x_hat corresponding to the current image block x. For example, the image feature y_hat is input to a synthesis transformation network, which performs a synthesis transformation on the image feature y_hat to obtain the reconstructed image block x_hat, thus completing the image reconstruction process. Exemplarily, the synthesis transformation network (Synthesis Transform Net) can be a network built based on deep learning or a neural network, which is used to convert image features into images.

[0141] In one possible implementation, when the encoder encodes the image quantization feature or image feature s to obtain Bitstream #2 corresponding to the current image block, the encoder needs to first determine a probability distribution model and then encode the image quantization feature or image feature s based on the probability distribution model. Furthermore, when decoding Bitstream #2, the encoder also needs to first determine a probability distribution model and then decode Bitstream #2 based on the probability distribution model.

[0142] In order to obtain the probability distribution model, as shown in Figure 4, after obtaining the coefficient hyperparameter feature z_hat, the encoding end can perform a coefficient hyperparameter feature inverse transformation on the coefficient hyperparameter feature z_hat to obtain the probability distribution parameters. For example, the coefficient hyperparameter feature z_hat is input to the probability hyperparameter decoding network, and the probability hyperparameter decoding network performs a coefficient hyperparameter feature inverse transformation on the coefficient hyperparameter feature z_hat to obtain the probability distribution parameter sigma. After obtaining the probability distribution parameters, a probability distribution model can be generated based on the probability distribution parameters. Among them, the probability hyperparameter decoding network can be a trained neural network. There is no restriction on the training process of this probability hyperparameter decoding network. It only needs to be able to perform a coefficient hyperparameter feature inverse transformation on the coefficient hyperparameter feature z_hat.

[0143] In one possible implementation, the processing at the encoding end may be performed by a deep learning model or a neural network model, thereby achieving an end-to-end image compression and encoding process, without any restriction on the encoding process.

[0144] Example 4: For Example 1 and Example 2, the processing process of the decoding end can be seen in Figure 5. Of course, Figure 5 is only an example of the processing process of the decoding end and does not limit the processing process of the decoding end.

[0145] After obtaining Bitstream#1 corresponding to the current image block, the decoder can also decode Bitstream#1 to obtain the super-parameter quantization feature. That is, AD in Figure 5 represents the decoding process. Then, the super-parameter quantization feature is dequantized to obtain the coefficient super-parameter feature z_hat. The coefficient super-parameter feature z_hat can be the same as or different from the coefficient super-parameter feature z. The IQ operation in Figure 5 is the dequantization process. Alternatively, after obtaining Bitstream#1 corresponding to the current image block, the decoder can also decode Bitstream#1 to obtain the coefficient super-parameter feature z_hat without involving the dequantization process of the coefficient super-parameter feature z_hat.

[0146] For the decoding process of Bitstream#1, a decoding method using a fixed probability density model may be used, and there is no restriction on this.

[0147] The image can be divided into one image block or multiple image blocks. If the image is divided into one image block, the current image block x can also be an image, that is, the decoding process for the image block can also be directly used for the image.

[0148] After obtaining the coefficient hyperparameter feature z_hat, the decoding end can perform a context-based prediction based on the coefficient hyperparameter feature z_hat of the current image block and the reconstruction feature y_hat of the previous image block (the determination process of the reconstruction feature y_hat is described in subsequent embodiments) to obtain the predicted value mu (i.e., mean mu) corresponding to the current image block. For example, the coefficient hyperparameter feature z_hat and the reconstruction feature y_hat are input to the mean prediction network, and the mean prediction network determines the predicted value mu based on the coefficient hyperparameter feature z_hat and the reconstruction feature y_hat. There is no restriction on this prediction process. Among them, for the context-based prediction process, the input includes the coefficient hyperparameter feature z_hat and the decoded reconstruction feature y_hat, and the two are jointly input to obtain a more accurate prediction value mu.

[0149] It should be noted that the mean prediction network is an optional neural network, that is, the mean prediction network may not be present, that is, the predicted value mu does not need to be determined through the mean prediction network. The dotted box in Figure 5 indicates that the mean prediction network is optional.

[0150] After obtaining Bitstream#2 corresponding to the current image block, the decoder can also decode Bitstream#2 to obtain the image quantization feature. That is, AD in Figure 5 represents the decoding process. The decoder can then dequantize the image quantization feature to obtain image feature s'. Image feature s' can be the same as or different from image feature s. The IQ operation in Figure 5 represents the dequantization process. Alternatively, after obtaining Bitstream#2 corresponding to the current image block, the decoder can also decode Bitstream#2 to obtain image feature s' without involving the dequantization process of the image quantization feature.

[0151] After obtaining the image feature s', the decoding end can perform feature recovery on the image feature s' (i.e., the inverse process of feature processing) to obtain the residual feature r_hat. The residual feature r_hat is the same as or different from the residual feature r. After obtaining the residual feature r_hat, the decoding end determines the image feature y_hat (i.e., the reconstructed feature) based on the residual feature r_hat and the prediction value mu. The image feature y_hat is the same as or different from the image feature y, such as taking the sum of the residual feature r_hat and the prediction value mu as the image feature y_hat. In this case, it is necessary to deploy a mean prediction network, and the mean prediction network provides the prediction value mu. Alternatively, after obtaining the image feature s', the decoding end performs feature recovery on the image feature s' to obtain the image feature y_hat. The image feature y_hat can be the same as or different from the image feature y. In this case, there is no need to deploy a mean prediction network, and the dotted box indicates that the residual process is an optional process.

[0152] After obtaining the image feature y_hat, the decoder can perform a synthesis transformation on the image feature y_hat to obtain the reconstructed image block x_hat corresponding to the current image block x. For example, the image feature y_hat is input to the synthesis transformation network, which performs a synthesis transformation on the image feature y_hat to obtain the reconstructed image block x_hat, thus completing the image reconstruction process. Exemplarily, the synthesis transformation network (Synthesis Transform Net) can be a network built based on deep learning or a neural network, which is used to convert image features into images.

[0153] In one possible implementation, when decoding Bitstream#2, the decoding end needs to first determine the probability distribution model, and then decode Bitstream#2 based on the probability distribution model. In order to obtain the probability distribution model, referring to FIG5 , after obtaining the coefficient superparameter feature z_hat, the decoding end can perform a coefficient superparameter feature inverse transformation on the coefficient superparameter feature z_hat to obtain the probability distribution parameter sigma. For example, the coefficient superparameter feature z_hat is input to the probability superparameter decoding network, and the probability superparameter decoding network performs a coefficient superparameter feature inverse transformation on the coefficient superparameter feature z_hat to obtain the probability distribution parameters. After obtaining the probability distribution parameters, a probability distribution model can be generated based on the probability distribution parameters. The probability superparameter decoding network can be a trained neural network, and there is no restriction on the training process of the probability superparameter decoding network. It is sufficient to perform a coefficient superparameter feature inverse transformation on the coefficient superparameter feature z_hat to obtain the probability distribution parameters.

[0154] In one possible implementation, the processing process at the decoding end may be performed by a deep learning model or a neural network model, thereby realizing an end-to-end decoding process, without any limitation on this decoding process.

[0155] Example 5: Based on Examples 3 and 4, a feature domain enhancement module can be added before the synthesis transformation network. As shown in Figure 6A, the input feature of the feature domain enhancement module can be the image feature y_hat (hereinafter referred to as the initial reconstruction feature y_hat), and the output feature of the feature domain enhancement module can be the enhanced reconstruction feature y_hat_enhanced. The enhanced reconstruction feature y_hat_enhanced is input to the synthesis transformation network, which performs a synthesis transformation on the enhanced reconstruction feature y_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0156] On the encoder side: After obtaining the current image block x, the analysis and transformation network performs an analysis and transformation on the current image block x to obtain the image feature y corresponding to the current image block x. The hyperparameter coding network performs a coefficient hyperparameter feature transformation on the image feature y to obtain the coefficient hyperparameter feature z. The coefficient hyperparameter feature corresponding to the current image block (which can be the coefficient hyperparameter feature z itself or the hyperparameter quantization feature of the coefficient hyperparameter feature z) is encoded to obtain the code stream corresponding to the current image block.

[0157] The bitstream corresponding to the current image block is decoded to obtain the coefficient hyperparameter feature z_hat corresponding to the current image block (e.g., the coefficient hyperparameter feature z_hat itself is decoded from the bitstream, or the hyperparameter quantization feature is decoded from the bitstream and the hyperparameter quantization feature is dequantized to obtain the coefficient hyperparameter feature z_hat). Then, the coefficient hyperparameter feature z_hat is inversely transformed through the probabilistic hyperparameter decoding network to obtain the probability distribution parameter sigma.

[0158] Based on the probability distribution parameter sigma, the initial image features corresponding to the current image block (the initial image features can be the image feature y, the residual feature r corresponding to the image feature y, the image feature s after feature processing of the image feature y or the residual feature r, or the image quantization features corresponding to the image feature y, the residual feature r, or the image feature s, and there is no restriction on the initial image features) are encoded to obtain the code stream corresponding to the current image block.

[0159] The bitstream corresponding to the current image block is decoded based on the probability distribution parameter sigma to obtain the initial reconstructed feature y_hat corresponding to the current image block. For example, if the initial image feature is the image feature y, the initial reconstructed feature y_hat is decoded from the bitstream. If the initial image feature is the image quantization feature corresponding to the image feature y, the image quantization feature is decoded from the bitstream, and the image quantization feature is dequantized to obtain the initial reconstructed feature y_hat. For another example, if the initial image feature is the residual feature r corresponding to the image feature y, the residual feature r_hat is decoded from the bitstream, and the initial reconstructed feature y_hat is determined based on the residual feature r_hat and the predicted value mu. If the initial image feature is the image quantization feature corresponding to the residual feature r, the image quantization feature is decoded from the bitstream, and the image quantization feature is dequantized to obtain the residual feature r_hat. The initial reconstructed feature y_hat is determined based on the residual feature r_hat and the predicted value mu. If the initial image feature is the image feature y or the image feature s corresponding to the residual feature r, then image feature s' is decoded from the bitstream, and feature recovery is performed on image feature s' to obtain the initial reconstructed feature y_hat or the residual feature r_hat. If the residual feature r_hat is obtained, the initial reconstructed feature y_hat can also be determined based on the residual feature r_hat and the predicted value mu. If the initial image feature is the image quantization feature corresponding to the image feature s, then the image quantization feature is decoded from the bitstream, and the image quantization feature is dequantized to obtain image feature s'. Feature recovery is performed on image feature s' to obtain the initial reconstructed feature y_hat or the residual feature r_hat. If the residual feature r_hat is obtained, the initial reconstructed feature y_hat can also be determined based on the residual feature r_hat and the predicted value mu.

[0160] After obtaining the initial reconstructed feature y_hat, the initial reconstructed feature y_hat can be input into the feature domain enhancement module, which performs feature domain enhancement on the initial reconstructed feature y_hat to obtain the enhanced reconstructed feature y_hat_enhanced, and the enhanced reconstructed feature y_hat_enhanced is input into the synthetic transformation network, which performs synthetic transformation on the enhanced reconstructed feature y_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0161] On the decoding side: Decode the bitstream corresponding to the current image block to obtain the coefficient hyperparameter feature z_hat corresponding to the current image block (e.g., decode the coefficient hyperparameter feature z_hat itself from the bitstream, or decode the hyperparameter quantization feature from the bitstream and dequantize the hyperparameter quantization feature to obtain the coefficient hyperparameter feature z_hat). Then, perform an inverse transformation on the coefficient hyperparameter feature z_hat through the probabilistic hyperparameter decoding network to obtain the probability distribution parameter sigma.

[0162] The bitstream corresponding to the current image block is decoded based on the probability distribution parameter sigma to obtain the initial reconstructed feature y_hat corresponding to the current image block. For example, the initial reconstructed feature y_hat is decoded from the bitstream. Alternatively, the image quantization feature is decoded from the bitstream and the image quantization feature is dequantized to obtain the initial reconstructed feature y_hat. For another example, the residual feature r_hat is decoded from the bitstream and the initial reconstructed feature y_hat is determined based on the residual feature r_hat and the predicted value mu. Alternatively, the image quantization feature is decoded from the bitstream and the image quantization feature is dequantized to obtain the residual feature r_hat. The initial reconstructed feature y_hat is determined based on the residual feature r_hat and the predicted value mu. For another example, the image feature s' is decoded from the bitstream and feature recovery is performed on the image feature s' to obtain the initial reconstructed feature y_hat or the residual feature r_hat. If the residual feature r_hat is obtained, the initial reconstructed feature y_hat can also be determined based on the residual feature r_hat and the predicted value mu. Alternatively, the image quantization feature is decoded from the bitstream, and the image quantization feature is dequantized to obtain the image feature s', and the image feature s' is restored to obtain the initial reconstructed feature y_hat or the residual feature r_hat. If the residual feature r_hat is obtained, the initial reconstructed feature y_hat can also be determined based on the residual feature r_hat and the predicted value mu.

[0163] After obtaining the initial reconstructed feature y_hat, the initial reconstructed feature y_hat can be input into the feature domain enhancement module, which performs feature domain enhancement on the initial reconstructed feature y_hat to obtain the enhanced reconstructed feature y_hat_enhanced, and the enhanced reconstructed feature y_hat_enhanced is input into the synthetic transformation network, which performs synthetic transformation on the enhanced reconstructed feature y_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0164] Example 6: Based on Examples 3 and 4, a feature domain enhancement module can be added before the synthesis transformation network, and a filter enhancement module can be added after the synthesis transformation network. The filter enhancement module can include a luminance edge enhancement module and a chrominance filter enhancement module. As shown in FIG6B , the input features of the feature domain enhancement module can be the initial reconstructed features y_hat, and the output features of the feature domain enhancement module are the enhanced reconstructed features y_hat_enhanced. The enhanced reconstructed features y_hat_enhanced can be input to the synthesis transformation network, which performs a synthesis transformation on the enhanced reconstructed features y_hat_enhanced to obtain the initial reconstructed image block x_hat corresponding to the current image block x. The input features of the filter enhancement module can be the initial reconstructed image block x_hat, and the output features of the filter enhancement module can be the target reconstructed image block x_hat_enhanced. For example, the initial reconstructed image block x_hat includes the initial luminance reconstructed image block x_Y_hat and the initial chrominance reconstructed image block x_UV_hat. The luminance edge enhancement module performs luminance edge enhancement on the initial luminance reconstructed image block x_Y_hat to obtain the target luminance reconstructed image block x_Y_hat_enhanced. The chroma filtering enhancement module performs chroma filtering enhancement on the initial chroma reconstructed image block x_UV_hat to obtain a target chroma reconstructed image block x_UV_hat_enhanced. Then, the target luminance reconstructed image block x_Y_hat_enhanced is combined with the target chroma reconstructed image block x_UV_hat_enhanced to obtain a target reconstructed image block x_hat_enhanced.

[0165] For the encoding end: the current image block x is analyzed and transformed through the analysis transformation network to obtain the image feature y corresponding to the current image block x. The image feature y is subjected to coefficient superparameter feature transformation through the hyperparameter coding network to obtain the coefficient superparameter feature z. The coefficient superparameter feature corresponding to the current image block is encoded to obtain the code stream corresponding to the current image block. The code stream corresponding to the current image block is decoded to obtain the coefficient superparameter feature z_hat corresponding to the current image block, and the coefficient superparameter feature z_hat is inversely transformed through the probability superparameter decoding network to obtain the probability distribution parameter sigma. The initial image feature corresponding to the current image block is encoded based on the probability distribution parameter sigma to obtain the code stream corresponding to the current image block. The code stream corresponding to the current image block is decoded based on the probability distribution parameter sigma to obtain the initial reconstruction feature y_hat corresponding to the current image block. The above process can be referred to Example 5 and will not be repeated here.

[0166] After obtaining the initial reconstructed feature y_hat, the initial reconstructed feature y_hat can be input into the feature domain enhancement module, which performs feature domain enhancement on the initial reconstructed feature y_hat to obtain the enhanced reconstructed feature y_hat_enhanced, and the enhanced reconstructed feature y_hat_enhanced is input into the synthesis transformation network, which performs synthesis transformation on the enhanced reconstructed feature y_hat_enhanced to obtain the initial reconstructed image block x_hat corresponding to the current image block x, where the initial reconstructed image block x_hat includes the initial luminance reconstructed image block x_Y_hat and the initial chrominance reconstructed image block x_UV_hat.

[0167] After obtaining the initial luminance reconstructed image block x_Y_hat corresponding to the current image block x, the luminance edge enhancement module performs luminance edge enhancement on the initial luminance reconstructed image block x_Y_hat to obtain the target luminance reconstructed image block x_Y_hat_enhanced. After obtaining the initial chrominance reconstructed image block x_UV_hat corresponding to the current image block x, the chrominance filtering enhancement module performs chrominance filtering enhancement on the initial chrominance reconstructed image block x_UV_hat_enhanced to obtain the target chrominance reconstructed image block x_UV_hat_enhanced. The target luminance reconstructed image block x_Y_hat_enhanced is then combined with the target chrominance reconstructed image block x_UV_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0168] On the decoding end: Decode the bitstream corresponding to the current image block to obtain the coefficient hyperparameter feature z_hat corresponding to the current image block. Perform an inverse transformation on the coefficient hyperparameter feature z_hat through the probabilistic hyperparameter decoding network to obtain the probability distribution parameter sigma. Based on the probability distribution parameter sigma, decode the bitstream corresponding to the current image block to obtain the initial reconstruction feature y_hat corresponding to the current image block. The above process can be found in Example 5 and will not be repeated here.

[0169] After obtaining the initial reconstructed feature y_hat, the initial reconstructed feature y_hat can be input into the feature domain enhancement module, which performs feature domain enhancement on the initial reconstructed feature y_hat to obtain the enhanced reconstructed feature y_hat_enhanced, and the enhanced reconstructed feature y_hat_enhanced is input into the synthesis transformation network, which performs synthesis transformation on the enhanced reconstructed feature y_hat_enhanced to obtain the initial reconstructed image block x_hat corresponding to the current image block x, where the initial reconstructed image block x_hat includes the initial luminance reconstructed image block x_Y_hat and the initial chrominance reconstructed image block x_UV_hat.

[0170] After obtaining the initial luminance reconstructed image block x_Y_hat corresponding to the current image block x, the luminance edge enhancement module performs luminance edge enhancement on the initial luminance reconstructed image block x_Y_hat to obtain the target luminance reconstructed image block x_Y_hat_enhanced. After obtaining the initial chrominance reconstructed image block x_UV_hat corresponding to the current image block x, the chrominance filtering enhancement module performs chrominance filtering enhancement on the initial chrominance reconstructed image block x_UV_hat_enhanced to obtain the target chrominance reconstructed image block x_UV_hat_enhanced. The target luminance reconstructed image block x_Y_hat_enhanced is then combined with the target chrominance reconstructed image block x_UV_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0171] Example 7: Based on Examples 3 and 4, a filtering enhancement module can be added after the synthetic transformation network. This filtering enhancement module can include a luma edge enhancement module and a chroma filtering enhancement module. As shown in FIG6C , the input features of the filtering enhancement module can be the initial reconstructed image block x_hat, and the output features of the filtering enhancement module can be the target reconstructed image block x_hat_enhanced. For example, the initial reconstructed image block x_hat can include an initial luma reconstructed image block x_Y_hat and an initial chroma reconstructed image block x_UV_hat. The luma edge enhancement module performs luma edge enhancement on the initial luma reconstructed image block x_Y_hat to obtain a target luma reconstructed image block x_Y_hat_enhanced. The chroma filtering enhancement module performs chroma filtering enhancement on the initial chroma reconstructed image block x_UV_hat to obtain a target chroma reconstructed image block x_UV_hat_enhanced. The target luma reconstructed image block x_Y_hat_enhanced and the target chroma reconstructed image block x_UV_hat_enhanced can then be combined to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0172] For the encoding end: the current image block x is analyzed and transformed through the analysis transformation network to obtain the image feature y corresponding to the current image block x. The image feature y is subjected to coefficient superparameter feature transformation through the hyperparameter coding network to obtain the coefficient superparameter feature z. The coefficient superparameter feature corresponding to the current image block is encoded to obtain the code stream corresponding to the current image block. The code stream corresponding to the current image block is decoded to obtain the coefficient superparameter feature z_hat corresponding to the current image block, and the coefficient superparameter feature z_hat is inversely transformed through the probability superparameter decoding network to obtain the probability distribution parameter sigma. The initial image feature corresponding to the current image block is encoded based on the probability distribution parameter sigma to obtain the code stream corresponding to the current image block. The code stream corresponding to the current image block is decoded based on the probability distribution parameter sigma to obtain the initial reconstruction feature y_hat corresponding to the current image block. The above process can be referred to Example 5 and will not be repeated here.

[0173] After obtaining the initial reconstruction feature y_hat, the initial reconstruction feature y_hat is input to the synthetic transformation network (with

[0174] The difference between Examples 5 and 6 is that here, the enhanced reconstruction feature y_hat_enhanced is not input into the synthesis transformation network. Instead, the synthesis transformation network performs a synthesis transformation on the initial reconstruction feature y_hat to obtain the initial reconstructed image block x_hat corresponding to the current image block x. The initial reconstructed image block x_hat includes an initial luminance reconstruction image block x_Y_hat and an initial chrominance reconstruction image block x_UV_hat.

[0175] After obtaining the initial luminance reconstructed image block x_Y_hat corresponding to the current image block x, the luminance edge enhancement module performs luminance edge enhancement on the initial luminance reconstructed image block x_Y_hat to obtain the target luminance reconstructed image block x_Y_hat_enhanced. After obtaining the initial chrominance reconstructed image block x_UV_hat corresponding to the current image block x, the chrominance filtering enhancement module performs chrominance filtering enhancement on the initial chrominance reconstructed image block x_UV_hat_enhanced to obtain the target chrominance reconstructed image block x_UV_hat_enhanced. The target luminance reconstructed image block x_Y_hat_enhanced is then combined with the target chrominance reconstructed image block x_UV_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0176] On the decoding end: Decode the bitstream corresponding to the current image block to obtain the coefficient hyperparameter feature z_hat corresponding to the current image block. Perform an inverse transformation on the coefficient hyperparameter feature z_hat through the probabilistic hyperparameter decoding network to obtain the probability distribution parameter sigma. Based on the probability distribution parameter sigma, decode the bitstream corresponding to the current image block to obtain the initial reconstruction feature y_hat corresponding to the current image block. The above process can be found in Example 5 and will not be repeated here.

[0177] After obtaining the initial reconstructed feature y_hat, the initial reconstructed feature y_hat is input into the synthesis transformation network, and the synthesis transformation network performs a synthesis transformation on the initial reconstructed feature y_hat to obtain the initial reconstructed image block x_hat corresponding to the current image block x. The initial reconstructed image block x_hat includes the initial luminance reconstruction image block x_Y_hat and the initial chrominance reconstruction image block x_UV_hat.

[0178] After obtaining the initial luminance reconstructed image block x_Y_hat corresponding to the current image block x, the luminance edge enhancement module performs luminance edge enhancement on the initial luminance reconstructed image block x_Y_hat to obtain the target luminance reconstructed image block x_Y_hat_enhanced. After obtaining the initial chrominance reconstructed image block x_UV_hat corresponding to the current image block x, the chrominance filtering enhancement module performs chrominance filtering enhancement on the initial chrominance reconstructed image block x_UV_hat_enhanced to obtain the target chrominance reconstructed image block x_UV_hat_enhanced. The target luminance reconstructed image block x_Y_hat_enhanced is then combined with the target chrominance reconstructed image block x_UV_hat_enhanced to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x.

[0179] Example 8: In Example 5 and Example 6, the initial reconstructed feature y_hat is input into the feature domain enhancement module, and the feature domain enhancement module performs feature domain enhancement on the initial reconstructed feature y_hat to obtain the enhanced reconstructed feature y_hat_enhanced. For example, the initial reconstructed feature y_hat can be enhanced in the feature domain based on the feature domain enhancement parameters and probability distribution parameters sigma corresponding to the current image block to obtain the enhanced reconstructed feature y_hat_enhanced. The feature domain enhancement can include feature adaptive edge enhancement and feature adaptive stretching. The probability distribution parameter sigma is used to assist in calculation, determine the channels that require feature adaptive edge enhancement and the channels that require feature adaptive stretching, and determine the strength of the stretching. The process is described below.

[0180] The initial reconstruction feature y_hat can be of size C L xHL xW L A three-dimensional tensor, where C L is the number of channels of the initial reconstructed feature y_hat (i.e., the number of channels in the feature domain and the number of channels in the Latent domain), H L is the feature height of the initial reconstructed feature y_hat (i.e., the feature height of the feature domain and the feature height of the Latent domain), W L is the feature width of the initial reconstructed feature y_hat (i.e., the feature width of the feature domain and the feature width of the Latent domain). For each channel of the initial reconstructed feature y_hat, take channel ch as an example, ch can be [1, 2, ..., C L ], the shape of the channel ch can be H L xW L The two-dimensional tensor y_hat_ch is called the feature channel map, so the initial reconstruction feature y_hat can include C L Feature channel map.

[0181] The probability distribution parameter sigma can also be of size C L xH L xW L A three-dimensional tensor, where C L is the number of channels of the probability distribution parameter sigma, H L is the characteristic height of the probability distribution parameter sigma, W L is the characteristic width of the probability distribution parameter sigma. For each channel of the probability distribution parameter sigma, take channel ch as an example, ch can be [1, 2, ..., C L ], the shape of the channel ch can be H L xW L The two-dimensional tensor sigma_ch is called the probability distribution channel map, so the probability distribution parameter sigma can include C L probability distribution channel graph, and C L The probability distribution channel graph and C L The feature channel maps correspond one to one, such as the first probability distribution channel map corresponds to the first feature channel map, the second probability distribution channel map corresponds to the second feature channel map, and so on. L The probability distribution channel diagram and the C L The feature channel map corresponds to

[0182] Targeting C L feature channel map, C LThe feature channel map is divided into important feature channel maps and non-important feature channel maps. There can be at least one important feature channel map and multiple non-important feature channel maps. For example, C L At least one feature channel map is selected from the feature channel maps as an important feature channel map, and the remaining feature channel maps are selected as non-important feature channel maps. L The probability distribution channel graph and C L The feature channel maps correspond one to one, so the probability distribution channel map corresponding to the important feature channel map can be selected as the important probability distribution channel map, and the probability distribution channel map corresponding to the unimportant feature channel map can be selected as the unimportant probability distribution channel map. For example, if the first feature channel map is an important feature channel map, the first probability distribution channel map corresponding to the first feature channel map can be selected as the important probability distribution channel map.

[0183] In summary, the initial reconstructed feature y_hat may include an important feature channel map and an unimportant feature channel map, and the probability distribution parameter sigma may include an important probability distribution channel map corresponding to the important feature channel map and an unimportant probability distribution channel map corresponding to the unimportant feature channel map. For the important feature channel map, feature-adaptive edge enhancement can be performed on the important feature channel map based on the feature domain enhancement parameters and the important probability distribution channel map to obtain a first reconstructed feature after feature-adaptive edge enhancement. For the unimportant feature channel map, feature-adaptive stretching can be performed on the unimportant feature channel map based on the feature domain enhancement parameters and the unimportant probability distribution channel map to obtain a second reconstructed feature after feature-adaptive stretching. On this basis, an enhanced reconstructed feature y_hat_enhanced can be generated based on the first reconstructed feature and the second reconstructed feature.

[0184] For example, assuming that feature channel map 1 is an important feature channel map, feature channel map 2-feature channel map 4 are unimportant feature channel maps, probability distribution channel map 1 is an important probability distribution channel map, and probability distribution channel map 2-probability distribution channel map 4 are unimportant probability distribution channel maps, then: feature channel map 1 can be feature-adaptively edge enhanced based on the feature domain enhancement parameters and probability distribution channel map 1 to obtain the first reconstructed feature 1 after feature-adaptive edge enhancement (i.e., the reconstructed feature of the first channel). Feature channel map 2 can be feature-adaptively stretched based on the feature domain enhancement parameters and probability distribution channel map 2 to obtain the second reconstructed feature 2 after feature-adaptive stretching (i.e., the reconstructed feature of the second channel). Feature channel map 3 can be feature-adaptively stretched based on the feature domain enhancement parameters and probability distribution channel map 3 to obtain the second reconstructed feature 3 after feature-adaptive stretching. Feature channel map 4 can be feature-adaptively stretched based on the feature domain enhancement parameters and probability distribution channel map 4 to obtain the second reconstructed feature 4 after feature-adaptive stretching. Then, the first reconstruction feature 1, the second reconstruction feature 2, the second reconstruction feature 3 and the second reconstruction feature 4 may be combined according to channels (eg, spliced ​​according to channels), that is, the reconstruction features of the four channels may be combined to obtain the enhanced reconstruction feature y_hat_enhanced.

[0185] Example 9: In Example 8, it is necessary to L The feature channel maps are divided into important feature channel maps and non-important feature channel maps. For example, C L The feature channel maps are divided into important feature channel maps and unimportant feature channel maps:

[0186] Method 1: The encoder encodes the important channel identifier in the code stream corresponding to the current image block, and the decoder decodes the important channel identifier from the code stream corresponding to the current image block, so that the decoder can obtain the important channel identifier.

[0187] For example, the encoding end will C L The feature channel map is divided into important feature channel maps and non-important feature channel maps. There is no restriction on this process, and the important channel identifier (also called important channel number, recorded as important_channel) is encoded in the code stream corresponding to the current image block. After receiving the code stream, the decoding end decodes the important channel identifier from the code stream and obtains the important channel identifier from C. L Among the feature channel maps, the feature channel map corresponding to the important channel identifier is selected as the important feature channel map, and the remaining feature channel maps are selected as unimportant feature channel maps. The probability distribution channel map corresponding to the important feature channel map is selected as the important probability distribution channel map, and the probability distribution channel map corresponding to the unimportant feature channel map is selected as the unimportant probability distribution channel map.

[0188] Among them, the encoding end is CL When the feature channel graphs are divided into important feature channel graphs and unimportant feature channel graphs, the feature values ​​in each feature channel graph and the probability distribution values ​​in each probability distribution channel graph can be used to calculate the feature channel graphs from C. L The important feature channel maps are selected from the feature channel maps, and the remaining feature channel maps are selected as non-important feature channel maps.

[0189] Method 2: For both the encoding and decoding ends, based on the feature values ​​in each feature channel map and the probability distribution values ​​in each probability distribution channel map, L Select the important feature channel maps from the feature channel maps.

[0190] For example, for each feature channel map, the number of consumed bits corresponding to the feature channel map can be determined based on the feature value in the feature channel map and the probability distribution value in the probability distribution channel map corresponding to the feature channel map. For example, the number of consumed bits corresponding to the feature channel map can be determined using the following expression, which is only an example.

[0191] In the above expression, bits_per_ch can be used to represent the number of consumed bits corresponding to the feature channel graph ch, y_hat_ch(i,j) can be used to represent the eigenvalue of the feature point (i,j) in the feature channel graph ch, sigma_ch(i,j) can be used to represent the probability distribution value of the feature point (i,j) in the probability distribution channel graph ch, and Φ(.) can be the standard normal distribution cumulative function.

[0192] After performing the above processing on each feature channel map, the number of consumed bits corresponding to each feature channel map can be obtained. Based on the number of consumed bits corresponding to each feature channel map, the number of consumed bits corresponding to C L The important feature channel graph is selected from the feature channel graphs. For example, the feature channel graph consuming the largest number of bits is selected as the important feature channel graph, or K feature channel graphs consuming a large number of bits are selected as the important feature channel graphs, where K can be a positive integer greater than 1.

[0193] In C L After selecting important feature channel maps from the feature channel maps, the remaining feature channel maps can be selected as non-important feature channel maps, the probability distribution channel map corresponding to the important feature channel map can be selected as the important probability distribution channel map, and the probability distribution channel map corresponding to the non-important feature channel map can be selected as the non-important probability distribution channel map.

[0194] Method 3: For both the encoding and decoding ends, the consumption bit rate corresponding to each feature channel map is calculated from C LFor example, based on the consumption bit rate corresponding to each feature channel map, C can be sorted in descending order of consumption bit rate. L The feature channel maps are sorted and the top K feature channel maps are selected as important feature channel maps. Alternatively, based on the consumption bit rate corresponding to each feature channel map, the C feature channel maps can be sorted in the order of consumption bit rate from small to large. L The feature channel maps are sorted and the K feature channel maps with the lowest sorting order are selected as important feature channel maps.

[0195] In C L After selecting important feature channel maps from the feature channel maps, the remaining feature channel maps can be selected as non-important feature channel maps, the probability distribution channel map corresponding to the important feature channel map can be selected as the important probability distribution channel map, and the probability distribution channel map corresponding to the non-important feature channel map can be selected as the non-important probability distribution channel map.

[0196] Method 4: For both the encoding end and the decoding end, the default feature channel map (i.e., the fixed feature channel map) is used as the important feature channel map. For example, the first feature channel map is pre-agreed as the important feature channel map, or the sixth feature channel map is pre-agreed as the important feature channel map, or the tenth feature channel map is pre-agreed as the important feature channel map, and so on. Of course, any feature channel map can be used as the important feature channel map, and there is no restriction on this.

[0197] Mode 5: The encoder encodes the important channel identifier in the code stream corresponding to the current image block, and the decoder decodes the important channel identifier from the code stream corresponding to the current image block, so that the decoder can obtain the important channel identifier.

[0198] For example, the encoding end will C L The feature channel map is divided into important feature channel maps and non-important feature channel maps, and the important channel identifier is encoded in the code stream corresponding to the current image block. After receiving the code stream, the decoding end decodes the important channel identifier from the code stream and obtains the important channel identifier from C L From the feature channel maps, the feature channel maps corresponding to the important channel identifiers are selected as the important feature channel maps, and the remaining feature channel maps are selected as the unimportant feature channel maps. The probability distribution channel maps corresponding to the important feature channel maps are selected as the important probability distribution channel maps, and the probability distribution channel maps corresponding to the unimportant feature channel maps are selected as the unimportant probability distribution channel maps. L When a feature channel map is divided into an important feature channel map and an unimportant feature channel map, the important feature channel map and the important channel identifier can be determined based on the feature variance scale_hat in the following manner.

[0199] LinearToLog is a mapping table that can map the feature variance scale_hat to the logarithmic domain feature variance logarithmic_scale. For example, logarithmic_scale = LinearToLog(scale_hat).

[0200] Slice along the channel dimension to obtain the logarithmic domain feature variance logarithmic_scale_channel corresponding to a certain feature channel.

[0201] Based on logarithmic_scale_channel, calculate the logarithmic variance and sum_per_channel corresponding to each feature channel:

[0202] The above process is repeated for a total of C channels of logarithmic_scale. L times, that is, the logarithmic variance and sum_per_channel corresponding to each feature channel are obtained, and then the channel number corresponding to the maximum sum_per_channel is selected as the important channel identifier important_channel, and the feature channel corresponding to the important channel identifier important_channel is the important feature channel map.

[0203] Example 10: In Example 8, feature-adaptive edge enhancement is performed on the important feature channel map based on the feature domain enhancement parameters and the important probability distribution channel map to obtain the first reconstructed feature. The feature-adaptive edge enhancement process is described below.

[0204] Exemplarily, the feature domain enhancement parameters may include multiple edge enhancement segment strength values ​​and multiple edge enhancement segment thresholds. For example, the encoding end encodes multiple edge enhancement segment strength values ​​and multiple edge enhancement segment thresholds in the bitstream corresponding to the current image block, and the decoding end decodes multiple edge enhancement segment strength values ​​and multiple edge enhancement segment thresholds from the bitstream corresponding to the current image block, and the multiple edge enhancement segment strength values ​​and multiple edge enhancement segment thresholds are used as feature domain enhancement parameters.

[0205] The number of edge enhancement segment strength values ​​and the number of edge enhancement segment thresholds may be the same or different. The encoding end may also encode the number of edge enhancement segment strength values ​​(or edge enhancement segment thresholds) in the bitstream corresponding to the current image block, and the decoding end may also decode the number of edge enhancement segment strength values ​​from the bitstream corresponding to the current image block. Taking the case where the number of edge enhancement segment strength values ​​is the same as the number of edge enhancement segment thresholds, and the number of edge enhancement segment strength values ​​is n as an example, the n edge enhancement segment strength values ​​may be recorded as magl-1, magl-2, ..., magl-n, and the n edge enhancement segment thresholds may be recorded as thrl-1, thrl-2, ..., thrl-n.

[0206] Illustratively, multiple edge enhancement segmentation thresholds may constitute multiple edge enhancement threshold intervals, and the multiple edge enhancement threshold intervals correspond one-to-one to the multiple edge enhancement segmentation strength values. See Table 1 for an example of the correspondence.

[0207] Table 1

[0208] Exemplarily, the important probability distribution channel map may include multiple probability distribution values. For each probability distribution value, the edge enhancement threshold interval corresponding to the probability distribution value is first determined, and the edge enhancement segmentation strength value corresponding to the probability distribution value is determined based on the edge enhancement threshold interval. For example, if the probability distribution value is within [thrl-2, thrl-3), the edge enhancement segmentation strength value corresponding to the probability distribution value is magl-2; if the probability distribution value is within [thrl-3, thrl-4), the edge enhancement segmentation strength value corresponding to the probability distribution value is magl-3, and so on. After obtaining the edge enhancement segmentation strength value corresponding to each probability distribution value, feature-adaptive edge enhancement can be performed on the important feature channel map based on the edge enhancement segmentation strength value corresponding to each probability distribution value to obtain the first reconstructed feature after feature-adaptive edge enhancement.

[0209] For example, the following steps S11-S14 may be used to perform feature-adaptive edge enhancement on the important feature channel map.

[0210] Step S11: normalize the important feature channel map to obtain a normalized feature map.

[0211] Exemplarily, normalization can be performed based on the important feature channel map, the mean feature corresponding to the important feature channel map, and the variance feature corresponding to the important feature channel map to obtain a normalized feature map. For example, the important channel feature map can be subtracted from the mean feature and then divided by the variance feature to obtain a normalized feature map. For another example, the important channel feature map can be subtracted from the mean feature and then divided by the variance feature to obtain an intermediate feature, and then the intermediate feature can be converted to obtain a normalized feature map, such as multiplying the intermediate feature by 0.1 and adding 0.5 (0.1 and 0.5 are examples), and then limiting the feature value to be between 0 and 1. Of course, the above are just a few examples of normalizing the important feature channel map, and there is no limitation on this normalization method.

[0212] Step S12: Generate a high-frequency detail image based on the important feature channel map and the normalized feature map.

[0213] For example, a convolution operation (e.g., a two-dimensional convolution operation) can be performed on the normalized feature map with a Gaussian blur convolution kernel (which can be a 3*3 convolution kernel or a convolution kernel of other sizes, and there is no limitation on this convolution kernel) to obtain a Gaussian blurred image. Then, the Gaussian blurred image is subtracted from the important feature channel map to obtain a high-frequency detail image. Of course, the above is only an example of generating a high-frequency detail image. As long as the high-frequency details of the important feature channel map can be obtained, there is no limitation on this.

[0214] For example, for each value in the Gaussian blur convolution kernel, the denominator of the value may be a power of 2, such as 2, 4, 8, 16, 32, 64, 128, etc. The Gaussian blur convolution kernel may be an N*N convolution kernel, where N may be a positive integer. If the value of N is 3, that is, the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel may include but is not limited to:

[0215] A may be a positive integer, B may be a positive integer, and M may be a power of 2, such as 2, 4, 8, 16, 32, 64, or 128. For example, A may be 5, B may be 24, and M may be 64.

[0216] Step S13: for each eigenvalue in the high-frequency detail image, edge enhancement is performed on the eigenvalue based on the edge enhancement segment strength value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an edge enhancement eigenvalue.

[0217] Exemplarily, the important feature channel map includes multiple eigenvalues, the important probability distribution channel map includes multiple probability distribution values, and the multiple probability distribution values ​​correspond one-to-one to the multiple eigenvalues. Since the high-frequency detail image includes multiple eigenvalues ​​(corresponding one-to-one to the multiple eigenvalues ​​of the important feature channel map), the multiple probability distribution values ​​of the important probability distribution channel map correspond one-to-one to the multiple eigenvalues ​​of the high-frequency detail image. Based on this, for each eigenvalue in the high-frequency detail image, the probability distribution value corresponding to the eigenvalue can be determined from the important probability distribution channel map, and the edge enhancement threshold interval corresponding to the probability distribution value can be determined. Based on the edge enhancement threshold interval, the edge enhancement segmented strength value corresponding to the probability distribution value is determined.

[0218] After obtaining the edge enhancement segmentation strength value corresponding to the probability distribution value, the eigenvalue can be edge enhanced based on the edge enhancement segmentation strength value to obtain an edge enhancement eigenvalue, such as multiplying the eigenvalue by the edge enhancement segmentation strength value (such as magl-1, magl-2, etc.) to obtain an edge enhancement eigenvalue. If the edge enhancement threshold interval is "less than thrl-1", the eigenvalue is multiplied by 1, that is, the eigenvalue is kept unchanged and the eigenvalue is not edge enhanced. If the edge enhancement threshold interval is [thrl-1, thrl-2), the eigenvalue is multiplied by magl-1, magl-1 is a value greater than 1, so that the eigenvalue is edge enhanced. Similarly, other edge enhancement segmentation strength values ​​such as magl-2 are greater than 1, which can achieve edge enhancement.

[0219] For each eigenvalue in the high-frequency detail image, the above method can be used to obtain the edge enhancement segmentation strength value corresponding to the eigenvalue, and then the edge enhancement feature map can be determined based on the edge enhancement eigenvalue corresponding to each eigenvalue. For example, the edge enhancement segmentation strength values ​​corresponding to all eigenvalues ​​can be combined to obtain the edge enhancement feature map.

[0220] Step S14: Denormalize the edge enhancement feature map to obtain a first reconstruction feature (ie, a first reconstruction feature map). The first reconstruction feature is a reconstruction feature map after feature-adaptive edge enhancement is performed on the important feature channel map.

[0221] After obtaining the edge enhancement feature map, the edge enhancement feature map can be directly denormalized to obtain the first reconstructed feature. Alternatively, after obtaining the edge enhancement feature map, the edge enhancement feature map can be added to the normalized feature map to obtain a modified edge enhancement feature map, and the modified edge enhancement feature map can be denormalized to obtain the first reconstructed feature.

[0222] Exemplarily, the edge-enhanced normalized feature map can be determined based on the edge-enhanced feature map, and the first reconstructed feature can be determined based on the edge-enhanced normalized feature map, the mean feature corresponding to the edge-enhanced normalized feature map, and the variance feature corresponding to the edge-enhanced normalized feature map. The first reconstructed feature can also be called y_hat_sharp.

[0223] For example, the edge enhancement feature map can be transformed to obtain a normalized feature map after edge enhancement. For example, the eigenvalues ​​in the edge enhancement feature map are first restricted to be between 0 and 1, and then the eigenvalues ​​in the edge enhancement feature map are subtracted by 0.5 and divided by 10 (0.5 and 10 are both examples) to obtain the normalized feature map after edge enhancement.

[0224] The normalized feature map after edge enhancement is multiplied by the variance feature, and then the mean feature is added to obtain the edge-enhanced feature map corresponding to the important feature channel map. The edge-enhanced feature map is recorded as the first reconstructed feature y_hat_sharp.

[0225] Of course, the above is only an example of denormalizing the edge enhancement feature map, and there is no limitation on the denormalization method.

[0226] In a possible implementation, the encoder needs to encode multiple edge enhancement segment strength values ​​and multiple edge enhancement segment thresholds in the bitstream corresponding to the current image block. For this process, the encoder can adopt the following method.

[0227] The encoding end can configure multiple candidate feature domain enhancement parameters. For each candidate feature domain enhancement parameter, the candidate feature domain enhancement parameter may include multiple edge enhancement segment strength values ​​and multiple edge enhancement segment thresholds. The cost value corresponding to each candidate feature domain enhancement parameter can be determined. Based on the cost value corresponding to each candidate feature domain enhancement parameter, the feature domain enhancement parameter corresponding to the current image block can be selected from all candidate feature domain enhancement parameters, that is, the candidate feature domain enhancement parameter with the smallest cost value. The encoding end can encode the feature domain enhancement parameter to obtain the code stream corresponding to the current image block.

[0228] For example, for each candidate feature domain enhancement parameter, the initial reconstructed feature can be enhanced in the feature domain based on the candidate feature domain enhancement parameter and the probability distribution parameter to obtain an enhanced reconstructed feature. The feature domain enhancement process refers to the above embodiment. The target reconstructed image block x_hat_enhanced is determined based on the enhanced reconstructed feature, and the cost value corresponding to the candidate feature domain enhancement parameter is determined based on the target reconstructed image block x_hat_enhanced. There is no restriction on the method of determining this cost value.

[0229] For example, for each candidate feature domain enhancement parameter, the candidate feature domain enhancement parameters include magl-1, magl-2, ..., magl-n, thrl-1, thrl-2, ..., thrl-n. Based on the candidate feature domain enhancement parameters, the enhanced reconstruction features can be obtained, and then the target reconstructed image block x_hat_enhanced is obtained. The distortion index is used to calculate the distortion index value of the target reconstructed image block x_hat_enhanced and the current image block x. After obtaining the distortion index value corresponding to each candidate feature domain enhancement parameter, the candidate feature domain enhancement parameter corresponding to the minimum distortion index value can be selected as the feature domain enhancement parameter corresponding to the current image block, that is, the optimal feature domain enhancement parameter. The encoder encodes the feature domain enhancement parameter in the bitstream.

[0230] Example 11: In Example 8, it is necessary to perform feature adaptive stretching on the non-important feature channel map based on the feature domain enhancement parameters and the non-important probability distribution channel map (i.e., the non-important probability distribution channel map corresponding to the non-important feature channel map) to obtain the second reconstructed feature after feature adaptive stretching. The feature adaptive stretching process is described below.

[0231] Exemplarily, the feature domain enhancement parameter may include a stretching parameter value. For example, the encoding end may encode the stretching parameter value in the code stream corresponding to the current image block, and the decoding end may decode the stretching parameter value from the code stream corresponding to the current image block. The stretching parameter value is used as the feature domain enhancement parameter, and the stretching parameter value may be denoted as ρ.

[0232] Exemplarily, the non-important feature channel graph may include multiple eigenvalues, and the non-important probability distribution channel graph corresponding to the non-important feature channel graph may include multiple probability distribution values, and the multiple probability distribution values ​​correspond one-to-one to the multiple eigenvalues. Based on this, for each eigenvalue in the non-important feature channel graph, the stretched eigenvalue corresponding to the eigenvalue can be determined based on the eigenvalue, the stretching parameter value, and the probability distribution value corresponding to the eigenvalue. For example, the stretched eigenvalue corresponding to the eigenvalue can be determined using the following expression. Of course, the following expression is only an example and is not limited to this.

[0233] y_hat_scale=y_hat+ρ*clip3(sigma*y_hat,-0.5,0.5)

[0234] In the above expression, y_hat_scale represents the stretched eigenvalue, y_hat represents the eigenvalue in the unimportant feature channel map, ρ represents the stretching parameter value, sigma represents the probability distribution value in the unimportant probability distribution channel map, and the probability distribution value sigma corresponds to the eigenvalue y_hat. Clip3 is a limit operation used to limit sigma*y_hat to between -0.5 and 0.5. -0.5 and 0.5 are configurable values ​​and are not restricted. For example, when sigma*y_hat is less than -0.5, sigma*y_hat is limited to -0.5. When sigma*y_hat is greater than 0.5, sigma*y_hat is limited to 0.5. Other values ​​remain unchanged.

[0235] After performing the above processing on each eigenvalue in the non-important feature channel map, the stretched eigenvalue corresponding to each eigenvalue can be obtained, and then the second reconstruction feature (i.e., the second reconstruction feature map) is determined based on the stretched eigenvalue corresponding to each eigenvalue. The second reconstruction feature is the reconstructed feature map after feature-adaptive stretching of the non-important feature channel map. For example, the stretched eigenvalues ​​corresponding to all eigenvalues ​​are combined to obtain the second reconstruction feature. The second reconstruction feature is also called y_hat_scale.

[0236] The second reconstructed feature y_hat_scale corresponds to the non-important feature channel map of the initial reconstructed feature y_hat, that is, the non-important feature channel map is enhanced to obtain the second reconstructed feature y_hat_scale, the first reconstructed feature y_hat_sharp corresponds to the important feature channel map of the initial reconstructed feature y_hat, that is, the important feature channel map is enhanced to obtain the first reconstructed feature y_hat_sharp, and the second reconstructed feature y_hat_scale and the first reconstructed feature y_hat_sharp are merged to obtain the enhanced reconstructed feature y_hat_enhanced.

[0237] In one possible implementation, the encoder needs to encode a stretching parameter value in a code stream corresponding to the current image block. For this process, the encoder can adopt the following method: the encoder can configure multiple candidate stretching parameter values, determine a cost value corresponding to each candidate stretching parameter value, and based on the cost value corresponding to each candidate stretching parameter value, select a stretching parameter value corresponding to the current image block from all candidate stretching parameter values, that is, the candidate stretching parameter value with the smallest cost value. The encoder can encode the stretching parameter value to obtain a code stream corresponding to the current image block.

[0238] For example, for each candidate stretching parameter value, feature adaptive stretching can be performed on the non-important feature channel map based on the candidate stretching parameter value and the non-important probability distribution channel map to obtain a second reconstruction feature after feature adaptive stretching, and then an enhanced reconstruction feature is obtained based on the second reconstruction feature. The target reconstructed image block x_hat_enhanced is determined based on the enhanced reconstruction feature, and the cost value corresponding to the candidate stretching parameter value is determined based on the target reconstructed image block x_hat_enhanced.

[0239] For example, for each candidate stretch parameter value, an enhanced reconstruction feature can be obtained based on the candidate stretch parameter value, and then the target reconstructed image block x_hat_enhanced can be obtained. The distortion index is then used to calculate the distortion index value of the target reconstructed image block x_hat_enhanced and the current image block x. After obtaining the distortion index value corresponding to each candidate stretch parameter value, the encoder can select the candidate stretch parameter value corresponding to the minimum distortion index value as the stretch parameter value corresponding to the current image block, i.e., the optimal stretch parameter value. The encoder can then encode this stretch parameter value in the bitstream.

[0240] In another possible embodiment, the encoding end configures multiple candidate feature domain enhancement parameters. For each candidate feature domain enhancement parameter, the candidate feature domain enhancement parameter includes multiple edge enhancement segment strength values, multiple edge enhancement segment thresholds, and a stretching parameter value. The cost value corresponding to each candidate feature domain enhancement parameter is determined. Based on the cost value corresponding to each candidate feature domain enhancement parameter, the feature domain enhancement parameter corresponding to the current image block is selected from all candidate feature domain enhancement parameters, that is, the candidate feature domain enhancement parameter with the smallest cost value. The encoding end encodes the feature domain enhancement parameter (that is, multiple edge enhancement segment strength values, multiple edge enhancement segment thresholds, and a stretching parameter value) to obtain a code stream corresponding to the current image block. For example, for each candidate feature domain enhancement parameter, the initial reconstructed feature is enhanced based on the candidate feature domain enhancement parameter and the probability distribution parameter to obtain an enhanced reconstructed feature. The target reconstructed image block x_hat_enhanced is determined based on the enhanced reconstructed feature. The cost value corresponding to the candidate feature domain enhancement parameter is determined based on the target reconstructed image block x_hat_enhanced.

[0241] Example 12: In Example 6 and Example 7, the initial reconstructed image block x_hat is input to the filtering enhancement module, which enhances the initial reconstructed image block x_hat to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x. For example, the initial reconstructed image block x_hat includes the initial luminance reconstructed image block x_Y_hat and the initial chrominance reconstructed image block x_UV_hat. The luminance edge enhancement module performs luminance edge enhancement on the initial luminance reconstructed image block x_Y_hat to obtain the target luminance reconstructed image block x_Y_hat_enhanced. The chrominance filtering enhancement module performs chrominance filtering enhancement on the initial chrominance reconstructed image block x_UV_hat to obtain the target chrominance reconstructed image block x_UV_hat_enhanced. The target luminance reconstructed image block x_Y_hat_enhanced and the target chrominance reconstructed image block x_UV_hat_enhanced are combined to obtain the target reconstructed image block x_hat_enhanced. The luminance edge enhancement process of the luminance edge enhancement module is described below.

[0242] For example, the brightness edge enhancement module can perform brightness edge enhancement on the initial brightness reconstructed image block x_Y_hat based on the image domain enhancement parameter and probability distribution parameter sigma corresponding to the current image block to obtain the target brightness reconstructed image block x_Y_hat_enhanced corresponding to the current image block. The brightness edge enhancement process is described below.

[0243] Exemplarily, the image domain enhancement parameters may include multiple image enhancement segmentation strength values ​​and multiple image enhancement segmentation thresholds. For example, the encoding end encodes multiple image enhancement segmentation strength values ​​and multiple image enhancement segmentation thresholds in the bitstream corresponding to the current image block, and the decoding end decodes multiple image enhancement segmentation strength values ​​and multiple image enhancement segmentation thresholds from the bitstream corresponding to the current image block, and the multiple image enhancement segmentation strength values ​​and multiple image enhancement segmentation thresholds are used as image domain enhancement parameters.

[0244] The number of image enhancement segmentation strength values ​​and the number of image enhancement segmentation thresholds may be the same or different. The encoding end may also encode the number of image enhancement segmentation strength values ​​(or image enhancement segmentation thresholds) in the bitstream corresponding to the current image block, and the decoding end may also decode the number of image enhancement segmentation strength values ​​from the bitstream corresponding to the current image block. Taking the case where the number of image enhancement segmentation strength values ​​is the same as the number of image enhancement segmentation thresholds, and the number of image enhancement segmentation strength values ​​is m as an example, the m image enhancement segmentation strength values ​​may be recorded as magy-1, magy-2, ..., magy-m, and the m image enhancement segmentation thresholds may be recorded as thry-1, thry-2, ..., thry-m.

[0245] Exemplarily, multiple image enhancement segmentation thresholds may constitute multiple image enhancement threshold intervals, and the multiple image enhancement threshold intervals correspond one-to-one to the multiple image enhancement segmentation strength values. See Table 2 for an example of this correspondence.

[0246] Table 2

[0247] Exemplarily, the following steps S21-S24 may be used to perform brightness edge enhancement on the initial brightness reconstructed image block x_Y_hat.

[0248] Step S21: Obtain a target probability distribution channel map based on the probability distribution parameters.

[0249] For example, the initial brightness reconstruction image block x_Y_hat can be a two-dimensional tensor of size H×W, where H is the image height of the initial brightness reconstruction image block x_Y_hat and W is the image width of the initial brightness reconstruction image block x_Y_hat. The probability distribution parameter sigma can be a two-dimensional tensor of size C L xH L xW L The three-dimensional tensor, C L is the number of channels of the probability distribution parameter sigma, H L is the characteristic height of the probability distribution parameter sigma, W L is the characteristic width of the probability distribution parameter sigma. The image height H of the initial brightness reconstruction image block x_Y_hat can be greater than the characteristic height H of the probability distribution parameter sigma. L , such as the image height H is the feature height H L 4 times, 8 times, 16 times, etc. The image width W of the initial brightness reconstruction image block x_Y_hat can be greater than the feature width W of the probability distribution parameter sigma L , such as the image width W is the feature width W L 4 times, 8 times, 16 times, etc.

[0250] In order to perform brightness edge enhancement on the initial brightness reconstructed image block x_Y_hat using probability distribution parameters, it is necessary to obtain a target probability distribution channel map based on the probability distribution parameters. The target probability distribution channel map can be a two-dimensional tensor of size H×W.

[0251] For example, you can start from C L Select a probability distribution channel graph from the probability distribution channel graphs. Since the probability distribution parameter sigma includes important probability distribution channel graphs (one) and non-important probability distribution channel graphs (multiple), we can select a probability distribution channel graph from C L Select an important probability distribution channel diagram from the probability distribution channel diagram, or you can select an important probability distribution channel diagram from C LSelect a non-important probability distribution channel graph from the probability distribution channel graphs, and then take selecting an important probability distribution channel graph as an example to illustrate.

[0252] Then, the important probability distribution channel map is upsampled to obtain the target probability distribution channel map. There is no restriction on the upsampling method, and the size of the target probability distribution channel map can be the same as the size of the initial brightness reconstructed image block x_Y_hat.

[0253] For example, the nearest neighbor upsampling method can be used to upsample the important probability distribution channel map to obtain the target probability distribution channel map. For the nearest neighbor upsampling method, a pixel can be selected on the original low-resolution image (i.e., the important probability distribution channel map) as the center point of the corresponding area on the target high-resolution image (i.e., the target probability distribution channel map). The value of the pixel closest to the pixel on the original low-resolution image is used as the value of the corresponding pixel on the target high-resolution image. Repeat the above process until all pixels of the target high-resolution image are assigned values.

[0254] In summary, the important probability distribution channel map can be upsampled to the same size as the initial brightness reconstructed image block x_Y_hat, and the upsampled probability distribution channel map is recorded as the target probability distribution channel map sigma_channel_upscale.

[0255] For example, the code stream corresponding to the current image block can be decoded to obtain the important channel identifier corresponding to the current image block. Of course, in addition to decoding the important channel identifier from the code stream, other methods can also be used to obtain the important channel identifier, as shown in the five implementation methods of Example 9. On this basis, if the probability distribution parameters include multiple probability distribution channel maps, the probability distribution channel map corresponding to the important channel identifier is selected from the multiple probability distribution channel maps as the important probability distribution channel map; then, the important probability distribution channel map is upsampled to obtain the target probability distribution channel map.

[0256] Step S22: If the target probability distribution channel map includes multiple probability distribution values, for each probability distribution value, determine the image enhancement segmentation intensity value corresponding to the probability distribution value based on the image enhancement threshold interval corresponding to the probability distribution value.

[0257] Exemplarily, the target probability distribution channel map may include multiple probability distribution values. For each probability distribution value, the image enhancement threshold interval corresponding to the probability distribution value may be determined first, and the image enhancement segmentation intensity value corresponding to the probability distribution value may be determined based on the image enhancement threshold interval. For example, if the probability distribution value is within [thry-2, thry-3), the image enhancement segmentation intensity value corresponding to the probability distribution value may be magy-2; if the probability distribution value is within [thry-3, thry-4), the image enhancement segmentation intensity value corresponding to the probability distribution value may be magy-3, and so on.

[0258] Step S23 : performing brightness edge enhancement on the initial brightness reconstructed image block x_Y_hat based on the image enhancement segment intensity value corresponding to each probability distribution value to obtain a target brightness reconstructed image block x_Y_hat_enhanced corresponding to the current image block.

[0259] First, a high-frequency detail image can be generated based on the initial brightness reconstructed image block x_Y_hat. For example, a convolution operation (such as a two-dimensional convolution operation) can be performed on the initial brightness reconstructed image block x_Y_hat with a Gaussian blur convolution kernel (which can be a 3*3 convolution kernel or a convolution kernel of other sizes, and there is no restriction on this convolution kernel) to obtain a Gaussian blurred image. Then, the Gaussian blurred image is subtracted from the initial brightness reconstructed image block x_Y_hat to obtain a high-frequency detail image. Of course, the above is only an example of generating a high-frequency detail image, as long as the high-frequency details of the initial brightness reconstructed image block x_Y_hat can be obtained.

[0260] For example, for each value in the Gaussian blur convolution kernel, the denominator of the value may be a power of 2, such as 2, 4, 8, 16, 32, 64, 128, etc. The Gaussian blur convolution kernel may be an N*N convolution kernel, where N may be a positive integer. If the value of N is 3, that is, the Gaussian blur convolution kernel is a 3*3 convolution kernel, then the Gaussian blur convolution kernel may include but is not limited to:

[0261] A may be a positive integer, B may be a positive integer, and M may be a power of 2, such as 2, 4, 8, 16, 32, 64, or 128. For example, A may be 5, B may be 24, and M may be 64.

[0262] Then, for each eigenvalue in the high-frequency detail image, the edge enhancement is performed on the eigenvalue based on the image enhancement segmentation intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain the image enhancement eigenvalue. For example, the initial brightness reconstructed image block x_Y_hat includes multiple eigenvalues, the target probability distribution channel map includes multiple probability distribution values, and the multiple probability distribution values ​​correspond one-to-one to the multiple eigenvalues. Since the high-frequency detail image includes multiple eigenvalues ​​(corresponding one-to-one to the multiple eigenvalues ​​of the initial brightness reconstructed image block x_Y_hat), the multiple probability distribution values ​​of the target probability distribution channel map correspond one-to-one to the multiple eigenvalues ​​of the high-frequency detail image. Based on this, for each eigenvalue in the high-frequency detail image, the probability distribution value corresponding to the eigenvalue can be determined from the target probability distribution channel map, and the image enhancement threshold interval corresponding to the probability distribution value can be determined, and then the image enhancement segmentation intensity value corresponding to the probability distribution value can be determined based on the image enhancement threshold interval.

[0263] After obtaining the image enhancement segmentation strength value corresponding to the probability distribution value, the eigenvalue can be edge enhanced based on the image enhancement segmentation strength value to obtain an image enhancement eigenvalue, such as multiplying the eigenvalue by the image enhancement segmentation strength value (such as magy-1, magy-2, etc.) to obtain an image enhancement eigenvalue. If the image enhancement threshold interval is "less than thry-1", the eigenvalue is multiplied by 1, that is, the eigenvalue is kept unchanged and the eigenvalue is not edge enhanced. If the image enhancement threshold interval is [thry-1, thry-2), the eigenvalue is multiplied by magy-1, magy-1 is a value greater than 1, so that the eigenvalue is edge enhanced. Similarly, other image enhancement segmentation strength values ​​such as magy-2 are greater than 1, which can achieve edge enhancement.

[0264] Then, the target brightness reconstructed image block x_Y_hat_enhanced is determined based on the image enhancement eigenvalue corresponding to each eigenvalue in the high-frequency detail image. For example, for each eigenvalue in the high-frequency detail image, the image enhancement eigenvalue corresponding to the eigenvalue can be obtained in the above manner, and then the image enhancement feature map can be determined based on the image enhancement eigenvalue corresponding to each eigenvalue. For example, the image enhancement eigenvalues ​​corresponding to all eigenvalues ​​can be combined to obtain an image enhancement feature map. Then, the image enhancement feature map is added to the initial brightness reconstructed image block x_Y_hat to obtain the final edge-enhanced image, and the edge-enhanced image is then limited to the image value range to obtain the target brightness reconstructed image block x_Y_hat_enhanced.

[0265] In a possible implementation, the encoder needs to encode multiple image enhancement segment strength values ​​and multiple image enhancement segment thresholds in the bitstream corresponding to the current image block. For this process, the encoder can adopt the following method.

[0266] The encoding end can configure multiple candidate image domain enhancement parameters. For each candidate image domain enhancement parameter, the candidate image domain enhancement parameter may include multiple image enhancement segmentation strength values ​​and multiple image enhancement segmentation thresholds. The cost value corresponding to each candidate image domain enhancement parameter can be determined. Based on the cost value corresponding to each candidate image domain enhancement parameter, the image domain enhancement parameter corresponding to the current image block can be selected from all candidate image domain enhancement parameters, that is, the candidate image domain enhancement parameter with the smallest cost value. The encoding end can encode the image domain enhancement parameter to obtain the code stream corresponding to the current image block.

[0267] For example, for each candidate image domain enhancement parameter, brightness edge enhancement is performed on the initial brightness reconstructed image block x_Y_hat based on the candidate image domain enhancement parameter and the probability distribution parameter to obtain a target brightness reconstructed image block x_Y_hat_enhanced corresponding to the current image block. The brightness edge enhancement process is described in the above embodiment and is not further described here. Based on the target brightness reconstructed image block x_Y_hat_enhanced, a target reconstructed image block x_hat_enhanced can be obtained, and then a cost value corresponding to the candidate image domain enhancement parameter is determined based on the target reconstructed image block x_hat_enhanced. There is no limitation on the method for determining this cost value.

[0268] For example, for each candidate image domain enhancement parameter, the candidate image domain enhancement parameters include magy-1, magy-2, ..., magy-m, thry-1, thry-2, ..., thry-m. Based on the candidate image domain enhancement parameters, a target brightness reconstructed image block x_Y_hat_enhanced can be obtained. Then, based on the target brightness reconstructed image block x_Y_hat_enhanced, a target reconstructed image block x_hat_enhanced is obtained. The distortion index is used to calculate the distortion index value of the target reconstructed image block x_hat_enhanced and the current image block x. After obtaining the distortion index value corresponding to each candidate image domain enhancement parameter, the candidate image domain enhancement parameter corresponding to the minimum distortion index value can be selected as the image domain enhancement parameter corresponding to the current image block, that is, the optimal image domain enhancement parameter. In this way, the encoder can encode the image domain enhancement parameter in the bitstream.

[0269] Example 13: In Example 6 and Example 7, the chroma filtering enhancement module performs chroma filtering enhancement on the initial chroma reconstructed image block x_UV_hat to obtain the target chroma reconstructed image block x_UV_hat_enhanced. The chroma filtering enhancement process of the chroma filtering enhancement module is described below. For example, after obtaining the initial chroma reconstructed image block x_UV_hat corresponding to the current image block x, the chroma filtering enhancement module can also perform chroma filtering enhancement based on the initial luminance reconstructed image block x_Y_hat and the initial chroma reconstructed image block x_UV_hat, thereby obtaining the target chroma reconstructed image block x_UV_hat_enhanced corresponding to the current image block x. The chroma filtering enhancement process is described below.

[0270] First, adaptive upsampling is performed based on the initial chroma reconstructed image block x_UV_hat and the initial luma reconstructed image block x_Y_hat to obtain an upsampled chroma reconstructed image block, and the upsampled chroma reconstructed image block has the same resolution as the initial luma reconstructed image block x_Y_hat. For example, the initial chroma reconstructed image block x_UV_hat is adaptively upsampled to obtain an upsampled chroma reconstructed image block, and the upsampled chroma reconstructed image block has a desired resolution, such as the resolution of the initial luma reconstructed image block x_Y_hat, and the adaptive upsampling process is guided by the initial luma reconstructed image block x_Y_hat.

[0271] Then, cross-channel filtering is performed based on the upsampled chroma reconstructed image block and the initial luminance reconstructed image block x_Y_hat to obtain a cross-channel filtered chroma reconstructed image block. For example, after obtaining the upsampled chroma reconstructed image block, the upsampled chroma reconstructed image block will be subjected to cross-channel filtering to obtain a first enhanced chroma reconstructed image block (i.e., a cross-channel filtered chroma reconstructed image block), and the cross-channel filtering process is guided by the initial luminance reconstructed image block x_Y_hat.

[0272] As shown in FIG6D , after performing a wavelet transform (WT) on the upsampled chroma reconstructed image block and a wavelet transform on the initial luminance reconstructed image block x_Y_hat, they are connected in the channel dimension and the features after the channel dimension connection are input into the residual block network (Residual Block Network), so that the upsampled chroma reconstructed image block is enhanced. Finally, a cross-channel filtered chroma reconstructed image block is obtained by inverse wavelet transform. For example, the upsampled chroma reconstructed image block and the initial luminance reconstructed image block x_Y_hat can be wavelet transformed; the upsampled chroma reconstructed image block after wavelet transform and the initial luminance reconstructed image block after wavelet transform are connected in the channel dimension; the features after the channel dimension connection are input into the residual block network, and the features output by the residual block network are inverse wavelet transformed to obtain the cross-channel filtered chroma reconstructed image block. In this way, the quality of image reconstruction can be improved.

[0273] Then, nonlinear chroma filtering is performed based on the cross-channel filtered chroma reconstructed image block and the initial luminance reconstructed image block x_Y_hat to obtain the nonlinear chroma filtered chroma reconstructed image block, and the nonlinear chroma filtered chroma reconstructed image block is the target chroma reconstructed image block x_UV_hat_enhanced. For example, after obtaining the cross-channel filtered chroma reconstructed image block, the cross-channel filtered chroma reconstructed image block will be subjected to nonlinear chroma filtering to obtain the second enhanced chroma reconstructed image block, and the nonlinear chroma filtering process is guided by the initial luminance reconstructed image block x_Y_hat. After obtaining the second enhanced chroma reconstructed image block, this chroma reconstructed image block is the target chroma reconstructed image block x_UV_hat_enhanced.

[0274] As shown in Figure 6E , the initial luminance reconstructed image block x_Y_hat can be downsampled. The downsampled luminance reconstructed image block is then concatenated with the cross-channel filtered chrominance reconstructed image block in the channel dimension, where the resolution of the downsampled luminance reconstructed image block is the same as the resolution of the cross-channel filtered chrominance reconstructed image block. The channel-wise concatenated features are then input into a nonlinear filter, which then outputs the target chrominance reconstructed image block x_UV_hat_enhanced.

[0275] In the above process, cross-channel filtering is performed first and then nonlinear chroma filtering. In order to obtain the target chroma reconstructed image block x_UV_hat_enhanced, nonlinear chroma filtering can be performed first and then cross-channel filtering, or only cross-channel filtering can be performed (without nonlinear chroma filtering), or only nonlinear chroma filtering can be performed (without cross-channel filtering). There is no restriction on this, as long as x_UV_hat_enhanced can be obtained through filtering.

[0276] Example 14: In Example 6 and Example 7, the chroma filtering enhancement module performs chroma filtering enhancement on the initial chroma reconstructed image block x_UV_hat to obtain the target chroma reconstructed image block x_UV_hat_enhanced. The chroma filtering enhancement process of the chroma filtering enhancement module is described below. For example, after obtaining the initial chroma reconstructed image block x_UV_hat corresponding to the current image block x, the chroma filtering enhancement module can perform chroma filtering enhancement based on the target luminance reconstructed image block x_Y_hat_enhanced and the initial chroma reconstructed image block x_UV_hat to obtain the target chroma reconstructed image block x_UV_hat_enhanced corresponding to the current image block x. The chroma filtering enhancement process is described below.

[0277] First, adaptive upsampling is performed based on the initial chroma reconstructed image block x_UV_hat and the target luma reconstructed image block x_Y_hat_enhanced to obtain an upsampled chroma reconstructed image block. The upsampled chroma reconstructed image block has the same resolution as the target luma reconstructed image block x_Y_hat_enhanced. For example, the initial chroma reconstructed image block x_UV_hat is adaptively upsampled to obtain an upsampled chroma reconstructed image block with the same resolution as the target luma reconstructed image block x_Y_hat_enhanced, and the adaptive upsampling process is guided by the target luma reconstructed image block x_Y_hat_enhanced.

[0278] Then, cross-channel filtering is performed based on the upsampled chroma reconstructed image block and the target luminance reconstructed image block x_Y_hat_enhanced to obtain the cross-channel filtered chroma reconstructed image block. For example, after obtaining the upsampled chroma reconstructed image block, the upsampled chroma reconstructed image block will be subjected to cross-channel filtering to obtain the chroma reconstructed image block after the first enhancement (i.e., the cross-channel filtered chroma reconstructed image block), and the cross-channel filtering process is guided by the target luminance reconstructed image block x_Y_hat_enhanced. For example, after wavelet transforming the upsampled chroma reconstructed image block and the target luminance reconstructed image block x_Y_hat_enhanced, they are connected in the channel dimension, and the features after the channel dimension connection are input into the residual block network, so that the upsampled chroma reconstructed image block is enhanced, and the cross-channel filtered chroma reconstructed image block is obtained by inverse wavelet transform. For example, a wavelet transform can be performed on the upsampled chroma reconstructed image block and the target luminance reconstructed image block x_Y_hat_enhanced; the wavelet-transformed upsampled chroma reconstructed image block and the wavelet-transformed target luminance reconstructed image block are concatenated in the channel dimension; the channel-dimensional concatenated features are input into a residual block network, and the features output by the residual block network are inversely wavelet transformed to obtain a cross-channel filtered chroma reconstructed image block. This can improve the quality of image reconstruction.

[0279] Then, nonlinear chroma filtering is performed on the cross-channel filtered chroma reconstructed image block and the target luma reconstructed image block x_Y_hat_enhanced to obtain a nonlinear chroma filtered chroma reconstructed image block, which is the target chroma reconstructed image block x_UV_hat_enhanced. For example, after obtaining the cross-channel filtered chroma reconstructed image block, the cross-channel filtered chroma reconstructed image block is subjected to nonlinear chroma filtering to obtain a second enhanced chroma reconstructed image block (target chroma reconstructed image block x_UV_hat_enhanced), where the nonlinear chroma filtering process is guided by the target luma reconstructed image block x_Y_hat_enhanced. For example, the target luma reconstructed image block x_Y_hat_enhanced is downsampled, and the downsampled luma reconstructed image block is concatenated with the cross-channel filtered chroma reconstructed image block in the channel dimension, where the resolution of the downsampled luma reconstructed image block is the same as the resolution of the cross-channel filtered chroma reconstructed image block. The channel dimension is connected and the features are input into the nonlinear filter, and then the target chroma reconstructed image block x_UV_hat_enhanced is output.

[0280] In the above process, cross-channel filtering is performed first and then nonlinear chroma filtering. In order to obtain the target chroma reconstructed image block x_UV_hat_enhanced, nonlinear chroma filtering can be performed first and then cross-channel filtering, or only cross-channel filtering can be performed (without nonlinear chroma filtering), or only nonlinear chroma filtering can be performed (without cross-channel filtering). There is no restriction on this, as long as x_UV_hat_enhanced can be obtained through filtering.

[0281] Example 15: For Example 5-Example 14, if a feature domain enhancement module is added before the synthetic transformation network, then the feature domain enhancement module can perform feature domain enhancement on the luminance component, can also perform feature domain enhancement on the chrominance component, and can also perform feature domain enhancement on the luminance component and the chrominance component at the same time. In this embodiment, the feature domain enhancement of the luminance component is taken as an example. In order to perform feature domain enhancement on the luminance component, the initial reconstructed feature y_hat corresponding to the luminance component of the current image block can be feature domain enhanced based on the feature domain enhancement parameters and the probability distribution parameters to obtain the enhanced reconstructed feature y_hat_enhanced corresponding to the luminance component. The feature domain enhancement process can be referred to Example 5-Example 14 and will not be repeated here.

[0282] For Example 5 to Example 14, if a filtering enhancement module is added after the synthetic transformation network, then the filtering enhancement module can perform brightness edge enhancement on the brightness component, and can also perform chroma filtering enhancement on the chroma component, and can also perform edge enhancement on the brightness component and the chroma component at the same time. In this embodiment, the edge enhancement of the brightness component and the chroma component at the same time is taken as an example. For example, in order to perform brightness edge enhancement on the brightness component, the initial brightness reconstructed image block x_Y_hat corresponding to the brightness component of the current image block can be enhanced based on the image domain enhancement parameters and the probability distribution parameters to obtain the target brightness reconstructed image block x_Y_hat_enhanced. The brightness edge enhancement process can refer to Example 5 to Example 14 and will not be repeated here. In addition, in order to perform chroma filtering enhancement on the chroma component, the initial chroma reconstructed image block x_UV_hat can be enhanced by chroma filtering to obtain the target chroma reconstructed image block x_UV_hat_enhanced. The chroma filtering enhancement process can refer to Example 5 to Example 14 and will not be repeated here.

[0283] When performing chroma filtering enhancement on the initial chroma reconstructed image block x_UV_hat, a target probability distribution channel map for the chroma component can also be obtained based on the probability distribution parameters of the luma component. For example, a significant probability distribution channel map is selected from all probability distribution channel maps of the luma component's probability distribution parameters and upsampled to obtain a target probability distribution channel map for the chroma component. The size of the target probability distribution channel map is the same as that of the initial chroma reconstructed image block x_UV_hat for the chroma component. For example, the significant probability distribution channel map can be upsampled using a nearest neighbor upsampling method to obtain the target probability distribution channel map for the chroma component. For the nearest neighbor upsampling method, a pixel in the original low-resolution image (i.e., the significant probability distribution channel map) is selected as the center point of the corresponding region in the target high-resolution image (i.e., the target probability distribution channel map). The value of the nearest pixel to this pixel in the original low-resolution image is used as the value of the corresponding pixel in the target high-resolution image. This process is repeated until all pixels in the target high-resolution image have been assigned a value.

[0284] After obtaining the target probability distribution channel map of the chroma component, for each probability distribution value in the target probability distribution channel map, an image enhancement segment intensity value corresponding to the probability distribution value is determined based on the image enhancement threshold interval corresponding to the probability distribution value. Based on the image enhancement segment intensity value corresponding to each probability distribution value, chroma filtering enhancement is performed on the initial chroma reconstructed image block x_UV_hat corresponding to the chroma component to obtain the target chroma reconstructed image block x_UV_hat_enhanced. The chroma filtering enhancement process can be referred to in Example 12, and the relevant operations can be performed on the chroma component.

[0285] Embodiment 16: This embodiment proposes a decoding method, which may include the following steps S31-S38.

[0286] Step S31, decode the feature domain enhancement parameters, image domain enhancement parameters and important channel identifier (i.e., important channel number important_channel) from Bitstream#3 (also called the third code stream, the third code stream can also be called the feature enhancement header information code stream) corresponding to the current image block. Among them, the feature domain enhancement parameters may include multiple edge enhancement segment strength values ​​(denoted as magl-1, magl-2, ..., magl-n), multiple edge enhancement segment thresholds (denoted as thrl-1, thrl-2, ..., thrl-n), and a stretching parameter value ρ; the image domain enhancement parameters may include multiple image enhancement segment strength values ​​(denoted as magy-1, magy-2, ..., magy-m), and multiple image enhancement segment thresholds (denoted as thry-1, thry-2, ..., thry-m). The feature domain enhancement parameters may also include the number n of edge enhancement segment strength values, and the image domain enhancement parameters may also include the number m of image enhancement segment strength values.

[0287] Step S32: The input features of the feature domain enhancement module are the initial reconstruction feature y_hat and the probability distribution parameter sigma. The initial reconstruction feature y_hat can be a C L xH L xW L The three-dimensional tensor, C L is the number of channels in the feature domain, namely the Latent domain, H L is the characteristic height of the Latent domain, W L is the characteristic width of the Latent domain. The probability distribution parameter sigma can also be of size C L xH L xW L When the channel of the initial reconstruction feature y_hat is [1, 2, ..., C L ], the shape corresponding to this channel, that is, the size is H L xW L The two-dimensional tensor y_hat_ch is called the feature channel map. When the channel of the probability distribution parameter sigma is [1, 2, ..., C L ], the channel corresponds to the shape, that is, the size is H L xW L The two-dimensional tensor sigma_ch is called the probability distribution channel map.

[0288] Step S33, the initial reconstruction feature y_hat has a total of C L feature channel graphs, the probability distribution parameter sigma has C L probability distribution channel graph, C L The probability distribution channel graph and C LThe probability distribution channel map is represented as sigma_ch. Feature adaptive edge enhancement is performed on the feature channel map where channel ch is an important channel marker important_channel, and feature adaptive stretching is performed on the feature channel map where channel ch is a non-important channel marker.

[0289] Step S34, for the feature adaptive edge enhancement process, the input data is the important feature channel map y_hat_ch, the important probability distribution channel map sigma_ch, multiple edge enhancement segmentation strength values ​​(magl-1, magl-2, ..., magl-n), and multiple edge enhancement segmentation thresholds (thrl-1, thrl-2, ..., thrl-n). Based on the above input data, the important feature channel map y_hat_ch can be feature-adaptively enhanced to obtain a reconstructed feature map after feature adaptive edge enhancement. The reconstructed feature map is called the first reconstructed feature y_hat_sharp. The feature adaptive edge enhancement process is described in Example 10 and will not be repeated here.

[0290] Step S35: for the feature adaptive stretching process, the input data is the non-important feature channel map (the remaining C L -1 non-important feature channel map), non-important probability distribution channel map (the remaining C L -1 non-important probability distribution channel map), stretching parameter value ρ, based on the above input data, feature adaptive stretching can be performed on each non-important feature channel map to obtain a reconstructed feature map after feature adaptive stretching, and the reconstructed feature map is called the second reconstructed feature y_hat_scale. The feature adaptive stretching process is shown in Example 11 and will not be repeated here. Among them, the size of the non-important feature channel map subjected to feature adaptive stretching is (C L -1)xH L xW L , the size of the corresponding probability distribution parameter sigma is also (C L -1)xH L xW L Each element in this three-dimensional tensor can be subjected to the following adaptive stretching algorithm to obtain the second reconstructed feature after stretching: y_hat_scale = y_hat + ρ*clip3(sigma*y_hat, -0.5, 0.5), where clip3 is a limit operation. The second reconstructed feature after the feature adaptive stretching algorithm is y_hat_scale.

[0291] Step S36, the second reconstruction feature y_hat_scale corresponds to the non-important channel enhancement of the initial reconstruction feature y_hat, the first reconstruction feature y_hat_sharp corresponds to the important channel enhancement of the initial reconstruction feature y_hat, and the first reconstruction feature y_hat_sharp and the second reconstruction feature y_hat_scale are merged to obtain the enhanced reconstruction feature y_hat_enhanced after image enhancement.

[0292] Step S37, the enhanced reconstruction feature y_hat_enhanced after image enhancement is input to the synthesis transformation network to obtain the initial reconstructed image block x_hat, the initial reconstructed image block x_hat is a two-dimensional tensor with a size of H×W, the important probability distribution channel map sigma_channel of the probability distribution parameter sigma is upsampled to the same size as the initial reconstructed image block x_hat, and the upsampled probability distribution channel map is recorded as the target probability distribution channel map sigma_channel_upscale.

[0293] Step S38, for the filtering enhancement process, the input data of the filtering enhancement module are the initial reconstructed image block x_hat, the target probability distribution channel map sigma_channel_upscale, multiple image enhancement segmentation intensity values ​​(magy-1, magy-2, ..., magy-m), and multiple image enhancement segmentation thresholds (thry-1, thry-2, ..., thry-m). Based on the above input data, the filtering enhancement module can perform filtering enhancement on the initial reconstructed image block x_hat to obtain the target reconstructed image block x_hat_enhanced corresponding to the current image block x. For the filtering enhancement process, please refer to Example 12-Example 14 and will not be repeated here.

[0294] Example 17: This example proposes an encoding method, which may include the following steps S41-S45.

[0295] Step S41: Based on C L feature channel maps y_hat and C L A probability distribution channel map sigma is used to determine the important channel identifier corresponding to the important feature channel map (the important channel identifier is also called the important channel number important_channel).

[0296] For example, slice y_hat and sigma along the channel dimension, and get the current y_hat_channel and sigma_channel after slicing. Calculate the bits_per_ch of each feature channel map through these two tensors. See the following expression and loop the above process C LThe bits_per_ch of each feature channel map is obtained, and the channel number corresponding to the maximum bits_per_ch is selected as the important channel identifier.

[0297] In the above expression, bits_per_ch can be used to represent the number of consumed bits corresponding to the feature channel graph ch, y_hat_ch(i,j) can be used to represent the eigenvalue of the feature point (i,j) in the feature channel graph ch, sigma_ch(i,j) can be used to represent the probability distribution value of the feature point (i,j) in the probability distribution channel graph ch, and Φ(.) can be the standard normal distribution cumulative function.

[0298]

[0299] Step S42: Determine a stretch parameter value ρ corresponding to the feature adaptive stretching process. For example, select N1 candidate stretch parameter values ​​ρ, obtain the enhanced reconstruction features and target reconstructed image block x_hat_enhanced corresponding to each candidate stretch parameter value ρ, calculate the distortion index value of the target reconstructed image block x_hat_enhanced and the current image block x using the distortion index, and select the candidate stretch parameter value ρ corresponding to the minimum distortion index value as the optimal stretch parameter value ρ corresponding to the feature adaptive stretching process.

[0300] Step S43: Determine feature domain enhancement parameters corresponding to the feature adaptive edge enhancement process, where the feature domain enhancement parameters include multiple edge enhancement segmentation strength values ​​(mag1-1, magl-2, ..., magl-n) and multiple edge enhancement segmentation thresholds (thrl-1, thrl-2, ..., thrl-n). For example, select N2 candidate feature domain enhancement parameters, obtain enhanced reconstruction features and target reconstructed image block x_hat_enhanced corresponding to each candidate feature domain enhancement parameter, calculate the distortion index value of the target reconstructed image block x_hat_enhanced and the current image block x using the distortion index, and select the candidate feature domain enhancement parameter corresponding to the minimum distortion index value as the optimal feature domain enhancement parameter corresponding to the feature adaptive edge enhancement process.

[0301] Step S44: Determine image domain enhancement parameters corresponding to the image domain enhancement process. The image domain enhancement parameters may include multiple image enhancement segmentation intensity values ​​(magy-1, magy-2, ..., magy-m) and multiple image enhancement segmentation thresholds (thry-1, thry-2, ..., thry-m). For example, select N3 candidate image domain enhancement parameters, obtain enhanced reconstruction features and target reconstructed image block x_hat_enhanced corresponding to each candidate image domain enhancement parameter, calculate the distortion index value of the target reconstructed image block x_hat_enhanced and the current image block x using the distortion index, and select the candidate image domain enhancement parameter corresponding to the minimum distortion index value as the optimal image domain enhancement parameter corresponding to the image domain enhancement process.

[0302] Step S45: Encode the important channel identifiers corresponding to the important feature channel map, the optimal stretch parameter value ρ, the optimal feature domain enhancement parameters, and the optimal image domain enhancement parameters into the header information bitstream (the third bitstream #Bitstreams3 corresponding to the current image block). It should be noted that the feature domain enhancement module and the image domain enhancement module do not change #Bitstreams1 and #Bitstreams2. However, the image reconstruction at both the encoder and decoder will pass through the enhancement module, so the reconstructed image will remain consistent at both the encoder and decoder.

[0303] Example 18: The method for image adaptive edge enhancement (i.e., the process in which the filtering enhancement module performs filtering enhancement on the initial reconstructed image block x_hat to obtain the target reconstructed image block x_hat_enhanced) can be a non-edge enhancement mask edge enhancement algorithm, also known as a USM edge enhancement (Unsharp Masking edge enhancement) algorithm, which includes the following steps S51-S54.

[0304] Step S51: Perform a two-dimensional convolution operation on the original reconstructed image and the Gaussian blur convolution kernel to obtain a Gaussian blurred image.

[0305] Step S52: Subtract the Gaussian blurred image from the original reconstructed image to obtain a high-frequency detail image.

[0306] Step S53: multiply the high-frequency detail image by an edge enhancement coefficient (ie, the image enhancement segment intensity value corresponding to the probability distribution value corresponding to the eigenvalue), and add it to the original reconstructed image to obtain a final edge-enhanced image.

[0307] Step S54: Limit the edge-enhanced image to the value range of the image.

[0308] Exemplarily, the original reconstructed image is the initial reconstructed image block x_hat. After limiting the edge-enhanced image to the value range of the image, the target reconstructed image block x_hat_enhanced can be obtained. The process can be referred to the above embodiment.

[0309] Example 19: The method of feature adaptive edge enhancement (i.e., the process in which the feature domain enhancement module performs feature adaptive edge enhancement on the important feature channel map based on the feature domain enhancement parameters and the important probability distribution channel map to obtain the first reconstructed feature) can be a non-edge enhancement mask edge enhancement algorithm, that is, a USM edge enhancement algorithm, which includes the following steps S61-S68.

[0310] Step S61: Subtract the mean from the important channel feature map y_hat_channel and divide it by the variance to obtain a normalized feature map.

[0311] Step S62: multiply the normalized feature map by 0.1 and add 0.5, and limit the feature value to between 0 and 1.

[0312] Step S63: Perform a two-dimensional convolution operation on the normalized feature map and the Gaussian blur convolution kernel to obtain a Gaussian blurred image.

[0313] Step S64: Subtract the Gaussian blurred image from the original reconstructed image to obtain a high-frequency detail image.

[0314] Step S65: multiply the high-frequency detail image by an edge enhancement coefficient (ie, the edge enhancement segment strength value corresponding to the probability distribution value corresponding to the eigenvalue), and add the result to the normalized feature map to obtain an edge enhancement feature map.

[0315] Step S66: Limit the feature value of the edge enhancement feature map to between 0 and 1.

[0316] Step S67: Subtract 0.5 from the edge-enhanced feature map and divide the result by 10 to obtain a normalized feature map after edge enhancement.

[0317] Step S68: multiply the normalized feature map after edge enhancement by the variance and add the mean to obtain the important channel feature edge enhanced feature map y_hat_shap corresponding to the important channel feature map y_hat_channel, that is, the first reconstructed feature.

[0318] Exemplarily, the original reconstructed image is the important feature channel map y_hat_channel. The process can be referred to Example 10.

[0319] Example 20: During the decoding process, the initial chrominance reconstructed image block x_UV_hat undergoes adaptive upsampling, and the chrominance reconstructed image becomes the required resolution size. This process is guided by the initial luminance reconstructed image block x_Y_hat. The chrominance reconstructed image after adaptive upsampling will undergo cross-channel filtering to obtain a chrominance reconstructed image after the first enhancement. This process is guided by the initial luminance reconstructed image block x_Y_hat. The chrominance reconstructed image after the first enhancement will undergo nonlinear chrominance filtering to obtain a second chrominance image enhancement and output the final chrominance enhanced image x_UV_hat_enhanced. In addition, the initial luminance reconstructed image block x_Y_hat undergoes luminance edge enhancement to output the target luminance reconstructed image block x_Y_hat_enhanced.

[0320] For cross-channel filtering enhancement, the chroma reconstructed image and the initial luminance reconstructed image block x_Y_hat undergo wavelet transformation and are concatenated in the channel dimension. After passing through a residual block network, the chroma reconstructed image is enhanced, and finally, a new chroma reconstructed image is obtained through an inverse wavelet transform. And / or, for nonlinear chroma filtering enhancement, the initial luminance reconstructed image block x_Y_hat is downsampled to the same resolution as the chroma reconstructed image, then concatenated in the channel dimension and passed through a nonlinear filter to obtain the target chroma enhanced image x_UV_hat_enhanced.

[0321] Example 21: During the decoding process, the filtering enhancement submodule can extract important channel identifiers from the bitstream.

[0322] Example 22: During the decoding process, for the Gaussian blur convolution kernel involved in each embodiment, the denominator of each weight in the Gaussian blur convolution kernel is designed to be a power of 2. For example, an available weight design is shown below:

[0323] Example 23: During the encoding process, the important channel identifier can be determined based on the feature variance scale_hat. The method is as follows:

[0324] LinearToLog is a mapping table that can map the feature variance scale_hat to the logarithmic domain feature variance logarithmic_scale. For example, logarithmic_scale = LinearToLog(scale_hat).

[0325] Slice along the channel dimension to obtain the logarithmic domain feature variance logarithmic_scale_channel corresponding to a certain feature channel.

[0326] Based on logarithmic_scale_channel, calculate the logarithmic variance and sum_per_channel corresponding to each feature channel:

[0327] The above process is repeated for a total of C channels of logarithmic_scale. L times, that is, the logarithmic variance and sum_per_channel corresponding to each feature channel are obtained, and then the channel number corresponding to the maximum sum_per_channel is selected as the important channel identifier important_channel, and the important channel identifier important_channel is also called the important channel number.

[0328] As can be seen from the above technical solutions, in the embodiment of the present application, an end-to-end video image compression method is proposed, which can realize the encoding and decoding of video images based on a neural network, and achieve the purpose of improving the encoding efficiency and decoding efficiency by combining image domain enhancement parameters and probability distribution parameters. Combined with the network structure design and the header information code stream (such as the third code stream), the neural network can effectively guarantee the quality of the reconstructed image block while maintaining low complexity, thereby achieving the purpose of improving the encoding performance and decoding performance and reducing the complexity. The image quality of the features is enhanced by using image domain enhancement parameters and probability distribution parameters. The encoding end does not directly change the feature information, but encodes the image domain enhancement parameters into the header information code stream. The decoding end enhances the reconstructed image blocks through the image domain enhancement parameters, thereby improving the encoding and decoding performance and improving the reconstructed image quality. Channel specificity can be considered, and for finer-grained adaptive scaling of probability distribution parameters, edge enhancement can be introduced, which has a greater improvement on indicators such as VMAF (Video Multi-Method Assessment Fusion). The characteristics of the bottleneck layer tensor in the end-to-end image coding framework are utilized. The importance of the channel is judged by the bit rate consumed by each channel in the feature domain, and different feature domain enhancement methods are performed respectively. The probability distribution parameters of important channels in the feature domain are used to adaptively obtain edge enhancement, thereby improving the quality of the encoded and reconstructed image, and the subjective image quality evaluation indicators are also improved.

[0329] Illustratively, the above-mentioned embodiments can be implemented individually or in combination. For example, each embodiment in Embodiment 1 to Embodiment 23 can be implemented individually, and at least two embodiments in Embodiment 1 to Embodiment 23 can be implemented in combination.

[0330] Illustratively, in the above embodiments, the content of the encoding end can also be applied to the decoding end, that is, the decoding end can be processed in the same way, and the content of the decoding end can also be applied to the encoding end, that is, the encoding end can be processed in the same way.

[0331] Based on the same application concept as the above method, a decoding device is also proposed in the embodiment of the present application. The device is applied to the decoding end, and the device includes: a memory, which is configured to store video data; a decoder, which is configured to implement the decoding methods in the above embodiments 1 to 23, that is, the processing flow of the decoding end.

[0332] Based on the same application concept as the above method, an encoding device is also proposed in an embodiment of the present application. The device is applied to the encoding end, and the device includes: a memory, which is configured to store video data; an encoder, which is configured to implement the encoding method in the above embodiments 1 to 23, that is, the processing flow of the encoding end.

[0333] Based on the same application concept as the above-mentioned method, the decoding end device (also referred to as a video decoder) provided in the embodiments of the present application, from a hardware perspective, has a hardware architecture diagram as shown in FIG7A . The decoding end device includes a processor 711 and a machine-readable storage medium 712 , wherein the machine-readable storage medium 712 stores machine-executable instructions that can be executed by the processor 711 ; the processor 711 is configured to execute the machine-executable instructions to implement the decoding methods described in the above-mentioned embodiments 1 to 23 of the present application.

[0334] For example, in one possible embodiment, when the processor 711 executes machine-executable instructions, it is used to implement: decoding the code stream corresponding to the current image block to obtain the coefficient hyperparameter features corresponding to the current image block; determining probability distribution parameters based on the coefficient hyperparameter features, decoding the code stream corresponding to the current image block based on the probability distribution parameters to obtain the initial reconstruction features corresponding to the current image block; determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; and performing luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block.

[0335] Based on the same application concept as the above-mentioned method, the encoding end device (also referred to as a video encoder) provided in the embodiments of the present application, from a hardware perspective, has a hardware architecture diagram as shown in FIG7B . The encoding end device includes a processor 721 and a machine-readable storage medium 722 , wherein the machine-readable storage medium 722 stores machine-executable instructions that can be executed by the processor 721 ; the processor 721 is configured to execute the machine-executable instructions to implement the encoding methods described in the above-mentioned embodiments 1 to 23 of the present application.

[0336] For example, in one possible embodiment, the processor 721 executes machine-executable instructions to implement: encoding the coefficient hyperparameter features corresponding to the current image block; determining the probability distribution parameters based on the coefficient hyperparameter features, and encoding the initial image features corresponding to the current image block based on the probability distribution parameters; determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features corresponding to the initial image features; wherein the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; for each candidate image domain enhancement parameter, performing luminance edge enhancement on the initial luminance reconstructed image block based on the candidate image domain enhancement parameter and the probability distribution parameters to obtain a target luminance reconstructed image block; determining the cost value corresponding to the candidate image domain enhancement parameter based on the target luminance reconstructed image block; selecting the image domain enhancement parameter corresponding to the current image block from all candidate image domain enhancement parameters based on the cost value corresponding to each candidate image domain enhancement parameter, and encoding the image domain enhancement parameter.

[0337] Based on the same application concept as the above-mentioned method, an embodiment of the present application provides an electronic device. The electronic device includes a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor. The processor is configured to execute the machine-executable instructions to implement the decoding method or encoding method described in Embodiments 1 to 23 of the present application.

[0338] Based on the same application concept as the above method, an embodiment of the present application also provides a machine-readable storage medium, on which a number of computer instructions are stored. When the computer instructions are executed by a processor, the method disclosed in the above embodiment of the present application can be implemented, such as the decoding method or encoding method in the above embodiments.

[0339] Based on the same application concept as the above method, an embodiment of the present application further provides a computer application, which, when executed by a processor, can implement the decoding method or encoding method disclosed in the above embodiment of the present application.

[0340] Based on the same application concept as the above method, an embodiment of the present application also provides a computer program product, including computer program instructions. When the computer program instructions are executed by a processor, the decoding method or encoding method disclosed in the above embodiment of the present application can be implemented.

[0341] Based on the same application concept as the above method, an embodiment of the present application also proposes a decoding device, which can be applied to a decoding end (also called a video decoder). The decoding device includes: a decoding module, which is used to decode the code stream corresponding to the current image block to obtain the coefficient hyperparameter features corresponding to the current image block; determine the probability distribution parameters based on the coefficient hyperparameter features, and decode the code stream corresponding to the current image block based on the probability distribution parameters to obtain the initial reconstruction features corresponding to the current image block; a determination module, which is used to determine the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features; wherein the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; an enhancement module, which is used to perform luminance edge enhancement on the initial luminance reconstructed image block to obtain a target luminance reconstructed image block.

[0342] Exemplarily, the enhancement module is further used to perform chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block; the decoding device also includes: a generation module, used to generate a target reconstructed image block based on the target luminance reconstructed image block and the target chroma reconstructed image block.

[0343] Exemplarily, the enhancement module performs brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block, which is specifically used to: decode the code stream corresponding to the current image block to obtain image domain enhancement parameters corresponding to the current image block; and perform brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters to obtain a target brightness reconstructed image block corresponding to the current image block.

[0344] Exemplarily, the image domain enhancement parameters include multiple image enhancement segmentation intensity values ​​and multiple image enhancement segmentation thresholds, the multiple image enhancement segmentation thresholds constitute multiple image enhancement threshold intervals, and the multiple image enhancement threshold intervals correspond one-to-one to the multiple image enhancement segmentation intensity values; the enhancement module performs brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameters and the probability distribution parameters, and obtains the target brightness reconstructed image block corresponding to the current image block, which is specifically used to: obtain a target probability distribution channel map based on the probability distribution parameters; if the target probability distribution channel map includes multiple probability distribution values, then for each probability distribution value, determine the image enhancement segmentation intensity value corresponding to the probability distribution value based on the image enhancement threshold interval corresponding to the probability distribution value; perform brightness edge enhancement on the initial brightness reconstructed image block based on the image enhancement segmentation intensity value corresponding to each probability distribution value, and obtain a target brightness reconstructed image block.

[0345] Exemplarily, the enhancement module performs brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block, which is specifically used to: decode the code stream corresponding to the current image block to obtain an important channel identifier corresponding to the current image block; if the probability distribution parameters include multiple probability distribution channel maps, then select the probability distribution channel map corresponding to the important channel identifier as the important probability distribution channel map from the multiple probability distribution channel maps included in the probability distribution parameters; upsample the important probability distribution channel map to obtain the target probability distribution channel map; and perform brightness edge enhancement on the initial brightness reconstructed image block according to the target probability distribution channel map to obtain a target brightness reconstructed image block corresponding to the current image block.

[0346] Exemplarily, the enhancement module performs brightness edge enhancement on the initial brightness reconstructed image block based on the image enhancement segmented intensity value corresponding to each probability distribution value to obtain the target brightness reconstructed image block corresponding to the current image block, and is specifically used to: generate a high-frequency detail image based on the initial brightness reconstructed image block; for each eigenvalue in the high-frequency detail image, perform brightness edge enhancement on the eigenvalue based on the image enhancement segmented intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an image enhancement eigenvalue; and determine the target brightness reconstructed image block based on the image enhancement eigenvalue corresponding to each eigenvalue.

[0347] Exemplarily, when the enhancement module generates a high-frequency detail image based on the initial brightness reconstructed image block, it is specifically used to: perform a convolution operation on the initial brightness reconstructed image block and a Gaussian blur convolution kernel to obtain a Gaussian blur image; subtract the Gaussian blur image from the initial brightness reconstructed image block to obtain the high-frequency detail image; wherein, for each value in the Gaussian blur convolution kernel, the denominator of the value is a power of 2.

[0348] Exemplarily, the enhancement module performs chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block, specifically for: performing adaptive upsampling based on the initial chroma reconstructed image block and the initial luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, the upsampled chroma reconstructed image block having the same resolution as the initial luminance reconstructed image block; performing a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

[0349] Exemplarily, when the enhancement module performs a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block, it is specifically used to: perform cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or, perform nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or, perform cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and perform nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or, perform nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and perform cross-channel filtering on the nonlinear chroma filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0350] Exemplarily, the enhancement module performs chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block, which is specifically used to: perform adaptive upsampling based on the initial chroma reconstructed image block and the target luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, and the upsampled chroma reconstructed image block has the same resolution as the target luminance reconstructed image block; perform a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

[0351] Exemplarily, the enhancement module performs a filtering operation on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block, and is specifically used to: perform cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, perform nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, perform cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block, and perform nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, perform nonlinear chroma filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the nonlinear chroma filtered chroma reconstructed image block, and perform cross-channel filtering on the nonlinear chroma filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block.

[0352] Exemplarily, the enhancement module performs cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block, and is specifically used to: perform wavelet transform on the upsampled chroma reconstructed image block, and perform wavelet transform on the initial luminance reconstructed image block; connect the upsampled chroma reconstructed image block after wavelet transform and the initial luminance reconstructed image block after wavelet transform in the channel dimension; input the features after channel dimension connection into the residual block network, and perform inverse wavelet transform on the features output by the residual block network to obtain the cross-channel filtered chroma reconstructed image block. Image block; the enhancement module performs cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block, and is specifically used to: perform wavelet transform on the upsampled chroma reconstructed image block, and perform wavelet transform on the target luminance reconstructed image block; connect the upsampled chroma reconstructed image block after wavelet transform and the target luminance reconstructed image block after wavelet transform in the channel dimension; input the features after channel dimension connection into the residual block network, and perform inverse wavelet transform on the features output by the residual block network to obtain the cross-channel filtered chroma reconstructed image block.

[0353] Exemplarily, when the determination module determines the initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature, it is specifically used to: input the initial reconstruction feature to a synthetic transformation network to obtain the initial reconstructed image block; or, decode the code stream corresponding to the current image block to obtain a feature domain enhancement parameter corresponding to the current image block; perform feature domain enhancement on the initial reconstruction feature based on the feature domain enhancement parameter and the probability distribution parameter to obtain an enhanced reconstruction feature; and input the enhanced reconstruction feature to a synthetic transformation network to obtain the initial reconstructed image block.

[0354] Exemplarily, the decoding module is further used to decode the code stream corresponding to the current image block to obtain the important channel identifier corresponding to the current image block; wherein the initial reconstruction feature includes C feature channel maps, the probability distribution parameters include C probability distribution channel maps, the C probability distribution channel maps correspond to the C feature channel maps one-to-one, C is a positive integer, the feature channel map corresponding to the important channel identifier is used as the important feature channel map, the remaining feature channel maps in the C feature channel maps are used as non-important feature channel maps, the probability distribution channel map corresponding to the important feature channel map is used as the important probability distribution channel map, and the remaining probability distribution channels in the C probability distribution channel maps are used as the non-important feature channel maps. The image is used as a non-important probability distribution channel image; the determination module performs feature domain enhancement on the initial reconstructed feature based on the feature domain enhancement parameters and the probability distribution parameters, and when obtaining the enhanced reconstruction feature, it is specifically used to: perform feature adaptive edge enhancement on the important feature channel image based on the feature domain enhancement parameters and the important probability distribution channel image, and obtain a first reconstruction feature after feature adaptive edge enhancement; perform feature adaptive stretching on the non-important feature channel image based on the feature domain enhancement parameters and the non-important probability distribution channel image, and obtain a second reconstruction feature after feature adaptive stretching; and generate the enhanced reconstruction feature based on the first reconstruction feature and the second reconstruction feature.

[0355] Based on the same application concept as the above method, an embodiment of the present application also proposes an encoding device, which is applied to an encoding end (also called a video encoder), and the device includes: an encoding module, which is used to encode the coefficient hyperparameter features corresponding to the current image block; determine the probability distribution parameters based on the coefficient hyperparameter features, and encode the initial image features corresponding to the current image block based on the probability distribution parameters; a determination module, which is used to determine the initial reconstructed image block corresponding to the current image block based on the initial reconstruction features corresponding to the initial image features; the initial reconstructed image block includes an initial luminance reconstructed image block and an initial chrominance reconstructed image block; an enhancement module, which is used to perform luminance edge enhancement on the initial luminance reconstructed image block based on each candidate image domain enhancement parameter and the probability distribution parameter to obtain a target luminance reconstructed image block; the determination module is also used to determine the cost value corresponding to the candidate image domain enhancement parameter based on the target luminance reconstructed image block; select the image domain enhancement parameter corresponding to the current image block from all candidate image domain enhancement parameters based on the cost value corresponding to each candidate image domain enhancement parameter; the encoding module is also used to encode the image domain enhancement parameter.

[0356] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. The present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. The embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, read-only optical disk storage (Compact Disc Read-Only Memory, CD-ROM), optical storage, etc.) containing computer-usable program code. The foregoing is merely an embodiment of the present application and is not intended to limit the present application.

[0357] For those skilled in the art, various modifications and variations are possible in this application. Any modification, equivalent replacement, improvement, etc. made within the principles of this application should be included in the scope of the claims of this application.

Claims

1. An image decoding method, characterized in that: The method comprises: Decoding a bitstream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; Determine a probability distribution parameter based on the coefficient hyperparameter feature, and decode a bitstream corresponding to the current image block based on the probability distribution parameter to obtain an initial reconstruction feature corresponding to the current image block; Determine an initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature; wherein the initial reconstructed image block includes an initial brightness reconstructed image block and an initial chrominance reconstructed image block; Brightness edge enhancement is performed on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block.

2. The method according to claim 1, characterized in that After determining the initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature, the method further includes: Performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block; A target reconstructed image block is generated based on the target luminance reconstructed image block and the target chrominance reconstructed image block.

3. The method according to claim 1, characterized in that The step of performing brightness edge enhancement on the initial brightness reconstructed image block to obtain a target brightness reconstructed image block comprises: Decoding a bitstream corresponding to the current image block to obtain an image domain enhancement parameter corresponding to the current image block; Brightness edge enhancement is performed on the initial brightness reconstructed image block based on the image domain enhancement parameter and the probability distribution parameter to obtain a target brightness reconstructed image block corresponding to the current image block.

4. The method according to claim 3, characterized in that: The image domain enhancement parameters include a plurality of image enhancement segmentation strength values ​​and a plurality of image enhancement segmentation thresholds, wherein the plurality of image enhancement segmentation thresholds constitute a plurality of image enhancement threshold intervals, and the plurality of image enhancement threshold intervals correspond one-to-one to the plurality of image enhancement segmentation strength values; The performing brightness edge enhancement on the initial brightness reconstructed image block based on the image domain enhancement parameter and the probability distribution parameter to obtain a target brightness reconstructed image block corresponding to the current image block includes: Acquire a target probability distribution channel map based on the probability distribution parameters; If the target probability distribution channel map includes multiple probability distribution values, then for each probability distribution value, an image enhancement segmentation intensity value corresponding to the probability distribution value is determined based on the image enhancement threshold interval corresponding to the probability distribution value; The initial brightness reconstructed image block is subjected to brightness edge enhancement based on the image enhancement segment intensity value corresponding to each probability distribution value to obtain a target brightness reconstructed image block corresponding to the current image block.

5. The method according to claim 4, characterized in that The obtaining of a target probability distribution channel map based on the probability distribution parameters includes: Decoding a code stream corresponding to the current image block to obtain an important channel identifier corresponding to the current image block; If the probability distribution parameters include a plurality of probability distribution channel graphs, then selecting the probability distribution channel graph corresponding to the important channel identifier as the important probability distribution channel graph from the plurality of probability distribution channel graphs included in the probability distribution parameters; The important probability distribution channel map is upsampled to obtain a target probability distribution channel map.

6. The method according to claim 4, characterized in that The step of performing brightness edge enhancement on the initial brightness reconstructed image block based on the image enhancement segment intensity value corresponding to each probability distribution value to obtain a target brightness reconstructed image block corresponding to the current image block includes: Generate a high-frequency detail image based on the initial brightness reconstruction image block; For each eigenvalue in the high-frequency detail image, brightness edge enhancement is performed on the eigenvalue based on the image enhancement segment intensity value corresponding to the probability distribution value corresponding to the eigenvalue to obtain an image enhancement eigenvalue; The target brightness reconstructed image block is determined based on the image enhancement eigenvalue corresponding to each eigenvalue.

7. The method according to claim 6, characterized in that The generating a high-frequency detail image based on the initial brightness reconstruction image block comprises: Performing a convolution operation on the initial brightness reconstruction image block and a Gaussian blur convolution kernel to obtain a Gaussian blur image; Subtract the Gaussian blurred image from the initial brightness reconstructed image block to obtain the high-frequency detail image; Wherein, for each value in the Gaussian blur convolution kernel, the denominator of the value is a power of 2.

8. The method according to claim 7, characterized in that The Gaussian blur convolution kernel is an N*N convolution kernel, where N is a positive integer. If the Gaussian blur convolution kernel is a 3*3 convolution kernel, the Gaussian blur convolution kernel includes: Among them, A is a positive integer, B is a positive integer, and M is a power of 2.

9. The method according to claim 8, characterized in that The Gaussian blur convolution kernel is:

10. The method according to claim 2, characterized in that The step of performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block includes: Adaptively upsampling the initial chroma reconstructed image block and the initial luminance reconstructed image block to obtain an upsampled chroma reconstructed image block, wherein the upsampled chroma reconstructed image block has the same resolution as the initial luminance reconstructed image block; A filtering operation is performed on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

11. The method according to claim 10, characterized in that The filtering operation is performed on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block, including: Perform cross-channel filtering on the upsampled chrominance reconstructed image block based on the initial luminance reconstructed image block to obtain the target chrominance reconstructed image block; or, Based on the initial luminance reconstructed image block, nonlinear chrominance filtering is performed on the upsampled chrominance reconstructed image block to obtain the target chrominance reconstructed image block; or, performing cross-channel filtering on the upsampled chroma reconstructed image block based on the initial luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and performing nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the initial luminance reconstructed image block to obtain the target chroma reconstructed image block; or, Based on the initial luminance reconstructed image block, nonlinear chrominance filtering is performed on the upsampled chrominance reconstructed image block to obtain a nonlinear chrominance filtered chrominance reconstructed image block, and based on the initial luminance reconstructed image block, cross-channel filtering is performed on the nonlinear chrominance filtered chrominance reconstructed image block to obtain the target chrominance reconstructed image block.

12. The method according to claim 2, characterized in that: The step of performing chroma filtering enhancement on the initial chroma reconstructed image block to obtain a target chroma reconstructed image block includes: Adaptively upsampling the initial chroma reconstructed image block and the target brightness reconstructed image block to obtain an upsampled chroma reconstructed image block, wherein the upsampled chroma reconstructed image block has the same resolution as the target brightness reconstructed image block; A filtering operation is performed on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block; wherein the filtering operation includes cross-channel filtering and / or nonlinear chroma filtering.

13. The method according to claim 12, characterized in that The filtering operation is performed on the upsampled chroma reconstructed image block to obtain the target chroma reconstructed image block, including: Perform cross-channel filtering on the upsampled chrominance reconstructed image block based on the target luminance reconstructed image block to obtain the target chrominance reconstructed image block; or, Based on the target luminance reconstructed image block, nonlinear chrominance filtering is performed on the upsampled chrominance reconstructed image block to obtain the target chrominance reconstructed image block; or, performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target luminance reconstructed image block to obtain a cross-channel filtered chroma reconstructed image block, and performing nonlinear chroma filtering on the cross-channel filtered chroma reconstructed image block based on the target luminance reconstructed image block to obtain the target chroma reconstructed image block; or, Based on the target brightness reconstructed image block, nonlinear chroma filtering is performed on the upsampled chroma reconstructed image block to obtain a nonlinear chroma filtered chroma reconstructed image block, and based on the target brightness reconstructed image block, cross-channel filtering is performed on the nonlinear chroma filtered chroma reconstructed image block to obtain the target chroma reconstructed image block.

14. The method according to claim 11 or 13, characterized in that The method of performing cross-channel filtering on the upsampled chroma reconstructed image block based on the initial brightness reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block includes: performing wavelet transform on the upsampled chroma reconstructed image block and performing wavelet transform on the initial brightness reconstructed image block; connecting the upsampled chroma reconstructed image block after wavelet transform and the initial brightness reconstructed image block after wavelet transform in the channel dimension; inputting the features after the channel dimension connection into the residual block network, and performing inverse wavelet transform on the features output by the residual block network to obtain the cross-channel filtered chroma reconstructed image block; The method of performing cross-channel filtering on the upsampled chroma reconstructed image block based on the target brightness reconstructed image block to obtain the cross-channel filtered chroma reconstructed image block includes: performing wavelet transform on the upsampled chroma reconstructed image block and performing wavelet transform on the target brightness reconstructed image block; connecting the upsampled chroma reconstructed image block after wavelet transform and the target brightness reconstructed image block after wavelet transform in the channel dimension; inputting the features after the channel dimension connection into the residual block network, performing inverse wavelet transform on the features output by the residual block network, and obtaining the cross-channel filtered chroma reconstructed image block.

15. The method according to any one of claims 1 to 14, characterized in that: The determining, based on the initial reconstruction feature, an initial reconstructed image block corresponding to the current image block comprises: Inputting the initial reconstruction feature into a synthetic transformation network to obtain the initial reconstructed image block; or, The code stream corresponding to the current image block is decoded to obtain the feature domain enhancement parameters corresponding to the current image block; the initial reconstructed features are enhanced in the feature domain based on the feature domain enhancement parameters and the probability distribution parameters to obtain enhanced reconstructed features; the enhanced reconstructed features are input into a synthetic transformation network to obtain the initial reconstructed image block.

16. The method according to claim 15, characterized in that The method further comprises: Decode the bitstream corresponding to the current image block to obtain the important channel identifier corresponding to the current image block; wherein the initial reconstruction feature includes C feature channel maps, the probability distribution parameters include C probability distribution channel maps, the C probability distribution channel maps correspond to the C feature channel maps one-to-one, C is a positive integer, the feature channel map corresponding to the important channel identifier is used as the important feature channel map, the remaining feature channel maps in the C feature channel maps are used as non-important feature channel maps, the probability distribution channel map corresponding to the important feature channel map is used as the important probability distribution channel map, and the remaining probability distribution channel maps in the C probability distribution channel maps are used as non-important probability distribution channel maps; The step of performing feature domain enhancement on the initial reconstruction feature based on the feature domain enhancement parameter and the probability distribution parameter to obtain an enhanced reconstruction feature includes: Performing feature-adaptive edge enhancement on the important feature channel map based on the feature domain enhancement parameter and the important probability distribution channel map to obtain a first reconstructed feature after feature-adaptive edge enhancement; Based on the feature domain enhancement parameter and the non-important probability distribution channel map, the non-important feature channel map is subjected to feature adaptive stretching to obtain a second reconstructed feature after feature adaptive stretching; The enhanced reconstruction feature is generated based on the first reconstruction feature and the second reconstruction feature.

17. An image encoding method, characterized in that: The method comprises: Encode the coefficient hyperparameter features corresponding to the current image block Determine a probability distribution parameter based on the coefficient hyperparameter feature, and perform a Encode the initial image features corresponding to the image block; Determining an initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature corresponding to the initial image feature; wherein the initial reconstructed image block includes an initial brightness reconstructed image block and an initial chrominance reconstructed image block; For each candidate image domain enhancement parameter, based on the candidate image domain enhancement parameter and the probability distribution parameter, an initial brightness reconstructed image block is enhanced in brightness to obtain a target brightness reconstructed image block; and based on the target brightness reconstructed image block, a cost value corresponding to the candidate image domain enhancement parameter is determined; An image domain enhancement parameter corresponding to the current image block is selected from all candidate image domain enhancement parameters based on a cost value corresponding to each candidate image domain enhancement parameter, and the image domain enhancement parameter is encoded.

18. An image decoding device, characterized in that: The device comprises: A decoding module, configured to decode a bitstream corresponding to a current image block to obtain coefficient hyperparameter features corresponding to the current image block; determine probability distribution parameters based on the coefficient hyperparameter features, and decode the bitstream corresponding to the current image block based on the probability distribution parameters to obtain initial reconstruction features corresponding to the current image block; A determination module, configured to determine an initial reconstructed image block corresponding to the current image block based on the initial reconstruction feature; wherein the initial reconstructed image block includes an initial brightness reconstructed image block and an initial chrominance reconstructed image block; The enhancement module is used to perform brightness edge enhancement on the initial brightness reconstruction image block to obtain a target brightness reconstruction image block.

19. An image encoding device, characterized in that: The device comprises: An encoding module, used to encode coefficient hyperparameter features corresponding to the current image block; determine probability distribution parameters based on the coefficient hyperparameter features, and encode initial image features corresponding to the current image block based on the probability distribution parameters; A determination module, configured to determine an initial reconstructed image block corresponding to the current image block based on an initial reconstruction feature corresponding to the initial image feature; the initial reconstructed image block includes an initial brightness reconstructed image block and an initial chrominance reconstructed image block; An enhancement module, configured to perform brightness edge enhancement on an initial brightness reconstructed image block based on each candidate image domain enhancement parameter and the probability distribution parameter to obtain a target brightness reconstructed image block; The determination module is further used to determine a cost value corresponding to the candidate image domain enhancement parameter based on the target brightness reconstructed image block; and select an image domain enhancement parameter corresponding to the current image block from all candidate image domain enhancement parameters based on the cost value corresponding to each candidate image domain enhancement parameter; The encoding module is also used to encode the image domain enhancement parameters.

20. An image decoding device, characterized in that: The image decoding device comprises: a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor; The processor is used to execute machine executable instructions to implement the method described in any one of claims 1-16.

21. An image encoding device, characterized in that The image encoding device comprises: a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor; The processor is configured to execute machine executable instructions to implement the method of claim 17.

22. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores a plurality of computer instructions, and when the computer instructions are executed by the processor, the method described in any one of claims 1 to 16 is implemented; or, when the computer instructions are executed by the processor, the method described in claim 17 is implemented.

23. A computer program, characterized in that The computer program is configured to implement the method of any one of claims 1 to 16 when executed by a processor having a memory, or to implement the method of claim 17 when executed by a processor having a memory.

24. A computer program product, characterized in that The computer program instructions include computer program instructions, which are configured to, when executed by a processor having a memory, implement the method of any one of claims 1 to 16, or when executed by a processor having a memory, implement the method of claim 17.

Images