Data processing method, apparatus, and device

Abstract

The embodiment of the application provides a kind of data processing method, device, equipment, the method includes: determining the classification mode information corresponding to the to-be-encoded block in video data;Classification mode information includes the extended co-located luma reconstructed pixel corresponding to the color component pixel in to-be-encoded block and target classification mode;Extended co-located luma reconstructed pixel belongs to the target area with the real co-located luma reconstructed pixel of color component pixel as center;According to extended co-located luma reconstructed pixel and target classification mode, determine the boundary class corresponding to color component pixel;Compensate the reconstructed pixel of color component pixel based on boundary class, obtain the reconstructed pixel after compensation, and encode the to-be-encoded block by the reconstructed pixel after compensation.Processing.Using the embodiment of the application, the class accuracy of the boundary compensation corresponding to the color component pixel can be improved, and the overall encoding performance of the video data can be improved.

Description

Technical Field

[0001] This application relates to video coding technology, and more particularly to a data processing method, apparatus, and device. Background Technology

[0002] When viewing video data, compression is usually required. The compressed data is called a video stream, which is transmitted to the user's end via wired or wireless network for decoding and viewing. The entire video encoding process can include block partitioning, prediction, transform, quantization, and encoding. During the video encoding stage, after the entire frame image is reconstructed, pixel values ​​in the reconstructed image can be filtered and compensated to adjust the pixel values ​​and further improve image quality. However, in current video encoding compensation methods, the position of the iso-luminance component used during classification is fixed, which can lead to low final category accuracy, thus affecting the overall encoding performance of the video data. Summary of the Invention

[0003] This application provides a data processing method, apparatus, and device that can improve the accuracy of boundary compensation categories corresponding to color component pixels, thereby improving the overall coding performance of video data.

[0004] One embodiment of this application provides a data processing method, including:

[0005] Determine the classification mode information corresponding to the block to be encoded in the video data; the classification mode information includes the extended isotope reconstructed pixels corresponding to the color component pixels in the block to be encoded and the target classification mode; the extended isotope reconstructed pixels belong to the target region centered on the true isotope reconstructed pixels of the color component pixels;

[0006] Based on the extended iso-luminance reconstructed pixels and the target classification pattern, determine the boundary category corresponding to the color component pixels;

[0007] The reconstructed pixels of color component pixels are compensated based on the boundary category to obtain compensated reconstructed pixels, and the block to be encoded is encoded using the compensated reconstructed pixels.

[0008] One embodiment of this application provides a data processing method, including:

[0009] The video data block to be decoded is decoded to obtain the classification mode information corresponding to the block to be decoded. The classification mode information includes the extended isotope reconstructed pixel corresponding to the color component pixel and the target classification mode. The extended isotope reconstructed pixel belongs to the target region centered on the true isotope reconstructed pixel of the color component pixel.

[0010] Based on the extended iso-luminance reconstructed pixels and the target classification pattern, determine the boundary category corresponding to the color component pixels;

[0011] The color component pixels in the block to be decoded are reconstructed based on the compensation value corresponding to the boundary category.

[0012] One embodiment of this application provides a data processing apparatus, including:

[0013] The classification pattern information determination module is used to determine the classification pattern information corresponding to the block to be encoded in the video data; the classification pattern information includes the extended isotope reconstructed pixels corresponding to the color component pixels in the block to be encoded and the target classification pattern; the extended isotope reconstructed pixels belong to the target region centered on the true isotope reconstructed pixels of the color component pixels;

[0014] The first category determination module is used to determine the boundary category corresponding to the color component pixels based on the extended iso-brightness reconstructed pixels and the target classification pattern.

[0015] The encoding processing module is used to compensate the reconstructed pixels of color component pixels based on the boundary category, so as to obtain the compensated reconstructed pixels, and to encode the block to be encoded using the compensated reconstructed pixels.

[0016] One embodiment of this application provides a data processing apparatus, including:

[0017] The decoding processing module is used to decode the block to be decoded in the video data to obtain the classification mode information corresponding to the block to be decoded. The classification mode information includes the extended isotope reconstructed pixel corresponding to the color component pixel and the target classification mode. The extended isotope reconstructed pixel belongs to the target region centered on the true isotope reconstructed pixel of the color component pixel.

[0018] The second category determination module is used to determine the boundary category corresponding to the color component pixels based on the extended iso-brightness reconstructed pixels and the target classification pattern.

[0019] The pixel reconstruction module is used to reconstruct the color component pixels in the block to be decoded based on the compensation value corresponding to the boundary category.

[0020] One aspect of this application provides a computer device, including a memory and a processor. The memory is connected to the processor, the memory is used to store computer programs, and the processor is used to call the computer programs so that the computer device executes the method provided in one aspect of this application.

[0021] One aspect of this application provides a computer-readable storage medium storing a computer program adapted to be loaded and executed by a processor, so that a computer device having a processor performs the method provided in one aspect of this application.

[0022] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method provided in the above aspect.

[0023] In this embodiment, when encoding video data, the classification mode information corresponding to the block to be encoded in the video data can be determined. This classification mode information includes the extended isotope reconstructed pixels corresponding to the color component pixels in the block to be encoded and the target classification mode. The extended isotope reconstructed pixels belong to a target region centered on the true isotope reconstructed pixels of the color component pixels. Then, based on the extended isotope reconstructed pixels and the target classification mode, the boundary category corresponding to the color component pixels can be determined. The reconstructed pixels of the color component pixels are compensated based on the boundary category to obtain compensated reconstructed pixels, which are then used to encode the block to be encoded. Therefore, in the video data encoding stage, introducing extended isotope reconstructed pixels for boundary classification can improve the accuracy of the boundary compensation category corresponding to the color component pixels, thereby improving the overall encoding performance of the video data. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the structure of an encoding framework provided in an embodiment of this application;

[0026] Figure 2 This is a schematic diagram of a loop filtering process provided in an embodiment of this application;

[0027] Figure 3 This is a schematic diagram of the basic process of adaptive compensation across component sample points provided in an embodiment of this application;

[0028] Figure 4This is a schematic diagram of the location of type-classified samples in cross-component sample point adaptive compensation provided in an embodiment of this application;

[0029] Figure 5 This is a schematic diagram of a boundary type classification mode in cross-component sample point adaptive compensation provided in an embodiment of this application;

[0030] Figure 6 This is a schematic diagram of a cross-component adaptive loop filter provided in an embodiment of this application;

[0031] Figure 7 This is a schematic diagram of the shape of a cross-component adaptive loop filter provided in an embodiment of this application;

[0032] Figure 8 This is a schematic diagram of the structure of a data processing system provided in an embodiment of this application;

[0033] Figure 9 This is a flowchart illustrating a data processing method provided in an embodiment of this application;

[0034] Figure 10 This is a schematic diagram of a selection area for an extended co-position brightness reconstruction pixel provided in an embodiment of this application;

[0035] Figure 11 This is a schematic diagram of an extended classification mode provided in an embodiment of this application;

[0036] Figure 12 This is a schematic diagram illustrating an extended co-position brightness reconstruction pixel mode coverage provided in an embodiment of this application;

[0037] Figure 13 This is a schematic diagram illustrating the location of pixels reconstructed by extended isotopic brightness and their corresponding classification modes, provided in an embodiment of this application.

[0038] Figure 14 This is a schematic diagram of an embodiment of the present application providing a method for filling unusable areas of reconstructed pixels using coverage area expansion;

[0039] Figure 15 This is a flowchart illustrating another data processing method provided in an embodiment of this application;

[0040] Figure 16 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0041] Figure 17 This is a schematic diagram of another data processing device provided in an embodiment of this application;

[0042] Figure 18 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0044] To better understand the technical solutions provided in the embodiments of this application, the basic concepts involved in the embodiments of this application will be introduced first:

[0045] Video encoding refers to the process of encoding pixels in image frames of video data to obtain an encoded bitstream (also known as a video stream). It can also refer to the process of converting a video file of one format into another using specific compression techniques. The embodiments in this application are based on the Enhanced Compression Model (ECM) in current video encoding technologies. ECM improves the loop filtering part of Versatile Video Coding (VVC), meaning that in addition to continuing to use the existing loop filters in VVC, it introduces several additional loop filters. The encoding framework and loop filtering process of ECM are described below.

[0046] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of an encoding framework provided in an embodiment of this application. Figure 1 The coding framework shown is a block-based hybrid coding framework. A complete image frame in the video data (e.g., image frame 10a) can be divided into multiple blocks (e.g., Coding Tree Blocks (CTBs) or Coding Tree Units (CTUs)). Coding processes such as prediction, transformation, quantization, loop filtering, and entropy coding are performed on a block-by-block basis. Each block has its own corresponding coding parameters. Prediction can refer to not directly encoding the current signal (i.e., the object to be encoded, such as the image frame, block, or pixel to be encoded), but instead using one or more preceding signals to predict the next signal, and then encoding the difference between the actual value and the predicted value to achieve the encoding purpose.

[0047] The prediction can include intra-frame prediction and inter-frame prediction. Intra-frame prediction refers to predicting the pixel value in the current block to be encoded from the neighboring pixels that have already been encoded, in order to effectively remove spatial redundancy in video data.

[0048] Inter-frame prediction utilizes the temporal correlation of video data, using pixels from neighboring encoded image frames to predict pixels in the current image frame to be encoded, thereby removing temporal redundancy in the video data. The inter-frame prediction process mainly involves motion estimation and motion compensation. Motion estimation refers to finding the best-matching reference block in the reference image frame (encoded image frame), using a motion vector to represent the positional relationship between the best-matching reference block and the current block to be encoded (any block in the image frame that needs to be encoded, i.e., a block that has not yet been encoded). Motion compensation refers to encoding and transmitting the difference between the best-matching reference block and the current block to be encoded.

[0049] Transformation refers to performing an orthogonal transformation on the image frames to be encoded in video data to remove spatial correlations between pixels. Orthogonal transformation concentrates the energy originally distributed across each pixel into a few low-frequency coefficients in the frequency domain, representing most of the image information. This characteristic of frequency coefficients is advantageous for quantization methods based on human visual characteristics (HVS). Transformation methods can include, but are not limited to: Karhunen-Loeve Transform (KL Transform), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT).

[0050] Quantization refers to the process of reducing the precision of video data representation. Quantization reduces the amount of data that needs to be encoded and is a lossy compression technique. Quantization can include vector quantization and scalar quantization. Vector quantization jointly quantizes a set of data, while scalar quantization independently quantizes each input data point.

[0051] Loop filtering can remove or reduce various types of coding distortion generated during the block-by-block encoding process, such as block artifacts caused by discontinuities at block boundaries, ringing artifacts, and excessive smoothing of image content textures or boundaries. Specifically, since the quantization process after transforming the image frame to be encoded is a lossy process, it causes information loss in the video data. The blocks recovered after inverse quantization and inverse transform (called reconstructed blocks) have certain errors compared to the original blocks, which may cause the final recovered image frames (reconstructed image frames) to appear blocky. Blocky image frames have a significant impact on the prediction of subsequent image frames. Therefore, loop filtering can be used for deblocking.

[0052] The Decoded Picture Buffer is used to store all the reconstructed picture frames during the encoding stage.

[0053] Entropy coding refers to a method of bitrate compression that utilizes the information entropy of a source. It can remove statistical redundancy that remains after prediction and transformation. Entropy coding can improve the video compression ratio, and since it is lossless, the video data compressed using entropy coding can be reconstructed from the original video data without distortion at the decoding end. Commonly used entropy coding methods include, but are not limited to: variable-length coding and context-adaptive binary arithmetic coding (CABAC).

[0054] Variable-length coding can use codewords of different lengths to represent the differences to be encoded (i.e., the differences between the reconstructed pixel values ​​after loop filtering and the original pixel values ​​in the block to be encoded, where the original pixel values ​​are any pixel values ​​in the block to be encoded) or coefficients. The codeword length needs to be designed based on the probability of symbol occurrence; for example, residuals or coefficients with a high probability of occurrence are assigned shorter codewords, while those with a low probability of occurrence are assigned longer codewords. Commonly used codes include exponential Golomb coding and arithmetic coding.

[0055] CABAC can include the following steps:

[0056] ① Binarization: CABAC uses binary arithmetic encoding, meaning that only two numbers (1 or 0) are encoded. A non-binary numerical symbol, such as a transformation coefficient or motion vector, is first binarized or converted into a binary codeword before arithmetic encoding. This process is similar to converting a numerical value into a variable-length codeword, but this binary codeword is further encoded by an arithmetic encoder before transmission.

[0057] ② Context Model Selection: The context model is a probabilistic model selected based on the statistics of the most recently encoded data symbols. This model stores the probability that each "bin" is 1 or 0.

[0058] ③ Arithmetic encoding: The arithmetic encoder encodes each "bin" according to the selected probability model.

[0059] ④ Probability Update: The selected context model will be updated based on the actual encoded value. For example, if the value of "bin" is 1, then the frequency count of 1 will increase.

[0060] To better understand the above Figure 1 The loop filtering process in the coding framework shown below will be explained through... Figure 2 The loop filtering process is described in detail.

[0061] Please see Figure 2 , Figure 2 This is a schematic diagram of a loop filtering process provided in an embodiment of this application. For example... Figure 2 As shown, this loop filtering process can include multiple modules such as luma mapping with chroma scaling (LMCS), deblocking filter (DBF), bilateral filter (BIF), bilateral filter on chroma (BIF-Chroma), sample adaptive offset (SAO), cross-component sample adaptive offset (CCSAO), adaptive loop filtering (ALF), and cross-component adaptive loop filtering (CC-ALF). Among these, bilateral filter, chroma component bilateral filter, sample adaptive offset, and cross-component sample adaptive offset operate in parallel, and the resulting compensation values ​​(correction values) can be simultaneously added to the reconstructed pixels after deblocking filtering.

[0062] (1) Luminance Mapped Chromaticity Scaling (LMCS): LMCS is not designed for a specific type of coding distortion. It aims to improve coding efficiency by using a better range of sample values.

[0063] (2) Deblocking filter (DBF): DBF is used to reduce distortion caused by the encoding process, specifically to alleviate the discontinuity between blocks caused by block-based operations.

[0064] (3) Double-sided band filtering (BIF): BIF is a non-linear filtering method that is a compromise between spatial proximity and pixel value similarity of image frames. It also considers the spatial information and grayscale similarity of the reconstructed pixels to be filtered, so as to preserve the boundaries between blocks and remove noise. In this embodiment, the reconstructed pixels refer to the results generated by reconstructing the pixels in the block to be encoded during the encoding stage. The reconstructed pixels may include luminance reconstructed pixels (e.g., Y component pixels) and chrominance reconstructed pixels (e.g., U component pixels, V component pixels). Here, Y, U, and V refer to the three color components in the YUV color space. Of course, in addition to using the YUV color space, this embodiment may also use other color spaces, such as the YCbCr color space (Y refers to the luminance component, Cb refers to the blue chrominance component, and Cr refers to the red chrominance component). This embodiment does not limit the actual color space used.

[0065] (4) Bilateral band filtering of chroma components (BIF-Chroma): The main difference between BIF-Chroma and the aforementioned BIF is that BIF performs bilateral band filtering on all three color components of the reconstructed pixel, while BIF-Chroma only performs bilateral band filtering on the chroma reconstructed pixel (i.e. the reconstructed value of the reconstructed pixel on the chroma component).

[0066] (5) Sample Adaptive Compensation (SAO): SAO mitigates the difference between the reconstructed pixels and the original pixels in the block to be encoded caused by quantization by adaptively compensating each pixel sample with an offset value. Specifically, the input reconstructed pixels are divided into different categories, a corresponding compensation value (offset) is generated for each category, and the compensation value is added to the reconstructed pixels belonging to the corresponding category, thereby reducing the difference between the reconstructed pixels and the original pixels. In SAO classification, only the reconstructed value of the current color component to be processed is used for classification. For example, when the input reconstructed pixel has the reconstructed value of the chroma component (chroma reconstructed pixel), SAO only classifies the input chroma reconstructed pixel.

[0067] (6) Cross-Component Sample Adaptive Compensation (CCSAO): Similar to SAO, CCSAO can also reduce the difference between reconstructed pixels and original pixels by classifying the input reconstructed pixels into different categories, generating corresponding compensation values ​​for each category, and adding the compensation values ​​to the reconstructed pixels belonging to the corresponding category. It should be noted that CCSAO can classify any color component to be processed using the reconstructed values ​​of all three color components of the reconstructed pixel. For example, when CCSAO inputs chroma reconstructed pixels, it can use all chroma reconstructed pixels and luminance reconstructed pixels of the same pixel for classification.

[0068] Please see below. Figure 3 , Figure 3 This is a schematic diagram illustrating the basic process of adaptive compensation across component sample points provided in an embodiment of this application. Figure 3 As shown, the reconstructed pixels after deblocking filter (DBF) can include pixel values ​​on three color components. Here, we take the three color components in the YUV color space as an example. These three color components can include luminance reconstructed pixels (Y component pixels) and chrominance reconstructed pixels (U component pixels and V component pixels).

[0069] Specifically, after the Y component pixels processed by DBF are input into SAO, sample point adaptive compensation can be performed on the Y component pixels processed by DBF to obtain the compensation value 1 corresponding to the Y component pixels; at the same time, the Y component pixels processed by DBF can also be input into CCSAO to perform cross-component sample point adaptive compensation on the Y component pixels processed by DBF to obtain the compensation value 2 corresponding to the Y component pixels; then the compensation value 1 output by SAO and the compensation value 2 output by CCSAO can be added to the Y component pixels processed by DBF to obtain the compensated Y component pixels.

[0070] It should be noted that, for ease of understanding, this application only uses SAO and CCSAO as examples for description. In practical applications, the compensated Y component pixels can also have BIF and BIF-Chroma compensation values ​​added. That is, the Y component pixels after deblocking filtering can be sequentially input into BIF and BIF-Chroma, and their respective compensation values ​​will be output. Similarly, the U component pixels and V component pixels after DBF can perform the same operation as the aforementioned Y component pixels to obtain compensated U component pixels and compensated V component pixels, which will not be elaborated here.

[0071] It is understandable that CCSAO can include two compensation types: boundary compensation (BO) and edge compensation (EO). Specifically:

[0072] ① BO type in CCSAO:

[0073] For BO type, CCSAO can directly classify based on the pixel values ​​of the reconstructed pixels. Specifically, for any luma / chroma reconstructed pixel, it can be classified simultaneously using the reconstructed pixels of the corresponding three color components {corresponding Y pixel, corresponding U pixel, corresponding V pixel}. Here, corresponding Y pixel, corresponding U pixel, and corresponding V pixel can be understood as the reconstructed pixels whose positions in the input reconstructed pixel are located in the three color components. The three reconstructed pixels used for classification are first assigned to their respective bands {band}. Y band U band V Then, based on the band categories of the three color components, a joint category index is generated as the BO category of the reconstructed pixel for the current input.

[0074] For each BO category, a compensation value can be generated, and then this compensation value is added to the aforementioned input reconstructed pixels. The CCSAO BO processing procedure can be shown in the following formula (1):

[0075] (1)

[0076] Among them, {Y col U col V col} represent the corresponding reconstructed pixels of the three color components used for classification, Y col U represents the in-situ reconstructed pixel on the luminance component. col , Vcol Represents the reconstructed pixels on the chromaticity components; {N Y N U N V} represents the total number of band categories when the three color components are divided into bands, BD represents the pixel value bit depth, and i represents the category index jointly generated by the three color components, which also refers to the BO category of the currently input reconstructed pixel; C rec and These represent the reconstructed pixels before and after CCSAO, respectively; σ CCSAO [i] represents the compensation value corresponding to category i.

[0077] For the co-occurrence reconstructed pixels of the three color components used for classification, the co-occurrence chromaticity reconstructed pixels (U col V col ) are the true isotopic chromaticity reconstructed pixels (also called isotopic chromaticity components); isotopic luminance reconstructed pixels (Y col Then you can get from such Figure 4 The image shown is centered on the pixel reconstructed from the true iso-position brightness. Select from the area.

[0078] Figure 4This is a schematic diagram illustrating the location of type-classified samples in a cross-component sample point adaptive compensation method provided in an embodiment of this application. For example... Figure 4 The area 20d shown represents the true co-position brightness reconstructed pixel (i.e., the true co-position Y pixel) at position 4, and the area 20a represents the area centered on the true co-position brightness reconstructed pixel with a size of The target areas, areas 20b and 20c, both represent the co-position chromaticity reconstructed pixels at position 4, where area 20b represents the co-position U pixel and area 20c represents the co-position V pixel.

[0079] For CCSAO BO type, the corresponding parameters—namely, the position of the reconstructed pixel in the same brightness, the total number of band classes corresponding to the three color components, and the compensation value for each BO class—need to be determined through a rate-distortion optimization (RDO) process and transmitted to the decoding end. Specifically, for Figure 4 Rate distortion optimization can be performed on any possible co-position brightness reconstructed pixel position in region 20a shown to obtain the rate distortion loss of each possible co-position brightness reconstructed pixel position. The co-position brightness reconstructed pixel position with the smallest rate distortion loss is taken as the final co-position brightness reconstructed pixel position transmitted to the decoding end.

[0080] ② EO type in CCSAO:

[0081] Similar to Sample Adaptive Compensation (SAO), CCSAO can also use boundary-based classification methods. Currently, CCSAO supports four different boundary compensation (EO) classification modes; please refer to [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of a boundary type classification mode in cross-component sample point adaptive compensation provided in an embodiment of this application. For example... Figure 5 As shown, the four different boundary compensation (EO) classification modes in CCSAO are the horizontal classification mode shown in region 30a, the vertical classification mode shown in region 30b, the diagonal classification mode shown in region 30c, and the anti-diagonal classification mode shown in region 30d. For each EO classification mode, for any input luminance reconstruction pixel or chrominance reconstruction pixel, it is first necessary to calculate its corresponding luminance reconstruction pixel (e.g., ...). Figure 5 Position c) shown is adjacent to pixels (such as...) Figure 5 The difference between positions a and b (as shown) is used to determine the EO category of the currently reconstructed pixel by comparing this difference with a predefined threshold (Th). It is understood that each classification mode specifies the positional relationship between the co-position brightness reconstructed pixel and its two adjacent pixels (a and b), which are used to calculate the difference with the co-position brightness reconstructed pixel.

[0082] Unlike SAO, CCSAO EO classifies different color components by using only the corresponding real iso-brightness reconstructed pixels. The CCSAO EO processing can be shown in the following formula (2):

[0083]

[0084] (2)

[0085]

[0086]

[0087] In this context, "?:" represents a conditional statement. For example, "(expression1)? (expression2) : (expression3)" means that if expression1 is true, the value of the entire conditional expression is the value of expression2; if expression1 is false, the value of the entire conditional expression is the value of expression3. Ea represents... Figure 5 The difference between position a (adjacent pixel) and position c (pixel reconstructed at the same brightness) shown (for ease of understanding, this difference can be referred to as the first difference), Eb represents... Figure 5 The difference between position b and position c shown (for ease of understanding, this difference can be referred to as the second difference); i B This indicates the category used in the EO type. idx This refers to the EO category of the currently input reconstructed pixel. Specifically, the EO category of CCSAO is jointly generated by boundary-based categories and band categories, σ. CCSAO [class idx ] indicates the boundary type class idx The corresponding compensation value. Where i B The calculation process can be shown in the following formula (3):

[0088]

[0089] (3)

[0090]

[0091] Wherein, "cur" represents the currently input color component reconstruction pixel to be processed, and "col1" and "col2" represent the corresponding reconstruction pixels in the other two color components, respectively. If the currently input color component reconstruction pixel to be processed is a luminance reconstruction pixel, then "col1" and "col2" are the reconstruction values ​​of the corresponding reconstruction pixels in the U and V components, respectively. If the currently input color component reconstruction pixel to be processed is a corresponding reconstruction pixel in the U component, then "col1" and "col2" are the reconstruction values ​​of the corresponding reconstruction pixels in the Y and V components, respectively.

[0092] For CCSAO EO type, the encoder needs to use Rate-Distortion Optimization (RDO) from... Figure 5 The optimal classification mode is selected from the four classification modes shown. The best threshold Th is selected from a predefined series of candidate thresholds, and the corresponding index is sent to the decoder. Similarly, the encoder needs to select the reconstructed values ​​from "cur", "col1", and "col2" using RDO to generate the reconstructed pixels with categories, and send them to the decoder. In other words, for... Figure 5 The four classification modes shown can all be rate-distorted optimized to obtain the rate-distortion loss corresponding to each classification mode. The classification mode with the smallest rate-distortion loss is taken as the optimal classification mode to be transmitted to the decoding end. For “cur”, “col1” and “col2” in formula (3), rate-distortion optimization can also be performed respectively. The color component with the smallest rate-distortion loss is taken as the reconstruction value to generate the reconstructed pixel with category and transmitted to the decoding end.

[0093] For video data requiring encoding, different video content can use different CCSAO classifiers, and different locations within an image can also use different classifiers. The type and parameters of each classifier need to be explicitly transmitted to the decoder at the slice level. At the coding unit (CTU) level, it needs to be indicated whether the current CTU uses CCSAO. If CCSAO is used, the selection of the corresponding classifier needs to be further indicated. Each frame in a CCSAO supports a maximum of four different classifiers. It should be noted that if, for a given coding unit, the rate-distortion loss without using CCSAO is less than the rate-distortion loss with using CCSAO, then it can be determined that CCSAO will not be used for that coding unit; if the rate-distortion loss without using CCSAO is greater than the rate-distortion loss with using CCSAO, then it can be determined that CCSAO will be used for that coding unit, and the selection of a classifier will then be performed.

[0094] (7) Adaptive Loop Filtering (ALF): ALF is a Wiener filter that adaptively determines the filter coefficients based on the content of different video components, thereby reducing the mean square error (MSE) between the reconstructed color components and the original color components. As an adaptive filter, the Wiener filter can generate different filter coefficients for video content with different characteristics. Therefore, ALF needs to classify the video content first and use the corresponding filter for each category of video content. The input of ALF is the reconstructed pixel value after filtering by DBF, BIF, BIF-Chroma, SAO, and CCSAO. The output is the enhanced reconstructed luminance image and reconstructed chrominance image. The ALF for each luminance reconstructed pixel can support 25 different categories of filters, and the ALF for each chrominance reconstructed pixel can support up to 8 different categories of filters.

[0095] For luminance reconstruction pixels, ALF will perform the calculation at the sub-block level (e.g., this sub-block level can be...). (The brightness blocks) adaptively use different filters, that is, each needs to be... The pixel blocks are divided into one of 25 categories. The classification index C of the luminance pixel block is determined by the pixel block's directional feature D and quantized activity features. (Activity) is obtained together; where the category index C can be as shown in the following formula (4):

[0096] (4)

[0097] Among them, the directional feature D and the quantified activity feature are calculated. First, we can calculate The gradient values ​​of the horizontal, vertical, diagonal, and anti-diagonal of each pixel within the pixel block are shown in the following formulas (5) to (8):

[0098] (5)

[0099] (6)

[0100] (7)

[0101] (8)

[0102] Wherein, H in formula (5) k,l V represents the horizontal pixel gradient value at position (k, l), in formula (6) k,l D0 in formula (7) represents the vertical pixel gradient value at position (k, l). k,lD2 represents the gradient value of the diagonal pixel at position (k, l), in formula (8). k,l R(k,l) represents the anti-angle pixel gradient value at position (k,l); R(k,l) represents the reconstructed pixel value at position (k,l) before ALF filtering.

[0103] Based on the pixel gradient values ​​shown in formulas (5) to (8), each The gradient calculations for the overall horizontal, vertical, diagonal, and anti-diagonal angles of the pixel block are shown in the following formulas (9) and (10):

[0104] g h (9)

[0105] (10)

[0106] Where i and j represent The coordinates of the top-left pixel in the pixel block, g h express The overall horizontal pixel gradient value corresponding to the pixel block, g v express The overall vertical pixel gradient value corresponding to the pixel block, g d0 express The overall diagonal pixel gradient value corresponding to the pixel block, g d1 express The overall anti-angle pixel gradient value corresponding to the pixel block.

[0107] Furthermore, after obtaining the pixel gradient values ​​of the pixel blocks, the maximum value between the horizontal and vertical pixel gradient values ​​of each pixel block can be denoted as: The minimum value can be denoted as The maximum value between the diagonal and anti-diagonal pixel gradient values ​​of each pixel block can be denoted as: The minimum value can be denoted as .

[0108] The directional feature D can be obtained by comparing the maximum value of the horizontal pixel gradient value and the vertical pixel gradient value. Minimum value And the maximum value between the gradient values ​​of the diagonal pixels and the gradient values ​​of the anti-diagonal pixels. Minimum value The derivation steps are as follows:

[0109] Step 1: If and If both conditions are met, then the directional feature D is set to 0; where t1 can be represented as a pre-set parameter.

[0110] Step 2: If If yes, proceed to step 3; otherwise, proceed to step 4.

[0111] Step 3: If If the directional feature D is 2, then the directional feature D is set to 2; otherwise, the directional feature D is set to 1.

[0112] Step 4: If If the directional feature D is 4, then the directional feature D is set to 4; otherwise, the directional feature D is set to 3.

[0113] Wherein, t1 and t2 are parameters pre-set based on actual needs, and this application does not limit them.

[0114] Quantified activity characteristics Before quantification, it can be denoted as activity feature A, which is calculated by the following formula (11):

[0115] (11)

[0116] The activity feature A can be quantized to the range [0-4] to obtain the quantized activity feature. .

[0117] It should be noted that, for each Before filtering the luminance block (including the block composed of luminance reconstructed pixels of each pixel in the aforementioned pixel block), the filter coefficients and corresponding limiting values ​​can be geometrically transformed according to the rules in Table 1 based on the pixel gradient values ​​of the current luminance block. This geometric transformation can include, but is not limited to: no transformation, diagonal transformation, vertical flip, and rotation. Table 1 can be represented as follows:

[0118] Table 1

[0119]

[0120] Applying geometric transformations to the filter coefficients is equivalent to applying geometric transformations to the pixel values ​​without changing the coefficients, followed by filtering. The purpose of geometric transformations is to align the directionality of different pixel blocks as much as possible, thereby reducing the number of classifications required by the ALF and allowing different pixels to share the same filter coefficients. Introducing geometric transformations can increase the actual number of classifications from 25 to 100 without increasing the number of ALF filters, thus improving its adaptability.

[0121] (8) Cross-component adaptive loop filtering (CC-ALF): Similar to the aforementioned ALF, CC-ALF is also a Wiener filter, and its function is similar, so it will not be elaborated here. CC-ALF only operates on chroma reconstructed pixels. The input of CC-ALF is the luminance reconstructed pixel before ALF after filtering by DBF, BIF, BIF-Chroma, SAO, and CCSAO. The output is the correction value of the corresponding chroma reconstructed pixel. CC-ALF also first needs to classify the video content and use the corresponding filter for each category of video content. CC-ALF can support up to 4 different categories of filters for each chroma reconstructed pixel. CC-ALF can utilize the correlation between luminance reconstructed pixels and chroma reconstructed pixels to obtain the correction value of chroma reconstructed pixels through linear filtering of luminance reconstructed pixels. This correction value is added to the chroma reconstructed pixel after ALF to obtain the final reconstructed chroma pixel.

[0122] CC-ALF generates a corresponding correction value for each chroma reconstructed pixel by linearly filtering the luminance reconstructed pixels. The implementation process of CC-ALF and its relationship with ALF can be found in [link to documentation]. Figure 6 , Figure 6 This is a schematic diagram of a cross-component adaptive loop filter framework provided in an embodiment of this application. The embodiment uses the YCbCr color space as an example for description. Figure 6 As shown, the compensation values ​​generated by BIF, BIF-Chroma, SAO, and CCSAO are simultaneously added to the reconstructed pixels after deblocking filtering, thus obtaining the compensated luminance component pixels (such as Y component pixels R). Y ), the compensated blue chromaticity component pixel Cb and the compensated red chromaticity component pixel Cr.

[0123] Compensated luminance component pixel R Y You can input the ALF of the luminance component and output the final luminance component pixel Y through the ALF; the compensated luminance component pixel R Y The input is a CC-ALF filter on the blue chroma component. After CC-ALF processing, the reconstructed pixels on the blue chroma component Cb are output. The difference between the blue chroma component pixel values ​​output by CC-ALF and the blue chroma component pixel values ​​after deblocking filtering is calculated. ; Compensated luminance component pixel R Y The input is a CC-ALF on the red chromaticity component. After CC-ALF processing, the output is the reconstructed pixel on the red chromaticity component (Cr). The difference between the red chromaticity component pixel value output by CC-ALF and the red chromaticity component pixel value after deblocking filtering is calculated. .

[0124] The compensated blue chroma component pixels and the compensated red chroma component pixels can be input into the ALF of the chroma components. The ALF can then be used to output the reconstructed pixels on the blue chroma component, and compared with... The sums yield the final blue chromaticity component pixel Cb; the reconstructed pixels on the red chromaticity component can be output via ALF and compared with... The final red chromaticity component pixel Cr is obtained by adding them together.

[0125] The filtering process of CC-ALF can be represented by the following formula (12):

[0126] (12)

[0127] Among them, R Y The reconstructed sample is obtained by processing BIF, BIF-Chroma, SAO, and CCSAO, and then adding their corresponding compensation values ​​to the reconstructed pixels after deblocking filtering. (x, y) is the sample position of chrominance component pixel f (here, the chrominance component pixel can be the reconstructed chrominance component pixel, which can be called the chrominance reconstructed pixel), (x C y C ) is the luminance component pixel obtained from the chrominance component pixel f (the luminance component pixel here can also be called the luminance reconstruction pixel), S f This is the filtering region supported by the CC-ALF filter on the luminance component, c f (x0, y0) are the filter coefficients corresponding to the chroma component pixel f. (x0, y0) are the offset positions corresponding to the luma component pixel. The position corresponding to the luma component pixel f is obtained by transforming the coordinates of the chroma component pixel according to the scaling relationship between luma and chroma in the video data. This represents the correction value of the chroma component pixel f at the (x, y) position after CC-ALF processing.

[0128] Please see Figure 7 , Figure 7 This is a schematic diagram of the shape of a cross-component adaptive loop filter provided in an embodiment of this application. CC-ALF can support, for example... Figure 7 shown Rhombus filter, such as Figure 7 The black circle shown can represent the luminance component pixel supported by the CC-ALF filter, and the white dashed circle can represent the sample position (x, y) of the chrominance component pixel f.

[0129] It should be noted that, compared to ALF, CC-ALF's filter coefficients eliminate the symmetry constraint, allowing it to flexibly adapt to the relative relationships of various luminance and chrominance components. Furthermore, to reduce the number of filter coefficients that need to be transmitted, CC-ALF imposes the following two constraints on its filter coefficients in the current coding framework design:

[0130] ① The sum of all coefficients in CC-ALF is limited to 0; therefore, for The diamond-shaped filter only requires calculating and transmitting 7 filter coefficients; the filter coefficient at the center position can be automatically inferred at the decoding end based on this condition. ② The absolute value of each filter coefficient to be transmitted must be a power of 2 and can be represented by a maximum of 6 bits; therefore, the absolute values ​​of the CC-ALF filter coefficients are {0, 2, 4, 8, 16, 32, 64}. This design can use shift operations to replace multiplication operations, reducing the number of multiplication operations. Unlike the luma ALF, which supports sub-block level classification and adaptive selection, CC-ALF only supports CTU level classification and adaptive selection. For each chroma component pixel, all chroma component pixels within a CTU belong to the same category, and that CTU can use the same filter.

[0131] An Adaptation Parameter Set (APS) can contain up to 25 sets of luma ALF filter coefficients and their corresponding limiting indexes. Each chroma component pixel supports up to 8 sets of chroma ALF filter coefficients and their corresponding limiting indexes, and each chroma component pixel supports up to 4 sets of CC-ALF filter coefficients. To save bitrate, for luma ALF filters, filter coefficients of different categories can be merged, with multiple categories sharing a single set of filter coefficients. The encoder uses Rate-Distortion Optimization (RDO) to determine which categories of coefficients can be merged. The index of the APS used by the current slice is marked in the slice header. CC-ALF supports CTU-level adaptation. In cases with multiple filters, the CTU level adaptively selects whether to use CC-ALF and the index of the filter used for each chroma component pixel.

[0132] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of a data processing system provided in an embodiment of this application. For example... Figure 8As shown, the data processing system may include an encoding device 40a (encoding end) and a decoding device 40b (decoding end). The encoding device 40a may be a terminal device or a server, and the decoding device 40b may be a terminal device or a server. The encoding device 40a and the decoding device 40b may establish a direct connection through wired communication or an indirect communication connection through wireless communication.

[0133] Terminal devices may include, but are not limited to: smartphones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices (such as smartwatches and smart bracelets), smart voice interaction devices, smart home appliances (such as smart TVs), in-vehicle devices, and VR devices (such as VR headsets and VR glasses). Servers may be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0134] (1) For encoding device 40a:

[0135] Encoding device 40a can acquire video data, which can be obtained through scene capture. Scene capture video data refers to acquiring real-world visual scenes to obtain video data through a capture device associated with encoding device 40a; wherein, the capture device can be used to provide video data acquisition services for encoding device 40a, and the capture device can be, but is not limited to, any of the following: camera equipment, sensor equipment, scanning equipment.

[0136] Among them, the camera equipment may include ordinary cameras, stereo cameras, and light field cameras, etc.; the sensing equipment may include laser equipment, radar equipment, etc.; and the scanning equipment may include three-dimensional laser scanning equipment, etc.; the capture device associated with the encoding device 40a may refer to the hardware components set in the encoding device 40a, such as the camera or sensor of the terminal device. The capture device associated with the encoding device 40a may also refer to the hardware device connected to the encoding device 40a, such as the camera connected to the server, etc.

[0137] Encoding device 40a can encode image frames in video data to obtain the corresponding encoded bitstream. Specifically, this embodiment focuses on the EO type of CCSAO in loop filtering. During CCSAO EO classification, encoding device 40a can input the extended isotope reconstructed pixels (also called generalized isotope reconstructed pixels, i.e., a luminance reconstructed pixel within the target area centered on the actual isotope reconstructed pixel) of the reconstructed pixel to be processed for CCSAO EO classification. Simultaneously, it can use all available classification modes other than horizontal, vertical, diagonal, and anti-diagonal classification modes for CCSAO EO classification and calculate the compensation value for each EO category. The encoded bitstream is obtained by encoding the difference between the original pixels in the block to be encoded and the reconstructed pixels after adding the compensation value. Further, encoding device 40a can transmit the obtained encoded bitstream to decoding device 40b.

[0138] (2) For decoding device 40b:

[0139] After receiving the compressed bitstream (encoded bitstream) transmitted by the encoding device 40a, the decoding device 40b can decode the encoded bitstream to reconstruct the image frame pixels in the video data. Specifically, the decoding device 40b can determine the currently used classification mode by parsing the syntax elements related to the classification mode in the encoded bitstream, and then determine the extended iso-sigma reconstructed pixel position and the target classification mode (i.e., the actual classification mode used) for CCSAO EO classification based on the mode definition consistently set by the encoding device 40a and the decoding device 40b; alternatively, the decoding device 40b can determine the extended iso-sigma reconstructed pixel position by parsing the syntax elements related to the extended iso-sigma reconstructed pixel position in the encoded bitstream, and then determine the selected target classification mode by parsing the syntax elements related to the classification mode in the encoded bitstream. Further, the decoding device 40b can reconstruct the image frame pixels in the video data using the extended iso-sigma reconstructed pixel position and the actual classification mode.

[0140] In this embodiment, the encoding device can input the extended isotope luminance reconstructed pixels of color component pixels for CCSAO EO classification. Simultaneously, it can also use extended classification modes (including all available classification modes other than the aforementioned horizontal, vertical, diagonal, and anti-diagonal classification modes) for CCSAO EO classification, which can improve the accuracy of the pixel's CCSAO EO category and thus improve the encoding performance of video data. The decoding device can parse the extended isotope luminance reconstructed pixel positions and true classification modes from the image frame pixels in the video data, thereby improving the decoding performance of the video data. It is understood that the data processing system described in this embodiment is for the purpose of more clearly illustrating the technical solutions of this embodiment and does not constitute a limitation on the technical solutions of this embodiment. Those skilled in the art will recognize that with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in this embodiment are also applicable to similar technical problems.

[0141] Please see Figure 9 , Figure 9 This is a schematic flowchart illustrating a data processing method provided in an embodiment of this application. It can be understood that this data processing method can be executed by a computer device, which can be... Figure 8 The encoding device 40a shown corresponds to this data processing method, which is an encoding method. For example, Figure 9 As shown, the data processing method may include the following steps S101 to S103:

[0142] Step S101: Determine the classification mode information corresponding to the block to be encoded in the video data; the classification mode information includes the extended isotope reconstructed pixels corresponding to the color component pixels in the block to be encoded and the target classification mode; the extended isotope reconstructed pixels belong to the target region centered on the true isotope reconstructed pixels of the color component pixels.

[0143] Specifically, when a computer device encodes video data, it can perform frame segmentation to obtain a video sequence. This video sequence can be composed of image frames from the video data, and the image frames in the video sequence are arranged in chronological order within the video data. For an image frame to be encoded in the video data, it can be divided into multiple blocks (which can be coding blocks or coding tree units). The block currently being processed within this image frame can be called the encoding block.

[0144] It is understandable that video encoding is performed in blocks. Therefore, when encoding a block, it can undergo encoding operations such as transform, quantization, prediction, loop filtering, and entropy coding. The specific implementation process can be found in the aforementioned... Figures 1 to 7The description shown is provided. The technical solution proposed in this application mainly addresses the CCSAO EO classification process in loop filtering, which differs from existing video coding frameworks that only use fixed real co-position brightness to reconstruct pixels, and the aforementioned... Figure 5 The four classification modes shown (also known as the basic classification modes) are used for CCSAO EO classification. This application proposes using extended isotopic reconstructed pixels (which can be selected from the target region centered on the true isotopic reconstructed pixels, also known as generalized isotopic reconstructed pixels) for CCSAO EO classification, or using extended classification modes (excluding...). Figure 5 CCSAO EO classification can be performed using any of the available classification modes other than the four classification modes shown, or by simultaneously using extended iso-sigma reconstructed pixels and extended classification modes. The following will describe the CCSAO EO classification process in detail using any color component pixel in the block to be encoded as an example.

[0145] In this embodiment of the application, for each original color component pixel in the block to be encoded, the aforementioned steps can be performed. Figure 2 The loop filtering process shown means that the color component pixels input to the CCSAO are essentially the reconstructed pixels of the original color component pixels in the block to be encoded after deblocking filtering. For the color component pixels input to the CCSAO, the computer device can obtain the true co-position brightness reconstructed pixel corresponding to the color component pixel, use the true co-position brightness reconstructed pixel as the region center, determine a target region with a target size, and determine the extended co-position brightness reconstructed pixel corresponding to the color component pixel from the target region. That is, the extended co-position brightness reconstructed pixel can be any brightness reconstructed pixel in the target region. The candidate classification mode set corresponding to the color component pixel can be obtained, the target classification mode corresponding to the color component pixel can be determined from the candidate classification mode set, and the extended co-position brightness reconstructed pixel and the target classification mode are added to the classification mode information corresponding to the block to be encoded.

[0146] The target size can be used to represent the coverage area of ​​the target region, such as the target region being... The target area can be considered as the selection area for extended co-position brightness reconstruction pixels; the extended co-position brightness reconstruction pixels corresponding to the color component pixels are the optimal brightness reconstruction pixels selected from the target area. See also... Figure 10 , Figure 10 This is a schematic diagram of the selection area for extended co-position brightness reconstruction pixels provided in an embodiment of this application. For example... Figure 10The position c0 shown can be represented as the real co-position brightness reconstructed pixel corresponding to the color component pixel. This position c0 is the center of the target area. The target area can include positions 0 to 7, as well as the position c0 where the real co-position brightness reconstructed pixel itself is located. The extended co-position brightness reconstructed pixel corresponding to the color component pixel can be the optimal brightness reconstructed pixel selected from positions 0 to 7, or the optimal brightness reconstructed pixel selected from positions 0 to 7 and position c0.

[0147] The target classification mode can be the optimal classification mode selected from a set of candidate classification modes. This set of candidate classification modes can refer to the set of all possible classification modes used to calculate the difference between the extended isotopic reconstructed pixel and its neighboring pixels at every possible position in the target region. When using extended isotopic reconstructed pixels for CCSAO EO classification, the coverage area of ​​different classification modes in this set of candidate classification modes is not limited; for example, the size of the coverage area of ​​different classification modes can be... In this scenario, when the extended isotopic reconstructed pixel is any one of the reconstructed pixels in the target region, the candidate classification pattern set can include the basic classification pattern and the extended classification pattern, or the candidate classification pattern can include the extended classification pattern.

[0148] Specifically, if the expanded co-position brightness reconstructed pixel is any other brightness reconstructed pixel in the target region besides the actual co-position brightness reconstructed pixel, such as... Figure 10 For any of the brightness reconstructed pixels at positions 1 to 7 shown, the candidate classification mode can include only the basic classification mode, only the extended classification mode, or both the basic and extended classification modes. If the extended co-position brightness reconstructed pixel is the true co-position brightness reconstructed pixel in the target region, such as... Figure 10 At position c0, the candidate classification patterns can include only extended classification patterns. These basic classification patterns can include horizontal, vertical, diagonal, and anti-diagonal classification patterns, as described above. Figure 5 The classification patterns shown; extended classification patterns refer to available classification patterns in addition to the basic classification patterns, such as... Figure 11 The extended classification pattern shown.

[0149] Please see Figure 11 , Figure 11 This is a schematic diagram of an extended classification mode provided in an embodiment of this application. The extended classification mode proposed in this application can be as follows: Figure 11 The 16 classification modes shown in extended classification modes 50a to 50r can be used to define the target classification mode for each color component pixel. Figure 11 Any of the extended classification patterns shown can be derived from... Figure 11 Select the target classification mode corresponding to the pixel of the color component from all the extended classification modes shown.

[0150] It should be noted that when using the extended classification mode for CCSAO EO classification, it can be used only for chroma component pixels, or for all color component pixels (chroma component pixels and luma component pixels), or only for luma component pixels. This application does not limit this.

[0151] The color component pixels in the block to be encoded can be either luma component pixels or chroma component pixels. For example, when using extended co-occurrence luma reconstruction pixels for CCSAO EO classification, they can be used only for chroma component pixels, or they can be used for all color component pixels (chroma component pixels and luma component pixels), or they can be used only for luma component pixels. This application does not limit this.

[0152] Understandably, regardless of whether the currently input color component pixels to be processed are luma component pixels or chroma component pixels, CCSAO EO classification is performed by calculating their corresponding isotopic luma reconstructed pixels. However, when calculating the isotopic luma reconstructed pixels for color component pixels, especially for chroma component pixels, the actual calculated position of the isotopic luma reconstructed pixels may deviate from the actual position of the color component pixels. Therefore, using extended isotopic luma reconstructed pixels for CCSAO EO classification can improve the accuracy of CCSAO EO classification for color component pixels. In practical application scenarios, based on bitrate considerations, it is also possible to choose to use extended isotopic luma reconstructed pixels only for chroma component pixels for CCSAO EO classification, which is supported by the technical solutions proposed in the embodiments of this application.

[0153] In one or more embodiments, when using extended isotope reconstructed pixels for CCSAO EO classification, if the coverage area of ​​different classification modes cannot exceed the range covered by different classification modes in the current existing CCSAO EO classification, that is, the coverage area of ​​different classification modes cannot exceed the target area centered on the true isotope reconstructed pixels, then the coverage area of ​​different classification modes can be referred to... Figure 12 , Figure 12 This is a schematic diagram illustrating an extended co-position brightness reconstruction pixel mode coverage provided in an embodiment of this application. For example... Figure 12The position c0 shown represents the true co-position brightness reconstructed pixel corresponding to the color component pixel, region 60a represents the unusable region of the extended co-position brightness reconstructed pixel, and region 60b is the coverage area specified by each classification mode. That is to say, the positions of the two adjacent elements of the extended co-position brightness reconstructed pixel specified by each classification mode in the candidate classification mode set must be in region 60b and cannot exceed the range of region 60b. That is, the region that exceeds the range of region 60b is the unusable region.

[0154] Optionally, when the coverage area specified by each classification mode is the target area, the brightness reconstruction pixels in the target area excluding vertex positions can be determined as the candidate brightness pixel set corresponding to the color component pixels. This candidate brightness pixel set can include the brightness reconstruction pixels in the cross-shaped region of the target area, such as... Figure 10 Positions 1, 3, 4, 6, and c0 are shown. For each luminance reconstruction pixel in the candidate luminance pixel set, rate-distortion optimization is performed on the color component pixels to obtain the first rate-distortion loss corresponding to each luminance reconstruction pixel in the candidate luminance pixel set. The luminance reconstruction pixel corresponding to the smallest first rate-distortion loss (the optimal luminance reconstruction pixel) is determined as the extended co-position luminance reconstruction pixel corresponding to the color component pixel.

[0155] Furthermore, provided that the coverage area specified by each classification mode is the target area, the available classification modes can be limited based on the position of the pixels reconstructed by extended iso-luminance. For example, the set of candidate classification modes corresponding to color component pixels can be determined based on the position of the pixels reconstructed by extended iso-luminance in the target area. In this case, the coverage area of ​​the classification modes in the candidate classification mode set is less than or equal to the target area; when the pixels reconstructed by extended iso-luminance are as follows... Figure 10 When any of the positions 1, 3, 4, and 6 shown are given, the candidate classification mode set corresponding to each possible position of the expanded same-position brightness reconstructed pixel can include either a horizontal classification mode or a vertical classification mode; when the expanded same-position brightness reconstructed pixel is as shown... Figure 10 At position c0 (i.e., the reconstructed pixel with true co-position brightness), the candidate classification mode set can include horizontal classification mode, vertical classification mode, diagonal classification mode, and anti-diagonal classification mode, that is... Figure 5 The four classification modes are shown. For each classification mode in the candidate classification mode set, rate-distortion optimization is performed on the color component pixels to obtain the second rate-distortion loss corresponding to each classification mode in the candidate classification mode set; the classification mode corresponding to the minimum second rate-distortion loss (optimal classification mode) is determined as the target classification mode for the color component pixels.

[0156] Please see below. Figure 13 , Figure 13 This is a schematic diagram illustrating the location of pixels and their corresponding classification modes in an extended isotopic brightness reconstruction embodiment provided in this application. For example... Figure 13 As shown, when the extended co-position brightness reconstruction pixel is Figure 10 When position 1 is shown, its corresponding classification pattern is as follows: Figure 13 The horizontal classification mode 70a is shown; when the extended isotopic brightness reconstruction pixel is Figure 10 When position 3 is shown, its corresponding classification pattern is as follows: Figure 13 The vertical classification mode 70b shown; when the extended co-position brightness reconstruction pixel is Figure 10 When position 4 is shown, its corresponding classification pattern is as follows: Figure 13 The vertical classification mode 70c shown; when the extended co-position brightness reconstruction pixel is Figure 10 When position 6 is shown, its corresponding classification pattern is as follows: Figure 13 The horizontal classification pattern shown is 70d.

[0157] Optionally, provided that the coverage area defined by each classification mode is the target area, if the extended isotopic luminance reconstruction pixel is any luminance reconstruction pixel in the target area, and the coverage range of different classification modes in the candidate classification mode set is larger than the target area, then the boundary pixels in the target area can be filled into the adjacent areas of the target area to obtain the mode coverage range; this mode coverage range is used to determine the target classification mode from the candidate classification mode set. In other words, when the coverage area of ​​different classification modes exceeds the coverage range of the different classification modes of the existing CCSAO EO classification, that is, when the coverage area of ​​each classification mode exceeds the target area, the unusable area of ​​the extended isotopic luminance reconstruction pixel can be filled using the coverage range of the different classification modes of the existing CCSAO EO classification, that is, the boundary pixels in the target area can be copied to fill the unusable area of ​​the extended isotopic luminance reconstruction pixel.

[0158] Please see Figure 14 , Figure 14 This is a schematic diagram illustrating how to fill unusable areas of reconstructed pixels using expanded co-position brightness, as provided in an embodiment of this application. Figure 14The area 61b shown represents the range covered by different classification modes of the existing CCSAO EO classification, or it can refer to the available area of ​​extended iso-saturation reconstructed pixels (e.g., the target area), while area 61a represents the unavailable area of ​​extended iso-saturation reconstructed pixels. When the coverage of a classification mode in the candidate classification mode set is greater than that of area 61b (the target area), each boundary pixel in area 61b can be filled into the area adjacent to the corresponding boundary pixel in area 61a; for example, boundary pixel P0 in area 61b can be filled into the area adjacent to boundary pixel P0 in area 61a, and boundary pixel P1 in area 61b can be filled into the area adjacent to boundary pixel P1 in area 61a.

[0159] It should be noted that the candidate luminance pixel set corresponding to the color component pixel may include all or part of the luminance reconstructed pixels in the target area. For example, luminance reconstructed pixels at any position in the target area may be selected as the candidate luminance pixel set corresponding to the color component pixel according to the constraints in the actual application scenario. This application does not limit this.

[0160] Step S102: Determine the boundary category corresponding to the color component pixel based on the extended iso-brightness reconstructed pixel and the target classification mode.

[0161] Specifically, after determining the extended co-position brightness reconstructed pixel and target classification mode corresponding to the color component pixels, for ease of understanding, the extended co-position brightness reconstructed pixel can be denoted as c, and the true co-position brightness reconstructed pixel can be denoted as c0. Since the boundary category can be jointly generated by the boundary-based category and the band category, the extended co-position brightness reconstructed pixel here can be used only to calculate the boundary category of the color component pixels, or it can be used only to calculate the band category of the color component pixels, or it can be used to calculate both the band category and the boundary category of the color component pixels.

[0162] The following example uses color component pixels as luminance component pixels. When the extended co-position luminance reconstruction pixels are used to calculate the band category of color component pixels (which can be denoted as i),... B ) and boundary categories (which can be denoted as class) idxWhen the target classification pattern is defined, the computer device can determine the first neighboring pixel (denoted as a) and the second neighboring pixel (denoted as b) corresponding to the extended co-position luminance reconstructed pixel c; obtain the first difference (denoted as Ea) between the first neighboring pixel and the extended co-position luminance reconstructed pixel c, and obtain the second difference (denoted as Eb) between the second neighboring pixel and the extended co-position luminance reconstructed pixel; obtain the first co-position chrominance pixel and the second co-position chrominance pixel corresponding to the real co-position luminance reconstructed pixel, that is, the reconstructed pixels of the color component pixel on the two chrominance components; and determine the band category i to which the luminance component pixel belongs based on the extended co-position luminance reconstructed pixel, the first co-position chrominance pixel, and the second co-position chrominance pixel. B According to category i B The third and fourth differences determine the boundary class corresponding to the color component pixels. idx .

[0163] Wherein, the band category i to which the color component pixel belongs B The acquisition process may include: acquiring the extended co-position luminance reconstructed pixels (at this time, the extended co-position luminance reconstructed pixels are equivalent to cur as shown in formula (3)) and the total number of band categories on the luminance component (which can be denoted as N). cur The first product of the first corresponding chromaticity pixel (which can be denoted as col1) and the total number of band categories on the first chromaticity component (which can be denoted as N) is obtained. col1 The second product of the second chromaticity pixel (which can be denoted as col2) and the total number of band categories on the second chromaticity component (which can be denoted as N) are obtained. col2 The third product of the pixel values; based on the numerical relationships between the pixel value bit depth (which can be denoted as BD) and the first, second, and third products, the band category i to which the luminance component pixel belongs is determined. B For example, in the YUV color space, the luminance component is Y, the first chrominance component can be U, and the second chrominance component can be V. This application does not limit the color space used. In other words, when calculating the band category to which a color component pixel belongs, the extended co-position luminance reconstructed pixel and the true co-position chrominance component pixel corresponding to the luminance component pixel are used; when calculating the boundary category to which a color component pixel belongs, the extended co-position luminance reconstructed pixel corresponding to the luminance component pixel is used.

[0164] The following example uses color component pixels as luminance component pixels. When the extended co-position luminance reconstruction pixels are only used to calculate the boundary class of color component pixels... idxThe computer device can determine the first and second neighboring pixels corresponding to the extended co-position luminance reconstructed pixels based on the target classification pattern; obtain the first difference between the first neighboring pixels and the extended co-position luminance reconstructed pixels, and obtain the second difference between the second neighboring pixels and the extended co-position luminance reconstructed pixels; obtain the first and second co-position chrominance pixels corresponding to the real co-position luminance reconstructed pixels; determine the band category to which the luminance component pixels belong based on the real co-position luminance reconstructed pixels, the first co-position chrominance pixels, and the second co-position chrominance pixels; and determine the boundary category to which the color component pixels belong based on the band category, the first difference, and the second difference. In other words, when calculating the band category to which the color component pixels belong, the real co-position luminance reconstructed pixels (i.e., the luminance reconstructed pixels themselves after deblocking filtering) and the real co-position chrominance component pixels corresponding to the luminance component pixels are used; when calculating the boundary category to which the color component pixels belong, the extended co-position luminance reconstructed pixels corresponding to the luminance component pixels are used.

[0165] The following example uses color component pixels as luminance component pixels. When the extended co-position luminance reconstruction pixels are only used to calculate the band category i of the color component pixels... B The computer device can determine the third neighboring pixel 'a' and the fourth neighboring pixel 'b' corresponding to the true co-position luminance reconstructed pixel based on the target classification pattern; obtain the third difference Ea between the third neighboring pixel and the true co-position luminance reconstructed pixel, and obtain the fourth difference Eb between the fourth neighboring pixel and the true co-position luminance reconstructed pixel; obtain the first co-position chrominance pixel and the second co-position chrominance pixel corresponding to the true co-position luminance reconstructed pixel; determine the band category to which the luminance component pixel belongs based on the extended co-position luminance reconstructed pixel, the first co-position chrominance pixel, and the second co-position chrominance pixel; and determine the boundary category to which the color component pixel belongs based on the band category, the third difference, and the fourth difference. In other words, when calculating the band category to which the color component pixel belongs, the extended co-position luminance reconstructed pixel and the true co-position chrominance component pixel corresponding to the luminance component pixel are used; when calculating the boundary category to which the color component pixel belongs, the true co-position luminance reconstructed pixel is used.

[0166] Optionally, color component pixels can also be chroma component pixels (e.g., U component pixels, V component pixels). When determining the boundary class of a color component, the same operation as for the aforementioned luminance component pixels can be performed. For example, in the YUV color space, if a color component pixel is a U component pixel, the corresponding real-position U component reconstructed pixel (which can be denoted as cur) can be a real-position luminance reconstructed pixel or an extended luminance reconstructed pixel. The corresponding V component reconstructed pixel is a real-position U component reconstructed pixel. It should be noted that the boundary class... idxThe implementation process can be shown in the aforementioned formula (2), with category i B The process of obtaining can be shown in the aforementioned formula (3), and will not be repeated here.

[0167] Step S103: Compensate the reconstructed pixels of the color component pixels based on the boundary category to obtain the compensated reconstructed pixels, and use the compensated reconstructed pixels to encode the block to be encoded.

[0168] Specifically, computer devices can calculate boundary categories (classes). idx The corresponding compensation value, i.e., the boundary class output by CCSAO. idx The corresponding compensation value is σ as shown in formula (2). CCSAO [class idx This compensation value can be added to the reconstructed pixels of the color component pixels to obtain the compensated reconstructed pixels. In other words, based on the boundary class... idx The corresponding compensation value is used to compensate the reconstructed pixel of the color component pixel to obtain the compensated reconstructed pixel corresponding to the color component pixel. The reconstructed pixel of the color component pixel can refer to the pixel output after deblocking filtering; the compensated reconstructed pixel can refer to the reconstructed pixel output after CCSAO EO using the extended isotope brightness reconstructed pixel corresponding to the color component pixel to be processed in CCSAO, as shown in the aforementioned formula (2). The implementation process of the reconstructed pixels after compensation will not be described here.

[0169] Furthermore, when using extended isotope reconstructed pixels for CCSAO EO classification, the positions of the extended isotope reconstructed pixels corresponding to the color component pixels, as well as the target classification pattern for calculating the first and second differences, all need to be encoded and transmitted to the decoding end (e.g., Figure 8 The corresponding decoding device 40b in the embodiment.

[0170] In one or more embodiments, the computer device can set position syntax elements for the extended isotope reconstructed pixels corresponding to color component pixels, and set pattern syntax elements for the target classification patterns corresponding to the color component pixels. For color component pixels in the block to be encoded, different color component pixels can belong to different boundary categories, and each boundary category can correspond to a compensation value. The compensation values ​​corresponding to each boundary category in the video data can be summed, and the total compensation value can be encoded to obtain the encoding result corresponding to the total compensation value. The position syntax elements, pattern syntax elements, and the encoding result corresponding to the total compensation value for each color component pixel in the block to be encoded can then be determined as the encoded bitstream corresponding to the block to be encoded, and this encoded bitstream can be transmitted to the decoding end. In other words, the computer device can encode the position of the extended isotope reconstructed pixels separately, such as by indexing the position of the extended isotope reconstructed pixels using dedicated syntax elements (i.e., position syntax elements); the target classification pattern can reuse the existing syntax elements (i.e., pattern syntax elements) used to identify the classification pattern in the CCSAO EO classification.

[0171] In one or more embodiments, a computer device can set joint syntax elements for the extended co-occurrence brightness reconstructed pixels corresponding to color component pixels and the target classification mode; based on the joint syntax elements and the total compensation value corresponding to each boundary category in the video data, the encoded bitstream of the block to be encoded is determined, and the encoded bitstream can be transmitted to the decoding end. In other words, the extended co-occurrence brightness reconstructed pixels corresponding to color component pixels can be encoded together with their corresponding neighboring pixels (including the first neighboring pixel and the second neighboring pixel). For example, each possible position of the extended co-occurrence brightness reconstructed pixels corresponding to color component pixels, and the available classification mode corresponding to each position can be used as a new mode in CCSAO EO classification, and then the syntax elements (joint syntax elements) identifying the classification mode in the existing CCSAO EO can be reused for transmission; therefore, a common joint syntax element can be set for the extended co-occurrence brightness reconstructed pixels corresponding to color component pixels and the target classification mode.

[0172] It should be noted that for color component pixels in the block to be encoded, CCSAO EO classification can be performed using extended co-occurrence brightness reconstruction pixels or not. If the rate-distortion loss when not using extended co-occurrence brightness reconstruction pixels is less than that when using extended co-occurrence brightness reconstruction pixels, then CCSAO EO classification can be performed without using extended co-occurrence brightness reconstruction pixels. If the rate-distortion loss when not using extended co-occurrence brightness reconstruction pixels is greater than that when using extended co-occurrence brightness reconstruction pixels, then CCSAO EO classification can be performed using extended co-occurrence brightness reconstruction pixels.

[0173] Optionally, during the video data encoding process, a flag bit can be used to indicate whether extended co-location brightness reconstruction pixels are used (extended co-location brightness reconstruction pixels here specifically refer to...). Figure 10 The flag bit (shown as a luminance reconstructed pixel at any of positions 0 to 7) and the extended classification mode can be called the classification identifier field. For example, if extended isotope luminance reconstructed pixels and the extended classification mode are selected for CCSAO EO classification, this classification identifier field can be set to a first identifier value; if extended isotope luminance reconstructed pixels and the extended classification mode are not selected for CCSAO EO classification, this classification identifier field can be set to a second identifier value. The first and second identifier values ​​here can be different identifier values, and both the first and second identifier values ​​can include, but are not limited to, symbols, numerical values, labels, etc.

[0174] The aforementioned classification identifier field can be transmitted in HLS (High Level Syntax). This classification identifier field can be stored in the Sequence Parameter Set (SPS), Picture Header, Slice Header, or APS; this application does not limit its location. If the classification identifier field is stored in the SPS, it indicates whether the video data uses extended isotope reconstruction pixels and extended classification mode. If the classification identifier field is stored in the Picture Header, it indicates whether the current image frame uses extended isotope reconstruction pixels and extended classification mode. If the classification identifier field is stored in the Slice Header, it indicates whether the current slice uses extended isotope reconstruction pixels and extended classification mode. If the classification identifier field is stored in the APS, it indicates whether the loop filtering uses extended isotope reconstruction pixels and extended classification mode.

[0175] Optionally, different flag bits can be used to identify whether extended simulacrum reconstruction pixels and extended classification modes are used for CCSAO EO classification. In this case, the classification identifier field can include an extended simulacrum identifier field and an extended mode identifier field. For example, if extended simulacrum reconstruction pixels are selected for CCSAO EO classification, the extended simulacrum identifier field can be set to a first identifier value; if extended simulacrum reconstruction pixels are not selected for CCSAO EO classification, the extended simulacrum identifier field can be set to a second identifier value. If extended classification mode is selected for CCSAO EO classification, the extended mode identifier field can also be set to a first identifier value; if extended classification mode is not selected for CCSAO EO classification, the extended mode identifier field can also be set to a second identifier value. Both the extended simulacrum identifier field and the extended mode identifier field can be transmitted in HLS, and the classification identifier field can be stored in SPS, PictureHeader, SliceHeader, or APS.

[0176] It should be noted that when it is determined that CCSAO EO classification is performed on video data using one or both of extended isotope luminance reconstruction pixels and extended classification mode, the classification identifier field can be set to the first identifier value, and then the video data can be encoded according to the technical solution of the embodiments of this application; if it is determined that extended isotope luminance reconstruction pixels and extended classification mode are not used on video data, the classification identifier field can be set to the second identifier value, and then the video data can be encoded according to the existing method.

[0177] In this embodiment, the encoding device can perform CCSAO EO classification by inputting the extended isotopic luminance reconstructed pixels corresponding to the color component pixels. At the same time, it can also use extended classification modes (including other available classification modes besides the aforementioned four classification modes: horizontal, vertical, diagonal, and anti-diagonal) to perform CCSAO EO classification, which can improve the accuracy of the CCSAO EO category of the color component pixels, thereby improving the overall encoding performance of the video data.

[0178] Please see Figure 15 , Figure 15 This is a schematic flowchart illustrating another data processing method provided in an embodiment of this application. It can be understood that this data processing method can be executed by a computer device, which can be... Figure 8 The decoding device 40b shown corresponds to this data processing method, which is a decoding method. For example, Figure 15 As shown, the data processing method may include the following steps S201 to S203:

[0179] Step S201: Decode the block to be decoded in the video data to obtain the classification mode information corresponding to the block to be decoded; the classification mode information includes the extended co-position brightness reconstruction pixel corresponding to the color component pixel and the target classification mode; the extended co-position brightness reconstruction pixel belongs to the target region centered on the true co-position brightness reconstruction pixel of the color component pixel.

[0180] Specifically, the computer device can parse the position syntax elements corresponding to the color component pixels to obtain the extended isotope brightness reconstructed pixels corresponding to the color component pixels; it can also parse the pattern syntax elements corresponding to the color component pixels to obtain the target classification pattern corresponding to the color component pixels, and determine the extended isotope brightness reconstructed pixels and the target classification pattern as the classification pattern information corresponding to the block to be decoded. In other words, the computer device can parse the position syntax elements (extended isotope brightness reconstructed pixel position-related syntax elements) to determine the position of the extended isotope brightness reconstructed pixels, parse the pattern syntax elements (classification pattern-related syntax elements) to determine the target classification pattern, and then calculate the difference between the position of the extended isotope brightness reconstructed pixels and the surrounding pixels in the area covered by the target classification pattern to perform boundary classification.

[0181] In one or more embodiments, a computer device can parse the joint syntax elements corresponding to color component pixels to obtain the extended isotope reconstructed pixels and the target classification mode corresponding to the color component pixels, and determine the extended isotope reconstructed pixels and the target classification mode as the classification mode information corresponding to the block to be decoded. In other words, the computer device can parse the joint syntax elements (syntax elements related to the classification mode), and then determine the position of the extended isotope reconstructed pixels for classification and the target classification mode based on the mode definition set consistently by the encoding and decoding ends.

[0182] Before parsing the classification mode information corresponding to the color component pixels in the block to be decoded, the computer device can first parse the classification identifier field in the sequence parameter set corresponding to the video data. If the classification identifier field is a first identifier value, it is determined that the video data uses extended isotope reconstruction pixels; if the classification identifier field is a second identifier value, it is determined that the video data does not use extended isotope reconstruction pixels. For example, if the classification identifier field in the sequence parameter set corresponding to the video data is a first identifier value, it means that the video data uses extended isotope reconstruction pixels and an extended classification mode; while when decoding a certain image frame in the video data, if the classification identifier field in the PictureHeader is parsed to be a second identifier value, it means that the currently processed image frame does not use extended isotope reconstruction pixels and an extended classification mode.

[0183] Optionally, if the classification identifier field includes an extended isotope identifier field for extended isotope reconstructed pixels and an extended classification pattern identifier field for extended classification patterns, the extended isotope identifier field and the extended classification pattern identifier field can be parsed sequentially first. Based on the parsing results, it can be determined whether the video data used extended isotope reconstructed pixels and extended classification patterns. If the parsing result indicates that the video data used one or both of extended isotope reconstructed pixels and extended classification patterns for CCSAO, subsequent decoding processing can be performed according to the technical solution provided in the embodiments of this application. If the parsing result indicates that the video data did not use extended isotope reconstructed pixels and extended classification patterns for CCSAO EO classification, subsequent decoding processing can be performed according to the existing solution.

[0184] Step S202: Determine the boundary category corresponding to the color component pixel based on the extended iso-luminance reconstructed pixel and the target classification mode.

[0185] The implementation process of step S202 can be found in the aforementioned document. Figure 9 Step S102 in the corresponding embodiment will not be described again here.

[0186] Step S203: Reconstruct the color component pixels in the block to be decoded based on the compensation value corresponding to the boundary category.

[0187] Specifically, computer equipment can determine the compensated reconstructed pixels based on the compensation values ​​corresponding to the boundary categories. These compensated reconstructed pixels can then be used to reconstruct the color component pixels in the block to be decoded. Specifically, the total compensation value corresponding to each boundary category in the video data can be parsed. By parsing the target classification pattern and the extended isotope reconstructed pixels, the boundary category corresponding to the color component pixels in the current block to be decoded can be determined. Furthermore, the compensation value corresponding to that boundary category can be determined from the total compensation value; that is, the compensation value corresponding to a single boundary category. Using the compensation value corresponding to a single boundary category, the color component pixels belonging to that boundary category can be reconstructed.

[0188] It should be noted that the video data decoding process is the inverse of the video data encoding process; therefore, the video data decoding process can be found in the aforementioned section. Figure 9 The descriptions in the corresponding embodiments will not be repeated here.

[0189] In this embodiment, the encoding device can perform CCSAO EO classification by inputting the extended iso-iso-luminance reconstructed pixels corresponding to the color component pixels. At the same time, it can also use extended classification modes (including other available classification modes besides the aforementioned four classification modes: horizontal, vertical, diagonal, and anti-diagonal) to perform CCSAO EO classification, which can improve the accuracy of the CCSAO EO classification of the color component pixels, thereby improving the overall decoding performance of the video data.

[0190] Please see Figure 16 , Figure 16 This is a schematic diagram of the structure of a data processing apparatus provided in an embodiment of this application; the data processing apparatus may be a computer program (including program code) running in an encoding device, for example, the data processing apparatus may be application software in the encoding device. Please see [link to relevant documentation]. Figure 16 The data processing device 1 may include a classification pattern information determination module 11, a first category determination module 12, and an encoding processing module 13;

[0191] The classification mode information determination module 11 is used to determine the classification mode information corresponding to the block to be encoded in the video data; the classification mode information includes the extended co-position brightness reconstruction pixel corresponding to the color component pixel in the block to be encoded and the target classification mode; the extended co-position brightness reconstruction pixel belongs to the target region centered on the true co-position brightness reconstruction pixel of the color component pixel;

[0192] The first category determination module 12 is used to determine the boundary category corresponding to the color component pixel based on the extended iso-brightness reconstructed pixel and the target classification mode;

[0193] The encoding processing module 13 is used to compensate the reconstructed pixels of the color component pixels based on the boundary category, so as to obtain the compensated reconstructed pixels, and to encode the block to be encoded using the compensated reconstructed pixels.

[0194] In one or more embodiments, the classification pattern information determination module 11 is specifically used for:

[0195] Obtain the color component pixels contained in the block to be encoded in the video data, and obtain the real co-position brightness reconstructed pixels corresponding to the color component pixels;

[0196] Using the real iso-saturation reconstructed pixel as the region center, a target region with a target size is determined, and the extended iso-saturation reconstructed pixel corresponding to the color component pixel is determined from the target region.

[0197] Obtain the candidate classification mode set corresponding to the color component pixels, determine the target classification mode corresponding to the color component pixels in the candidate classification mode set, and add the extended isotopic brightness reconstructed pixels and the target classification mode to the classification mode information corresponding to the block to be encoded.

[0198] In one or more embodiments, the classification pattern information determination module 11 determines the extended isotopic brightness reconstructed pixels corresponding to color component pixels within the target region, including:

[0199] The brightness reconstructed pixels in the target area, excluding vertex positions, are determined as the candidate brightness pixel set corresponding to the color component pixels;

[0200] For each luminance reconstructed pixel in the candidate luminance pixel set, rate-distortion optimization is performed on the color component pixels to obtain the first rate-distortion loss corresponding to each luminance reconstructed pixel in the candidate luminance pixel set.

[0201] The luminance reconstruction pixel corresponding to the minimum first rate distortion loss is determined as the extended isotope luminance reconstruction pixel corresponding to the color component pixel.

[0202] In one or more embodiments, the classification pattern information determination module 11 acquires a set of candidate classification patterns corresponding to color component pixels, and determines the target classification pattern corresponding to the color component pixels from the set of candidate classification patterns, including:

[0203] Based on the position of the reconstructed pixel in the target region using extended iso-brightness, a set of candidate classification patterns corresponding to the color component pixels is determined; the coverage of the classification patterns in the candidate classification pattern set is less than or equal to the target region, and the candidate classification pattern set includes horizontal classification patterns and vertical classification patterns.

[0204] For each classification mode in the candidate classification mode set, rate distortion optimization is performed on the color component pixels to obtain the second rate distortion loss corresponding to each classification mode in the candidate classification mode set.

[0205] The classification pattern corresponding to the minimum second rate distortion loss is determined as the target classification pattern corresponding to the color component pixels.

[0206] In one or more embodiments, the extended co-position brightness reconstruction pixel is any brightness reconstruction pixel in the target region;

[0207] Data processing device 1 is also used for:

[0208] If the coverage of a candidate classification pattern is greater than that of the target region, then the boundary pixels in the target region are filled into the adjacent regions of the target region to obtain the pattern coverage. The pattern coverage is used to determine the target classification pattern from the candidate classification pattern set.

[0209] In one or more embodiments, when the extended co-position luminance reconstruction pixel is any luminance reconstruction pixel in the target region, the candidate classification mode set includes a basic classification mode and an extended classification mode, or the candidate classification mode set includes an extended classification mode; the basic classification mode includes a horizontal classification mode, a vertical classification mode, a diagonal classification mode, and an anti-diagonal classification mode, and the extended classification mode refers to the available classification modes other than the basic classification mode.

[0210] In one or more embodiments, the color component pixels include luminance component pixels or chrominance component pixels.

[0211] In one or more embodiments, the color component pixels include the luminance component pixels; the first category determination module 12 is specifically used for:

[0212] Determine the first and second adjacent pixels corresponding to the extended isotopic brightness reconstruction pixels based on the target classification pattern;

[0213] Obtain a first difference between a first adjacent pixel and an extended co-position brightness reconstructed pixel, and obtain a second difference between a second adjacent pixel and an extended co-position brightness reconstructed pixel;

[0214] Obtain the first and second isochromatic pixels corresponding to the real isochromatic reconstructed pixels;

[0215] Based on the extended isotopic luminance reconstructed pixels, the first isotopic chrominance pixels, and the second isotopic chrominance pixels, the band category to which the luminance component pixels belong is determined;

[0216] Based on the band category, the first difference, and the second difference, the boundary category corresponding to the color component pixel is determined.

[0217] The first category determination module 12 determines the band category to which the luminance component pixel belongs based on the extended isotopic luminance reconstruction pixel, the first isotopic chrominance pixel, and the second isotopic chrominance pixel, including:

[0218] Obtain the first product of the extended co-position luminance reconstructed pixel and the total number of band categories on the luminance component, obtain the second product of the first co-position chrominance pixel and the total number of band categories on the first chrominance component, and obtain the third product of the second co-position chrominance pixel and the total number of band categories on the second chrominance component.

[0219] Based on the numerical relationships between the pixel value bit depth and the first, second, and third products, the band category to which the luminance component pixel belongs is determined.

[0220] In one or more embodiments, the color component pixels include the luminance component pixels; the first category determination module 12 is specifically used for:

[0221] Determine the first and second adjacent pixels corresponding to the extended isotopic brightness reconstruction pixels based on the target classification pattern;

[0222] Obtain a first difference between a first adjacent pixel and an extended co-position brightness reconstructed pixel, and obtain a second difference between a second adjacent pixel and an extended co-position brightness reconstructed pixel;

[0223] Obtain the first and second isochromatic pixels corresponding to the real isochromatic reconstructed pixels;

[0224] Based on the real co-position luminance reconstructed pixels, the first co-position chrominance pixels, and the second co-position chrominance pixels, the band category to which the luminance component pixels belong is determined;

[0225] Based on the band category, the first difference, and the second difference, the boundary category corresponding to the color component pixel is determined.

[0226] In one or more embodiments, the color component pixels include the luminance component pixels; the first category determination module 12 is specifically used for:

[0227] Based on the target classification pattern, determine the third and fourth adjacent pixels corresponding to the real iso-position brightness reconstructed pixels;

[0228] Obtain the third difference between the third adjacent pixel and the real co-position brightness reconstructed pixel, and obtain the fourth difference between the fourth adjacent pixel and the real co-position brightness reconstructed pixel;

[0229] Obtain the first and second isochromatic pixels corresponding to the real isochromatic reconstructed pixels;

[0230] Based on the extended isotopic luminance reconstructed pixels, the first isotopic chrominance pixels, and the second isotopic chrominance pixels, the band category to which the luminance component pixels belong is determined;

[0231] Based on the band category, the third difference, and the fourth difference, determine the boundary category corresponding to the color component pixel.

[0232] In one or more embodiments, the encoding processing module 13 encodes the block to be encoded using the compensated reconstructed pixels, including:

[0233] To expand the reconstructed pixels by the same brightness, set the position syntax element; to set the pattern syntax element for the target classification pattern.

[0234] Determine the compensation value corresponding to the boundary category, and obtain the encoded bit stream of the block to be encoded based on the positional syntax element, the pattern syntax element, and the compensation value. Transmit the encoded bit stream to the decoding end.

[0235] In one or more embodiments, the encoding processing module 13 encodes the block to be encoded using the compensated reconstructed pixels, including:

[0236] To set up joint syntax elements for expanding the reconstructed pixels by co-position brightness and the target classification mode;

[0237] Determine the compensation value corresponding to the boundary category, obtain the encoded bit stream of the block to be encoded based on the joint syntax element and the compensation value, and transmit the encoded bit stream to the decoding end.

[0238] According to the embodiments of this application, Figure 16 The data processing apparatus 1 shown can be composed of one or more additional units, either individually or entirely. Alternatively, some of the units can be further divided into smaller functional units. This achieves the same operation without affecting the technical effects of the embodiments of this application. The units are divided based on logical functions. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the data processing apparatus 1 may also include other units. In practical applications, these functions can be implemented with the assistance of other units, and multiple units can collaborate to achieve them.

[0239] In this embodiment, the encoding device can perform CCSAO EO classification by inputting the extended isotopic luminance reconstructed pixels corresponding to the color component pixels. At the same time, it can also use extended classification modes (including other available classification modes besides the aforementioned four classification modes: horizontal, vertical, diagonal, and anti-diagonal) to perform CCSAO EO classification, which can improve the accuracy of the CCSAO EO category of the color component pixels, thereby improving the overall encoding performance of the video data.

[0240] Please see Figure 17 , Figure 17 This is a schematic diagram of another data processing apparatus provided in an embodiment of this application; the data processing apparatus can be a computer program (including program code) running in the decoding device, for example, the data processing apparatus is application software in the decoding device. Please see... Figure 17 The data processing device 2 may include a decoding processing module 21, a second category determination module 22, and a pixel reconstruction module 23;

[0241] Decoding module 21 is used to decode the block to be decoded in the video data to obtain the classification mode information corresponding to the block to be decoded; the classification mode information includes the extended co-position brightness reconstruction pixel corresponding to the color component pixel and the target classification mode; the extended co-position brightness reconstruction pixel belongs to the target area centered on the true co-position brightness reconstruction pixel of the color component pixel;

[0242] The second category determination module 22 is used to determine the boundary category corresponding to the color component pixel based on the extended iso-brightness reconstructed pixel and the target classification mode;

[0243] The pixel reconstruction module 23 is used to reconstruct the color component pixels in the block to be decoded based on the compensation value corresponding to the boundary category.

[0244] In one or more embodiments, the decoding processing module 21 is specifically used for:

[0245] Parse the position syntax elements corresponding to the color component pixels to obtain the extended iso-position brightness reconstructed pixels corresponding to the color component pixels;

[0246] Parse the pattern syntax elements corresponding to the color component pixels to obtain the target classification pattern corresponding to the color component pixels, and determine the extended isotopic brightness reconstructed pixels and the target classification pattern as the classification pattern information corresponding to the block to be decoded.

[0247] In one or more embodiments, the decoding processing module 21 is specifically used for:

[0248] The joint syntax element corresponding to the color component pixels is parsed to obtain the extended isotope brightness reconstruction pixels and target classification mode corresponding to the color component pixels. The extended isotope brightness reconstruction pixels and target classification mode are determined as the classification mode information corresponding to the block to be decoded.

[0249] The data device 2 is also used for:

[0250] Parse the classification identifier field in the sequence parameter set corresponding to the video data;

[0251] If the classification identifier field is the first identifier value, then it is determined that the video data used extended iso-saturation reconstructed pixels;

[0252] If the classification identifier field is the second identifier value, it is determined that the video data did not use extended iso-luminance pixel reconstruction.

[0253] According to the embodiments of this application, Figure 17 The data processing device 2 shown can be composed of one or more additional units, either individually or entirely. Alternatively, some of the units can be further divided into smaller functional units. This achieves the same operation without affecting the technical effects of the embodiments of this application. The units are based on logical function. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the data processing device 2 may also include other units. In practical applications, these functions can be implemented with the assistance of other units, and multiple units can collaborate to achieve them.

[0254] In this embodiment, the encoding device can perform CCSAO EO classification by inputting the extended iso-iso-luminance reconstructed pixels corresponding to the color component pixels. At the same time, it can also use extended classification modes (including other available classification modes besides the aforementioned four classification modes: horizontal, vertical, diagonal, and anti-diagonal) to perform CCSAO EO classification, which can improve the accuracy of the CCSAO EO classification of the color component pixels, thereby improving the overall decoding performance of the video data.

[0255] Further, please see Figure 18 , Figure 18 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 18 As shown, the computer device 1000 can be a terminal device or a server, and this application does not limit this. For ease of understanding, this application takes the computer device as a terminal device as an example. The computer device 1000 may include: a processor 1001, a network interface 1004, and a memory 1005. In addition, the computer device 1000 may also include: a user interface 1003, and at least one communication bus 1002. The communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The memory 1005 may also optionally be at least one storage device located remotely from the aforementioned processor 1001. Figure 18 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a device control application.

[0256] The network interface 1004 in the computer device 1000 can also provide network communication functions, and the optional user interface 1003 can also include a display screen and a keyboard. Figure 18 In the computer device 1000 shown, the network interface 1004 provides network communication functions; while the user interface 1003 is mainly used to provide an input interface for users.

[0257] When the computer device 1000 is an encoding device, the processor 1001 can be used to call the device control application stored in the memory 1005 to achieve:

[0258] Determine the classification mode information corresponding to the block to be encoded in the video data; the classification mode information includes the extended isotope reconstructed pixels corresponding to the color component pixels in the block to be encoded and the target classification mode; the extended isotope reconstructed pixels belong to the target region centered on the true isotope reconstructed pixels of the color component pixels;

[0259] Based on the extended iso-luminance reconstructed pixels and the target classification pattern, determine the boundary category corresponding to the color component pixels;

[0260] The reconstructed pixels of color component pixels are compensated based on the boundary category to obtain compensated reconstructed pixels, and the block to be encoded is encoded using the compensated reconstructed pixels.

[0261] When the computer device 1000 is a decoding device, the processor 1001 can be used to call the device control application stored in the memory 1005 to achieve:

[0262] The video data block to be decoded is decoded to obtain the classification mode information corresponding to the block to be decoded. The classification mode information includes the extended isotope reconstructed pixel corresponding to the color component pixel and the target classification mode. The extended isotope reconstructed pixel belongs to the target region centered on the true isotope reconstructed pixel of the color component pixel.

[0263] Based on the extended iso-luminance reconstructed pixels and the target classification pattern, determine the boundary category corresponding to the color component pixels;

[0264] The color component pixels in the block to be decoded are reconstructed based on the compensation value corresponding to the boundary category.

[0265] It should be understood that the computer device 1000 described in the embodiments of this application can execute the foregoing text. Figure 9 or Figure 15 The description of the data processing method in the corresponding embodiments can also be performed as described above. Figure 16 The corresponding embodiment refers to data processing device 1 or the foregoing text Figure 17 The description of the data processing device 2 in the corresponding embodiments will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated.

[0266] Furthermore, it should be noted that this application embodiment also provides a computer-readable storage medium, which stores a computer program executed by the aforementioned data processing device 1 or cloud processing device 2. The computer program includes computer instructions, and when the processor executes the computer instructions, it can execute the aforementioned... Figure 9 or Figure 15The description of the data processing method in the corresponding embodiments is already provided and will not be repeated here. Similarly, the beneficial effects of using the same method will not be repeated here either. For technical details not disclosed in the computer-readable storage medium embodiments related to this application, please refer to the description of the method embodiments of this application. As an example, computer instructions can be deployed and executed on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network. These multiple computing devices distributed across multiple locations and interconnected via a communication network can constitute a blockchain system.

[0267] Furthermore, it should be noted that this application also provides a computer program product or computer program, which may include computer instructions, which may be stored in a computer-readable storage medium. The processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor may execute the computer instructions, causing the computer device to perform the aforementioned actions. Figure 9 or Figure 15 The description of the data processing method in the corresponding embodiments is already provided and will not be repeated here. Furthermore, the beneficial effects of using the same method will also not be repeated. For technical details not disclosed in the computer program products or computer program embodiments related to this application, please refer to the description of the method embodiments of this application.

[0268] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0269] The steps in the method of this application embodiment can be adjusted, combined, or deleted according to actual needs.

[0270] The modules in the device of this application embodiment can be merged, divided, and deleted according to actual needs.

[0271] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0272] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A data processing method, characterized in that, include: Determine the classification pattern information corresponding to the block to be encoded in the video data; The classification pattern information includes the extended isotope brightness reconstruction pixels corresponding to the color component pixels in the block to be encoded and the target classification pattern; the extended isotope brightness reconstruction pixels belong to the target region centered on the true isotope brightness reconstruction pixels of the color component pixels; Based on the extended isotopic brightness reconstructed pixels and the target classification mode, determine the boundary category corresponding to the color component pixels; The reconstructed pixels of the color component pixels are compensated based on the boundary category to obtain compensated reconstructed pixels, and the block to be encoded is encoded using the compensated reconstructed pixels.

2. The method according to claim 1, characterized in that, The determination of the classification mode information corresponding to the block to be encoded in the video data includes: Obtain the color component pixels contained in the block to be encoded in the video data, and obtain the real co-position brightness reconstructed pixels corresponding to the color component pixels; Using the real isotopic brightness reconstructed pixel as the region center, a target region with a target size is determined, and the extended isotopic brightness reconstructed pixel corresponding to the color component pixel is determined from the target region. Obtain a set of candidate classification modes corresponding to the color component pixels, determine the target classification mode corresponding to the color component pixels in the set of candidate classification modes, and add the extended isotopic brightness reconstructed pixels and the target classification mode to the classification mode information corresponding to the block to be encoded.

3. The method according to claim 2, characterized in that, Determining the extended co-position luminance reconstructed pixel corresponding to the color component pixel from the target region includes: The brightness reconstructed pixels in the target region, excluding vertex positions, are determined as the candidate brightness pixel set corresponding to the color component pixels; For each luminance reconstructed pixel in the candidate luminance pixel set, rate-distortion optimization processing is performed on the color component pixels to obtain the first rate-distortion loss corresponding to each luminance reconstructed pixel in the candidate luminance pixel set. The luminance reconstruction pixel corresponding to the minimum first rate distortion loss is determined as the extended isotopic luminance reconstruction pixel corresponding to the color component pixel.

4. The method according to claim 3, characterized in that, The step of obtaining a set of candidate classification modes corresponding to the color component pixels and determining the target classification mode corresponding to the color component pixels from the set of candidate classification modes includes: Based on the position of the extended iso-brightness reconstructed pixel in the target region, a candidate classification mode set corresponding to the color component pixel is determined; the coverage of the classification mode in the candidate classification mode set is less than or equal to the target region, and the candidate classification mode set includes horizontal classification mode and vertical classification mode; For each classification mode in the candidate classification mode set, rate-distortion optimization processing is performed on the color component pixels to obtain the second rate-distortion loss corresponding to each classification mode in the candidate classification mode set. The classification mode corresponding to the minimum second rate distortion loss is determined as the target classification mode corresponding to the color component pixel.

5. The method according to claim 2, characterized in that, The extended co-position brightness reconstruction pixel is any brightness reconstruction pixel in the target region; The method further includes: If the coverage of a classification pattern in the candidate classification pattern set is greater than that of the target region, then the boundary pixels in the target region are filled into the adjacent regions of the target region to obtain the pattern coverage; the pattern coverage is used to determine the target classification pattern from the candidate classification pattern set.

6. The method according to claim 2, characterized in that, When the extended co-position brightness reconstruction pixel is any brightness reconstruction pixel in the target region, the candidate classification mode set includes a basic classification mode and an extended classification mode, or the candidate classification mode set includes an extended classification mode; the basic classification mode includes a horizontal classification mode, a vertical classification mode, a diagonal classification mode, and an anti-diagonal classification mode, and the extended classification mode refers to the available classification modes other than the basic classification mode.

7. The method according to claim 1, characterized in that, The color component pixels include either luminance component pixels or chrominance component pixels.

8. The method according to claim 1, characterized in that, The color component pixels include the luminance component pixels; The step of determining the boundary category corresponding to the color component pixel based on the extended isotopic brightness reconstructed pixel and the target classification pattern includes: Based on the target classification pattern, determine the first and second adjacent pixels corresponding to the extended isotopic brightness reconstructed pixels; Obtain a first difference between the first adjacent pixel and the extended co-position brightness reconstructed pixel, and obtain a second difference between the second adjacent pixel and the extended co-position brightness reconstructed pixel; Obtain the first and second isochromatic pixels corresponding to the real isochromatic reconstructed pixels; Based on the extended isotopic luminance reconstructed pixel, the first isotopic chrominance pixel, and the second isotopic chrominance pixel, the band category to which the luminance component pixel belongs is determined; Based on the band category, the first difference, and the second difference, the boundary category corresponding to the color component pixel is determined.

9. The method according to claim 8, characterized in that, The step of determining the band category to which the luminance component pixel belongs based on the extended co-position luminance reconstructed pixel, the first co-position chrominance pixel, and the second co-position chrominance pixel includes: Obtain the first product of the extended co-position luminance reconstructed pixel and the total number of band categories on the luminance component, obtain the second product of the first co-position chrominance pixel and the total number of band categories on the first chrominance component, and obtain the third product of the second co-position chrominance pixel and the total number of band categories on the second chrominance component. Based on the numerical relationships between the pixel value bit depth and the first product, the second product, and the third product, the band category to which the luminance component pixel belongs is determined.

10. The method according to claim 1, characterized in that, The color component pixels include the luminance component pixels; The step of determining the boundary category corresponding to the color component pixel based on the extended isotopic brightness reconstructed pixel and the target classification pattern includes: Based on the target classification pattern, determine the first and second adjacent pixels corresponding to the extended isotopic brightness reconstructed pixels; Obtain a first difference between the first adjacent pixel and the extended co-position brightness reconstructed pixel, and obtain a second difference between the second adjacent pixel and the extended co-position brightness reconstructed pixel; Obtain the first and second isochromatic pixels corresponding to the real isochromatic reconstructed pixels; Based on the real isotopic luminance reconstructed pixel, the first isotopic chrominance pixel, and the second isotopic chrominance pixel, the band category to which the luminance component pixel belongs is determined; Based on the band category, the first difference, and the second difference, the boundary category corresponding to the color component pixel is determined.

11. The method according to claim 1, characterized in that, The color component pixels include the luminance component pixels; The step of determining the boundary category corresponding to the color component pixel based on the extended isotopic brightness reconstructed pixel and the target classification pattern includes: Based on the target classification pattern, determine the third and fourth adjacent pixels corresponding to the real isotopic brightness reconstructed pixels; Obtain the third difference between the third adjacent pixel and the real co-position brightness reconstructed pixel, and obtain the fourth difference between the fourth adjacent pixel and the real co-position brightness reconstructed pixel; Obtain the first and second isochromatic pixels corresponding to the real isochromatic reconstructed pixels; Based on the extended isotopic luminance reconstructed pixel, the first isotopic chrominance pixel, and the second isotopic chrominance pixel, the band category to which the luminance component pixel belongs is determined; The boundary category corresponding to the color component pixel is determined based on the band category, the third difference, and the fourth difference.

12. The method according to any one of claims 1-11, characterized in that, The encoding process of the block to be encoded using the compensated reconstructed pixels includes: Set position syntax elements for the extended isotopic brightness reconstructed pixels, and set pattern syntax elements for the target classification pattern; Determine the compensation value corresponding to the boundary category, obtain the encoded bitstream of the block to be encoded based on the position syntax element, the pattern syntax element, and the compensation value, and transmit the encoded bitstream to the decoding end.

13. The method according to any one of claims 1-11, characterized in that, The encoding process of the block to be encoded using the compensated reconstructed pixels includes: Set a joint syntax element for the extended isotopic brightness reconstructed pixels and the target classification pattern; Determine the compensation value corresponding to the boundary category, obtain the encoded bit stream of the block to be encoded based on the joint syntax element and the compensation value, and transmit the encoded bit stream to the decoding end.

14. A data processing method, characterized in that, include: The video data is decoded to obtain the classification mode information corresponding to the block to be decoded. The classification pattern information includes the extended isotope brightness reconstruction pixels corresponding to the color component pixels and the target classification pattern; the extended isotope brightness reconstruction pixels belong to the target region centered on the true isotope brightness reconstruction pixels of the color component pixels; Based on the extended isotopic brightness reconstructed pixels and the target classification mode, determine the boundary category corresponding to the color component pixels; The color component pixels in the block to be decoded are reconstructed based on the compensation value corresponding to the boundary category.

15. The method according to claim 14, characterized in that, The process of decoding the block to be decoded in the video data to obtain the classification mode information corresponding to the block to be decoded includes: Parse the position syntax elements corresponding to the color component pixels to obtain the extended co-position brightness reconstructed pixels corresponding to the color component pixels; Parse the pattern syntax elements corresponding to the color component pixels to obtain the target classification pattern corresponding to the color component pixels, and determine the extended isotopic brightness reconstructed pixels and the target classification pattern as the classification pattern information corresponding to the block to be decoded.

16. The method according to claim 14, characterized in that, The process of decoding the block to be decoded in the video data to obtain the classification mode information corresponding to the block to be decoded includes: The joint syntax element corresponding to the color component pixel is parsed to obtain the extended co-position brightness reconstruction pixel and the target classification mode corresponding to the color component pixel. The extended co-position brightness reconstruction pixel and the target classification mode are determined as the classification mode information corresponding to the block to be decoded.

17. The method according to any one of claims 14-16, characterized in that, Also includes: Parse the classification identifier field in the sequence parameter set corresponding to the video data; If the classification identifier field is the first identifier value, then it is determined that the video data uses extended iso-saturation reconstructed pixels; If the classification identifier field is the second identifier value, then it is determined that the video data did not use the extended iso-luminance to reconstruct pixels.

18. A data processing apparatus, characterized in that, include: The classification pattern information determination module is used to determine the classification pattern information corresponding to the block to be encoded in the video data; The classification pattern information includes the extended isotope brightness reconstruction pixels corresponding to the color component pixels in the block to be encoded and the target classification pattern; the extended isotope brightness reconstruction pixels belong to the target region centered on the true isotope brightness reconstruction pixels of the color component pixels; The first category determination module is used to determine the boundary category corresponding to the color component pixel based on the extended iso-saturation reconstructed pixel and the target classification mode; The encoding processing module is used to compensate the reconstructed pixels of the color component pixels based on the boundary category to obtain compensated reconstructed pixels, and to encode the block to be encoded using the compensated reconstructed pixels.

19. A data processing apparatus, characterized in that, include: The decoding processing module is used to decode the block to be decoded in the video data to obtain the classification mode information corresponding to the block to be decoded; The classification pattern information includes the extended isotope brightness reconstruction pixels corresponding to the color component pixels and the target classification pattern; the extended isotope brightness reconstruction pixels belong to the target region centered on the true isotope brightness reconstruction pixels of the color component pixels; The second category determination module is used to determine the boundary category corresponding to the color component pixel based on the extended isotopic brightness reconstructed pixel and the target classification mode; The pixel reconstruction module is used to reconstruct the color component pixels in the block to be decoded based on the compensation value corresponding to the boundary category.

20. A computer device, characterized in that, Including memory and processor; The memory is connected to the processor, the memory is used to store a computer program, and the processor is used to invoke the computer program so that the computer device performs the method according to any one of claims 1 to 13, or performs the method according to any one of claims 14 to 17.

21. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded and executed by a processor to cause a computer device having the processor to perform the method of any one of claims 1 to 13, or to perform the method of any one of claims 14 to 17.

22. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed by a processor, implement the method of any one of claims 1 to 13, or the method of any one of claims 14 to 17.

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