Data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring a Virnahoff equation associated with each type of pixel block in a to-be-coded image frame, grouping the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, and merging the Virnahoff equations associated with different types of pixel blocks in a target group to obtain N groups of pixel blocks; and combining the image frame with the image frame, taking the combined Virnahoff equation as a Virnahoff equation associated with the target group, and determining a filter coefficient group corresponding to the image frame based on the Virnahoff equation associated with the N groups of pixel blocks, the filter coefficient group being used for generating code stream data of the image frame. Visibly, by grouping the pixel blocks in the image frame and merging the Virnahoff equations associated with the different types of pixel blocks in the same group, the number of the Virnahoff equations needing to be solved in the encoding process can be reduced, and the calculation amount of rate distortion optimization of the filter coefficient group can be reduced, so that the encoding efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a data processing method, a data processing device, a computer device, and a computer-readable storage medium. Background Art

[0002] With the progress of scientific research, a vast amount of multimedia resources have emerged in the network. During the encoding process of multimedia resources, in order to reduce the bit consumption of filter coefficients, a filter bank multiplexing technology is usually adopted, that is, different types of pixel blocks in an image frame are allowed to share the same set of filter coefficients. It has been found that a large amount of calculation is involved in the process of multiplexing the filter bank (such as solving the Wiener-Hopf equation associated with each type of pixel block, and calculating the rate-distortion optimization of the filter coefficient group), and the encoding efficiency is low. Summary of the Invention

[0003] Embodiments of this application provide a data processing method, device, equipment, and computer-readable storage medium, which can improve the encoding efficiency.

[0004] On the one hand, embodiments of this application provide a data processing method, including:

[0005] Obtaining the Wiener-Hopf equations associated with each type of pixel block in the image frame to be encoded;

[0006] Grouping the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, where there is a target group among the N groups of pixel blocks, the target group includes at least two types of pixel blocks, and N is an integer greater than 1;

[0007] Merging the Wiener-Hopf equations associated with different types of pixel blocks in the target group, and using the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group;

[0008] Based on the Wiener-Hopf equations associated with the N groups of pixel blocks, determining a filter coefficient group corresponding to the image frame, where the filter coefficient group is used to generate the bitstream data of the image frame.

[0009] On the one hand, embodiments of this application provide a data processing device, which includes:

[0010] An obtaining unit, configured to obtain the Wiener-Hopf equations associated with each type of pixel block in the image frame to be encoded;

[0011] A processing unit, configured to group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, where there is a target group among the N groups of pixel blocks, the target group is associated with at least two types of Wiener-Hopf equations, and N is an integer greater than 1;

[0012] And for combining the Wiener - Hoff equations associated with different types of pixel blocks in the target group, and using the combined Wiener - Hoff equation as the Wiener - Hoff equation associated with the target group;

[0013] And for determining a set of filtering coefficients corresponding to the image frame based on the Wiener - Hoff equations associated with N groups of pixel blocks, where the set of filtering coefficients is used to generate the bitstream data of the image frame.

[0014] In one implementation, the feature of each pixel block includes a direction factor or a quantization activity factor; the processing unit is used to group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, specifically:

[0015] Grouping the pixel blocks with the same direction factor into one group to obtain N groups of pixel blocks; or,

[0016] Grouping the pixel blocks with the same quantization activity factor into one group to obtain N groups of pixel blocks.

[0017] In one implementation, the feature of each pixel block includes a direction factor and a quantization activity factor; the processing unit is used to group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, specifically:

[0018] Based on the direction factor and the quantization activity factor of each pixel block, calculating the feature value of the pixel block;

[0019] Grouping the pixel blocks whose feature values belong to the same numerical interval into one group to obtain N groups of pixel blocks.

[0020] In one implementation, the target group includes M types of pixel blocks, where M is an integer greater than 1; the processing unit is used to combine the Wiener - Hoff equations associated with different types of pixel blocks in the target group, and use the combined Wiener - Hoff equation as the Wiener - Hoff equation associated with the target group, specifically:

[0021] Performing a fusion process on the left - hand sides of the Wiener - Hoff equations associated with M types of pixel blocks to obtain a first fusion result;

[0022] Performing a fusion process on the right - hand sides of the Wiener - Hoff equations associated with M types of pixel blocks to obtain a second fusion result;

[0023] Constructing a target Wiener - Hoff equation based on the first fusion result and the second fusion result, where the left - hand side of the target Wiener - Hoff equation is the first fusion result, and the right - hand side of the target Wiener - Hoff equation is the second fusion result;

[0024] Using the target Wiener - Hoff equation as the Wiener - Hoff equation associated with the target group.

[0025] In one implementation, the processing unit is configured to determine a set of filtering coefficients corresponding to an image frame based on the Wiener-Hopf equations associated with N sets of pixel blocks, specifically:

[0026] Generate N combined results based on N sets of pixel blocks. The k-th combined result includes N - k + 1 sets of pixel blocks, where k is a positive integer less than or equal to N;

[0027] Calculate the rate-distortion optimization corresponding to the N combined results through the Wiener-Hopf equations associated with the N sets of pixel blocks;

[0028] Determine the set of filtering coefficients associated with the target combined result as the set of filtering coefficients corresponding to the image frame, where the target combined result is the combined result with the minimum rate-distortion optimization among the N combined results.

[0029] In one implementation, the process by which the processing unit generates N combined results based on N sets of pixel blocks includes:

[0030] Merge the i-th set of pixel blocks and the j-th set of pixel blocks to obtain the merged i-th set of pixel blocks. The combined rate-distortion optimization associated with the i-th set of pixel blocks and the j-th set of pixel blocks is the minimum among the combined rate-distortion optimizations associated with any two sets of pixel blocks in the N sets of pixel blocks;

[0031] Merge the Wiener-Hopf equation associated with the i-th set of pixel blocks and the Wiener-Hopf equation associated with the j-th set of pixel blocks to obtain the merged Wiener-Hopf equation associated with the i-th set of pixel blocks.

[0032] In one implementation, the processing unit is configured to calculate the rate-distortion optimization corresponding to the N combined results through the Wiener-Hopf equations associated with the N sets of pixel blocks, specifically:

[0033] Solve the Wiener-Hopf equations associated with N - k + 1 sets of pixel blocks to obtain the filtering coefficients associated with each set of pixel blocks;

[0034] Calculate the rate-distortion optimization of each set of pixel blocks based on the filtering coefficients associated with each set of pixel blocks;

[0035] Calculate the rate-distortion optimization corresponding to the k-th combined result through the rate-distortion optimizations of N - k + 1 sets of pixel blocks.

[0036] In one implementation, the processing unit is configured to calculate the rate-distortion optimization of each set of pixel blocks based on the filtering coefficients associated with each set of pixel blocks, specifically:

[0037] Based on the filtering coefficients associated with the h-th set of pixel blocks, calculate the distortion of each type of pixel block in the h-th set of pixel blocks, where h is a positive integer less than or equal to N - k + 1;

[0038] Calculate the rate-distortion optimization of the h-th group of pixel blocks based on the bitrate overhead of the filtering coefficients associated with the h-th group of pixel blocks and the distortion of each type of pixel block in the h-th group of pixel blocks.

[0039] In one implementation, the processing unit is configured to calculate the rate-distortion optimization corresponding to the k-th combined result by means of the rate-distortion optimizations of N - k + 1 groups of pixel blocks, specifically:

[0040] Accumulate the rate-distortion optimizations of N - k + 1 groups of pixel blocks to obtain the rate-distortion optimization corresponding to the k-th combined result.

[0041] In one implementation, the processing unit is configured to obtain the Wiener-Hopf equations associated with each type of pixel block in the image frame to be encoded, specifically:

[0042] Obtain the image frame to be encoded, where the image frame includes M pixel blocks, each pixel block is composed of at least one pixel point, and M is an integer greater than 1;

[0043] Classify the M pixel blocks according to the characteristics of each pixel block, where the characteristics of each pixel block include a direction factor and a quantization activity factor;

[0044] Based on the association relationship between the type of pixel block and the Wiener-Hopf equation, determine the Wiener-Hopf equations associated with each type of pixel block in the image frame.

[0045] In one implementation, the processing unit is further configured to:

[0046] If there is a group among the N groups of pixel blocks that contains only one type of pixel block, determine the Wiener-Hopf equation associated with the pixel blocks in this group as the Wiener-Hopf equation associated with this group.

[0047] In one implementation, the processing unit is further configured to:

[0048] Generate the bitstream data of the image frame based on the filtering coefficient group corresponding to the image frame;

[0049] Send the bitstream data to the decoding end so that the decoding end can present the image frame based on the bitstream data.

[0050] Correspondingly, the present application provides a computer device, which includes:

[0051] A memory, in which a computer program is stored;

[0052] A processor, configured to load the computer program to implement the above data processing method.

[0053] Accordingly, the present application provides a computer-readable storage medium storing a computer program, which is adapted to be loaded and executed by a processor to perform the above data processing method.

[0054] Accordingly, the present application provides a computer program product or a computer program, which includes 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 the processor executes the computer instructions, so that the computer device performs the above data processing method.

[0055] In an embodiment of the present application, Wiener-Hopf equations associated with pixel blocks of various types in an image frame to be encoded are obtained, the pixel blocks in the image frame are grouped according to a preset grouping rule to obtain N groups of pixel blocks, Wiener-Hopf equations associated with pixel blocks of different types in a target group are merged, and the merged Wiener-Hopf equation is used as the Wiener-Hopf equation associated with the target group. Based on the Wiener-Hopf equations associated with the N groups of pixel blocks, a filter coefficient group corresponding to the image frame is determined, and the filter coefficient group is used to generate bitstream data of the image frame. It can be seen that by grouping the pixel blocks in the image frame and merging the Wiener-Hopf equations associated with pixel blocks of different types in the same group, the number of Wiener-Hopf equations to be solved in the encoding process can be reduced (it is not necessary to solve the Wiener-Hopf equations associated with each type of pixel block), and the computational complexity of rate-distortion optimization of the filter coefficient group (the number of groups is less than the number of types of pixel blocks) can be reduced, thereby improving the encoding efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1a Schematic diagram of a general video encoder architecture provided by an embodiment of the present application;

[0057] Figure 1b Schematic diagram of a filter for a luminance component provided by an embodiment of the present application;

[0058] Figure 1c Schematic diagram of a filter for a chrominance component provided by an embodiment of the present application;

[0059] Figure 1d Schematic diagram of a data processing scenario provided by an embodiment of the present application;

[0060] Figure 2 Flowchart of a data processing method provided by an embodiment of the present application;

[0061] Figure 3 Flowchart of another data processing method provided by an embodiment of the present application;

[0062] Figure 4Structural schematic diagram of a data processing device provided by an embodiment of the present application;

[0063] Figure 5 Structural schematic diagram of a computer device provided by an embodiment of the present application. Specific implementation manners

[0064] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application.

[0065] The present application relates to technologies related to artificial intelligence and encoding / decoding. The related technologies involved are briefly introduced below:

[0066] Artificial Intelligence (AI): So-called AI is to use a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, a theory, method, technology, and application system that can perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines to enable the machines to have the functions of perception, reasoning, and decision-making. The embodiments of the present application mainly involve analyzing the Wiener-Hopf equation associated with N groups of pixel blocks through a prediction model to obtain a filter coefficient group corresponding to an image frame.

[0067] AI technology is an interdisciplinary subject, involving a wide range of fields, including both hardware-level technologies and software-level technologies. The basic technologies of artificial intelligence generally include, for example, sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, pre-trained model technologies, operating / interactive systems, mechatronics, etc. Among them, the pre-trained model, also known as the large model or the basic model, can be widely applied to downstream tasks in various major directions of artificial intelligence after fine-tuning. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0068] Machine Learning (ML) is an interdisciplinary field that involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize the existing knowledge structure to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its applications cover all fields of artificial intelligence. Machine learning and deep learning usually include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and rote learning. Pre-trained models are the latest development results of deep learning, integrating the above technologies. The embodiments of this application mainly involve training a prediction model based on a sample data set to further improve the accuracy of the prediction results of the prediction model.

[0069] Loop filtering is one of the core technologies in video coding. The Versatile Video Coding (VVC) encoder can support three loop filters, including the Deblocking Filter (DF), the Sample Adaptive Offset (SAO), and the Adaptive Loop Filter (ALF). Among them, ALF is an adaptive filter based on Wiener Filter, and its function is to optimize the output image signal to minimize the Mean Squared Error (MSE) between it and the original image, thereby achieving the effect of reducing distortion. Figure 1a The following is a schematic diagram of a general video encoder architecture provided by the embodiments of this application. As Figure 1a shown, in VVC, ALF is executed after the DF filter and the SAO filter. ALF derives the filter coefficients by solving the Wiener-Hopf equation of Wiener filtering, and it is determined by the encoder-side Rate-Distortion Optimization (RDO) whether to enable ALF. If ALF is enabled, the encoder needs to transfer the filter coefficient group to the decoder.

[0070] In the VVC standard, different-sized filters are defined for the luminance component and the chrominance component of pixel points respectively. Figure 1b The following is a schematic diagram of a filter for the luminance component provided by the embodiments of this application. As Figure 1b shown, a 7x7 diamond filter is used for the luminance component. Figure 1c The following is a schematic diagram of a filter for the chrominance component provided by the embodiments of this application. As Figure 1c shown, a 5x5 diamond filter is used for the chrominance component.Figure 1b and Figure 1c In [the above cases], the center position of the filter corresponds to the pixel position of the current filtering, and pixels symmetric to the center of the current pixel use the same filtering coefficients.

[0071] The filter parameter signals of the adaptive loop filter and the cross-component adaptive loop filter (CCALF) are included in the adaptive parameter set (APS). An APS can contain up to 25 sets of luminance ALF filtering coefficients and clipping values, and up to 8 sets of chrominance ALF filtering coefficients and clipping values. Each chrominance component of CCALF can have up to 4 sets of filtering coefficients in one APS. For the purpose of saving bitrate, signals between different classifications of the luminance filtering coefficients can be merged. The index of the APS used for the current slice can be sent in the slice header. To limit the computational complexity, the luminance and chrominance ALF filtering coefficients are quantized to integers in the range of [-2 7 , 2 7 -1] with the coefficient at the center position fixed at 128.

[0072] The clipping value index decoded from the APS can be used to confirm the clipping values of luminance and chrominance by looking up a table. These clipping values are related to the internal bit depth, and the corresponding relationship is shown in Table 1 (Table of the corresponding relationship between clipping values and internal bit depth).

[0073] Table 1

[0074]

[0075] For the luminance ALF filter, up to 7 APS index signals can be sent in the slice header to describe the luminance filtering parameter set used for the current slice. Whether to use ALF for each coding tree block (CTB) can be controlled by sending a switch signal at the CTB level. Each CTB can select filtering parameters from 16 sets of fixed ALF parameters and the APS of the current slice through the filter parameter set index. The 16 sets of fixed ALF parameters are predefined and stored in the encoder and decoder.

[0076] For the chrominance filter, an APS index can be sent in the slice header to specify the set of chrominance filtering parameters used for the current slice. If there are multiple filtering parameters in the APS, the filter used for the current chrominance CTB can also be determined by sending a filter parameter set index at the CTB level.

[0077] Based on the above technologies related to artificial intelligence and coding / decoding, embodiments of this application provide a data processing solution that can improve the coding efficiency. Figure 1d The following is a data processing scenario diagram provided by an embodiment of this application, as Figure 1d shown. The data processing scenario provided by this application includes a terminal device 101 and a server 102. The data processing solution provided by this application can be executed by the server 102. Among them, the terminal device may include, but is not limited to: smart phones (such as Android phones, IOS phones, etc.), tablet computers, portable personal computers, mobile Internet devices (abbreviated as MID), intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, aircraft, wearable devices, etc. Embodiments of this application do not make limitations in this regard; the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Embodiments of this application do not make limitations in this regard.

[0078] It should be noted that Figure 1d the numbers of the terminal device 101 and the server 102 are only for illustration and do not constitute an actual limitation of this application. The terminal device 101 and the server 102 can be connected by wired or wireless means, and this application does not make restrictions in this regard.

[0079] The general process of the data processing solution provided by this application is as follows:

[0080] (1) The server 102 (encoding end) obtains the Wiener - Hoff equations associated with pixel blocks of various types in the image frame to be encoded. A pixel block consists of at least one pixel point. In one embodiment, a pixel block can consist of 4 * 4 pixel points. The Wiener - Hoff equations associated with different types of pixel blocks are different. In one implementation, the type of a pixel block can be determined based on the characteristics of the pixel block. The characteristics of each pixel block include the directionality factor and the activity factor of the pixel block. The server 102 classifies the M pixel blocks in the image frame based on the characteristics of the pixel blocks, and then determines the Wiener - Hoff equations associated with pixel blocks of various types in the image frame according to the association relationship between the type of the pixel block and the Wiener - Hoff equation.

[0081] (2) The server 102 groups the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks. There is a target group among the N groups of pixel blocks. The target group includes at least two types of pixel blocks, and N is an integer greater than 1. The preset grouping rule can include: grouping the pixel blocks based on the directionality factor of the pixel blocks, grouping the pixel blocks based on the activity factor of the pixel blocks, and grouping the pixel blocks based on the directionality factor and the activity factor of the pixel blocks. In one implementation, the server 102 divides the pixel blocks with the same directionality factor into one group to obtain N groups of pixel blocks; or, the server 102 divides the pixel blocks with the same activity factor into one group to obtain N groups of pixel blocks; or, the server 102 calculates the characteristic value of each pixel block based on the directionality factor and the activity factor of the pixel block, and divides the pixel blocks with characteristic values belonging to the same numerical interval into one group to obtain N groups of pixel blocks.

[0082] (3) The server 102 merges the Wiener - Hoff equations associated with different types of pixel blocks in the target group, and uses the merged Wiener - Hoff equation as the Wiener - Hoff equation associated with the target group. In one implementation, the target group includes M types of pixel blocks, and M is an integer greater than 1. On the one hand, the server 102 performs a fusion process on the left - hand side equations of the Wiener - Hoff equations associated with the M types of pixel blocks to obtain a first fusion result; on the other hand, the server 102 performs a fusion process on the right - hand side equations of the Wiener - Hoff equations associated with the M types of pixel blocks to obtain a second fusion result. After obtaining the first fusion result and the second fusion result, the server 102 constructs a target Wiener - Hoff equation based on the first fusion result and the second fusion result, and uses the target Wiener - Hoff equation as the Wiener - Hoff equation associated with the target group; where the left - hand side equation of the target Wiener - Hoff equation is the first fusion result, and the right - hand side equation of the target Wiener - Hoff equation is the second fusion result.

[0083] (4) The server 102 determines a set of filtering coefficients corresponding to the image frame based on the Wiener - Hoff equations associated with N groups of pixel blocks. The set of filtering coefficients is used to generate the bitstream data of the image frame. In one implementation, the server 102 generates N combined results based on N groups of pixel blocks. The first combined result includes N groups of pixel blocks (i.e., the N groups of pixel blocks are not combined). The k - th combined result is obtained by combining k groups of pixel blocks among the N groups of pixel blocks. The k - th combined result includes N - k + 1 groups of pixel blocks, where k is an integer greater than 1 and less than or equal to N. Combining means merging at least two groups of pixel blocks into one group of pixel blocks. The Wiener - Hoff equation associated with the combined group (i.e., the group of pixel blocks obtained by merging at least two groups of pixel blocks) is obtained by combining the Wiener - Hoff equations associated with the groups of pixel blocks being merged. After generating the N combined results, the server 102 calculates the rate - distortion optimization corresponding to the N combined results through the Wiener - Hoff equations associated with the N groups of pixel blocks, and determines the set of filtering coefficients associated with the target combined result as the set of filtering coefficients corresponding to the image frame. The target combined result is the combined result with the minimum rate - distortion optimization among the N combined results.

[0084] Further, the server 102 generates the bitstream data of the image frame based on the set of filtering coefficients corresponding to the image frame, and sends the bitstream data to the terminal device 101 (decoding end). After receiving the bitstream data of the image frame, the terminal device 101 decodes the bitstream data and presents the image frame based on the decoding result of the bitstream data.

[0085] In the embodiments of the present application, the Wiener - Hoff equations associated with each type of pixel block in the image frame to be encoded are obtained, the pixel blocks in the image frame are grouped according to a preset grouping rule to obtain N groups of pixel blocks, the Wiener - Hoff equations associated with different types of pixel blocks in the target group are combined, and the combined Wiener - Hoff equation is used as the Wiener - Hoff equation associated with the target group. Based on the Wiener - Hoff equations associated with the N groups of pixel blocks, the set of filtering coefficients corresponding to the image frame is determined. The set of filtering coefficients is used to generate the bitstream data of the image frame. It can be seen that by grouping the pixel blocks in the image frame and combining the Wiener - Hoff equations associated with different types of pixel blocks in the same group, the number of Wiener - Hoff equations to be solved in the encoding process can be reduced (there is no need to solve the Wiener - Hoff equations associated with each type of pixel block), and the computational amount of the rate - distortion optimization of the set of filtering coefficients (the number of groups is less than the number of types of pixel blocks) can be reduced, thereby improving the encoding efficiency.

[0086] Based on the above data - processing scheme, the embodiments of the present application propose a more detailed data - processing method. The data - processing method proposed in the embodiments of the present application will be introduced in detail below with reference to the accompanying drawings.

[0087] Please refer to Figure 2 , Figure 2The flowchart of a data processing method provided by an embodiment of this application. This data processing method can be executed by a computer device, which can specifically be Figure 1d the server 102 shown in Figure 2 As shown in, this data processing method may include but is not limited to S201 - S204:

[0088] S201. Obtain the Wiener - Hoff equations associated with pixel blocks of various types in the image frame to be encoded.

[0089] The image frame can specifically be an independent image or an image in a video, and this application does not limit this. The image frame includes M pixel blocks, and each pixel block consists of one or more pixel points. For example, a pixel block can be composed of 4 * 4 pixel points.

[0090] In one implementation, the computer device can classify pixel blocks according to the characteristics of the pixel blocks. In one embodiment, the characteristics of each pixel block include the directionality and activity quantization factor of the pixel block, and the characteristics of each pixel block can be derived from the pixel gradient values of the horizontal, vertical, diagonal, and skew - diagonal directions of the pixel block. The pixel block classification index C of the luminance component can be determined by the directionality and activity quantization factor of the pixel block, and the specific formula can be expressed as:

[0091]

[0092] where C is the pixel block classification index of the luminance component, D is the directionality of the pixel block, is the activity quantization factor of the pixel block.

[0093] Both the directionality and activity quantization factor of the pixel block can be divided into 5 levels. The 5 levels of the directionality of the pixel block can be expressed as D0 - D4, and the 5 levels of the activity quantization factor of the pixel block can be expressed as Based on the directionality and activity quantization factor of the pixel block, the computer device can divide the pixel blocks into 25 categories. The specific division method of the 25 types of pixel blocks can refer to Table 2 (Pixel Block Classification Relationship Table):

[0094] Table 2

[0095]

[0096] As shown in Table 2, the computer device can determine the type of each pixel block based on the direction factor level and the quantization activity factor level of the pixel block. After determining the types of the M pixel blocks in the image frame, the computer device can determine the Wiener - Hoff equations associated with the pixel blocks of each type in the image frame based on the association relationship between the type of the pixel block and the Wiener - Hoff equation. Different types of pixel blocks are associated with different Wiener - Hoff equations.

[0097] S202. Group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks.

[0098] There is a target group among the N groups of pixel blocks. The target group includes at least two types of pixel blocks, and N is an integer greater than 1. The preset grouping rule may include: grouping the pixel blocks based on the direction factor (Directionality) of the pixel block, grouping the pixel blocks based on the quantization activity factor (Activity) of the pixel block, and grouping the pixel blocks based on the direction factor and the quantization activity factor of the pixel block.

[0099] In one implementation, the computer device groups the pixel blocks with the same direction factor (such as the same direction factor level) into one group to obtain N groups of pixel blocks. For example, the direction factor of the pixel block can be divided into 5 levels, respectively represented as D0 - D4. The computer device divides the M pixel blocks into 5 (N = 5) groups based on the direction factor level. The pixel blocks belonging to the same group have the same direction factor level. Combining with Table 2, it can be seen that a group of pixel blocks can include at most 5 types of pixel blocks. For example, the pixel block group with the direction factor level of D0 can include pixel blocks of type 1 - type 5.

[0100] In another implementation, the computer device groups the pixel blocks with the same quantization activity factor (such as the same quantization activity factor level) into one group to obtain N groups of pixel blocks. For example, the quantization activity factor of the pixel block can be divided into 5 levels, respectively represented as The computer device divides the M pixel blocks into 5 (N = 5) groups based on the quantization activity factor level. The pixel blocks belonging to the same group have the same quantization activity factor level. Combining with Table 2, it can be seen that a group of pixel blocks can include at most 5 types of pixel blocks. For example, the pixel block group with the quantization activity factor level of level can include pixel blocks of type 1, type 6, type 11, type 16, and type 21.

[0101] In yet another embodiment, the computer device calculates the eigenvalue of each pixel block based on the direction factor and the quantization activity factor of the pixel block, and divides the pixel blocks with eigenvalues belonging to the same numerical range into a group, obtaining N groups of pixel blocks. For example, the computer device can perform a summation process on the direction factor and the quantization activity factor of each pixel block to obtain the eigenvalue of the pixel block. When the eigenvalue of the pixel block belongs to the Nth range, the pixel block is added to the Nth group of pixel blocks. Each range does not overlap, and the number of ranges can be adjusted based on the actual situation, which is not limited in this application.

[0102] S203. Merge the Wiener-Hopf equations associated with different types of pixel blocks in the target group, and use the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group.

[0103] In one embodiment, the target group includes M types of pixel blocks, where M is an integer greater than 1. On the one hand, the computer device performs a fusion process on the left equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a first fusion result; on the other hand, the computer device performs a fusion process on the right equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a second fusion result. After obtaining the first fusion result and the second fusion result, the computer device constructs a target Wiener-Hopf equation based on the first fusion result and the second fusion result, and uses the target Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group; where the left equality of the target Wiener-Hopf equation is the first fusion result, and the right equality of the target Wiener-Hopf equation is the second fusion result. For example, assume that the gth group of pixel blocks includes pixel blocks of type 1 and type 2. Then the computer device merges the Wiener-Hopf equation associated with the pixel blocks of type 1 and the Wiener-Hopf equation associated with the pixel blocks of type 2, and uses the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the gth group of pixel blocks. The left equality of the merged Wiener-Hopf equation is obtained by fusing the left equality of the Wiener-Hopf equation associated with the pixel blocks of type 1 and the left equality of the Wiener-Hopf equation associated with the pixel blocks of type 2. The right equality of the merged Wiener-Hopf equation is obtained by fusing the right equality of the Wiener-Hopf equation associated with the pixel blocks of type 1 and the right equality of the Wiener-Hopf equation associated with the pixel blocks of type 2, where g is a positive integer less than or equal to N.

[0104] In another embodiment, if there is a group among the N groups of pixel blocks that includes only one type of pixel block (i.e., all pixel blocks in this group belong to the same type), the computer device determines the Wiener - Hoff equation associated with the pixel blocks in this group as the Wiener - Hoff equation associated with this group. For example, assume that the g - th group of pixel blocks includes only pixel blocks of type 1, then the computer device determines the Wiener - Hoff equation associated with the pixel blocks of type 1 as the Wiener - Hoff equation associated with the g - th group of pixel blocks, where g is a positive integer less than or equal to N.

[0105] S204. Determine a filter coefficient group corresponding to the image frame based on the Wiener - Hoff equations associated with the N groups of pixel blocks.

[0106] The filter coefficient group is used to generate the bitstream data of the image frame. In one embodiment, the computer device generates N combined results based on the N groups of pixel blocks. The first combined result includes the N groups of pixel blocks (i.e., the N groups of pixel blocks are not combined). The computer device solves the Wiener - Hoff equations associated with the N groups of pixel blocks to obtain the filter coefficients associated with each group of pixel blocks. The computer device generates reconstruction results for the pixel blocks of each type in the h - th group of pixel blocks based on the filter coefficients associated with the h - th group of pixel blocks, and calculates the distortion of the pixel blocks of each type through the difference between the reconstruction result of each type of pixel block and the pixel block in the original image. The h - th group of pixel blocks is any one of the N groups of pixel blocks. Then the computer device calculates the rate - distortion optimization of the h - th group of pixel blocks through the bit - rate overhead of the filter coefficients associated with the h - th group of pixel blocks (determined based on the filter coefficients associated with the h - th group of pixel blocks) and the distortion of the pixel blocks of each type in the h - th group of pixel blocks. The rate - distortion optimization of the h - th group of pixel blocks can be expressed as:

[0107] cost h = ΔD + λR

[0108] where cost h represents the rate - distortion optimization of the h - th group of pixel blocks; ΔD represents the distortion of the pixel blocks of each type in the h - th group of pixel blocks, that is, the deviation between the encoded pixel blocks and the pre - encoded pixel blocks in the h - th group of pixel blocks; λR represents the bit - rate overhead of the filter coefficients associated with the h - th group of pixel blocks. In the above - mentioned manner, the computer device can calculate the rate - distortion optimizations of the N groups of pixel blocks, and perform a summation process on the rate - distortion optimizations of the N groups of pixel blocks to obtain the rate - distortion optimization of the first combined result.

[0109] The k-th combined result is obtained by combining k groups of pixel blocks out of N groups of pixel blocks. The k-th combined result includes N - k + 1 groups of pixel blocks, where k is an integer greater than 1 and less than or equal to N. Combining means merging at least two groups of pixel blocks into one group of pixel blocks. The Wiener-Hopf equation associated with the combined group (i.e., the group of pixel blocks obtained by merging at least two groups of pixel blocks) is obtained by combining the Wiener-Hopf equations associated with the groups of pixel blocks being combined.

[0110] In one embodiment, the process by which a computer device generates N combined results based on N groups of pixel blocks includes: on the one hand, the computer device combines the i-th group of pixel blocks and the j-th group of pixel blocks to obtain the combined i-th group of pixel blocks (i.e., the combined group of pixel blocks); on the other hand, the computer device combines the Wiener-Hopf equation associated with the i-th group of pixel blocks and the Wiener-Hopf equation associated with the j-th group of pixel blocks to obtain the Wiener-Hopf equation associated with the combined i-th group of pixel blocks (i.e., the combined group of pixel blocks); where the combined rate-distortion optimization associated with the i-th group of pixel blocks and the j-th group of pixel blocks is the smallest among the combined rate-distortion optimizations associated with any two groups of pixel blocks in the N groups of pixel blocks.

[0111] The combined rate distortion optimization is obtained by summing the rate distortion optimization of the i-th group of pixel blocks and the rate distortion optimization of the j-th group of pixel blocks. The rate distortion optimization of the i-th group of pixel blocks is calculated based on the bitrate overhead of the filtering coefficients associated with the i-th group of pixel blocks and the distortion of each type of pixel block in the i-th group of pixel blocks. Among them, the filtering coefficients associated with the i-th group of pixel blocks are obtained by solving the Wiener-Hopf equation associated with the combined group (i.e., obtained by combining the Wiener-Hopf equation associated with the i-th group of pixel blocks and the Wiener-Hopf equation associated with the j-th group of pixel blocks). The distortion of each type of pixel block in the i-th group of pixel blocks is calculated based on the difference between the reconstruction result of each type of pixel block in the i-th group of pixel blocks and the pixel block in the original image (i.e., the image frame). The reconstruction result of each type of pixel block in the i-th group of pixel blocks is generated by the filtering coefficients associated with the i-th group of pixel blocks. Similarly, the rate distortion optimization of the j-th group of pixel blocks is calculated based on the bitrate overhead of the filtering coefficients associated with the j-th group of pixel blocks and the distortion of each type of pixel block in the j-th group of pixel blocks. Among them, the filtering coefficients associated with the j-th group of pixel blocks are obtained by solving the Wiener-Hopf equation associated with the combined group (i.e., obtained by combining the Wiener-Hopf equation associated with the i-th group of pixel blocks and the Wiener-Hopf equation associated with the j-th group of pixel blocks). The distortion of each type of pixel block in the j-th group of pixel blocks is calculated based on the difference between the reconstruction result of each type of pixel block in the j-th group of pixel blocks and the pixel block in the original image (i.e., the image frame). The reconstruction result of each type of pixel block in the j-th group of pixel blocks is generated by the filtering coefficients associated with the i-th group of pixel blocks. According to the above implementation manner, the computer device can combine N groups of pixel blocks in pairs, solve the corresponding combined rate distortion optimization, and combine the two groups of pixel blocks with the smallest combined rate distortion optimization.

[0112] For example, assume N = 3. The first combined result includes the first group of pixel blocks, the second group of pixel blocks, and the third group of pixel blocks. The combined rate distortion optimization of the first group of pixel blocks and the second group of pixel blocks is x1, the combined rate distortion optimization of the second group of pixel blocks and the third group of pixel blocks is x2, and the combined rate distortion optimization of the first group of pixel blocks and the third group of pixel blocks is x3, and x1 < x3 < x2. Then the computer device combines the first group of pixel blocks and the second group of pixel blocks to obtain the second combined result. The second combined result includes the merged first group of pixel blocks (obtained by merging the original first group of pixel blocks and the original second group of pixel blocks) and the third group of pixel blocks. At this time, N = 2. In the second combined result, the rate distortion optimization of the merged first group of pixel blocks is the combined rate distortion optimization of the original first group of pixel blocks and the original second group of pixel blocks.

[0113] According to the above method, the computer device merges the two pixel blocks with the smallest rate-distortion optimization in the first combined result to obtain the second combined result (the second combined result includes N - 1 pixel blocks). Then, the computer device merges the two pixel blocks with the smallest rate-distortion optimization in the second combined result to obtain the third combined result (the third combined result includes N - 2 pixel blocks). Repeat the above steps until the Nth combined result is obtained (the Nth combined result includes 1 pixel block).

[0114] After generating the N combined results, the computer device calculates the rate-distortion optimization corresponding to the N combined results through the Wiener-Hopf equations associated with the N pixel blocks. In one embodiment, the computer device accumulates the rate-distortion optimizations of each pixel block in each combined result to obtain the rate-distortion optimization corresponding to the combined result. Taking the kth combined result as an example, the kth combined result includes N - k + 1 pixel blocks, and the computer device accumulates the rate-distortion optimizations of the N - k + 1 pixel blocks to obtain the rate-distortion optimization corresponding to the kth combined result.

[0115] After obtaining the rate-distortion optimization corresponding to the N combined results, the computer device determines the filter coefficient group associated with the target combined result as the filter coefficient group corresponding to the image frame, and the target combined result is the combined result with the smallest rate-distortion optimization among the N combined results.

[0116] In another implementation, the computer device calls a prediction model to analyze the Wiener-Hopf equations associated with the N pixel blocks to obtain the filter coefficient group corresponding to the image frame; wherein, the prediction model is obtained by training a model to be trained based on a sample data set. Specifically, the sample data set includes sample data and verification data corresponding to the sample data. The computer device calls the model to be trained to process the sample data to obtain the prediction result of the sample data. Then, based on the difference between the prediction result of the sample data and the verification data corresponding to the sample data, the model to be trained is optimized to obtain the prediction model. In one embodiment, the prediction result output by the prediction model is the filter coefficient group corresponding to the image frame. In another embodiment, the prediction result output by the prediction model is a combined result obtained based on the N pixel blocks, and the combined result includes h pixel blocks, and each pixel block is associated with a Wiener-Hopf equation, where h is a positive integer less than or equal to N. The computer device solves the Wiener-Hopf equations associated with the h pixel blocks to obtain the filter coefficient group corresponding to the image frame.

[0117] Further, the computer device can generate the bitstream data of the image frame based on the filter coefficient group corresponding to the image frame and send the bitstream data to the decoding end, so that after receiving the bitstream data of the image frame, the decoding end decodes the bitstream data and presents the image frame based on the decoding result of the bitstream data.

[0118] In an embodiment of the present application, Wiener-Hopf equations associated with pixel blocks of various types in an image frame to be encoded are obtained. The pixel blocks in the image frame are grouped according to a preset grouping rule to obtain N groups of pixel blocks. The Wiener-Hopf equations associated with pixel blocks of different types in the target group are combined, and the combined Wiener-Hopf equation is used as the Wiener-Hopf equation associated with the target group. Based on the Wiener-Hopf equations associated with the N groups of pixel blocks, a set of filtering coefficients corresponding to the image frame is determined. The set of filtering coefficients is used to generate the bitstream data of the image frame. It can be seen that by grouping the pixel blocks in the image frame and combining the Wiener-Hopf equations associated with pixel blocks of different types in the same group, the number of Wiener-Hopf equations to be solved in the encoding process can be reduced (it is not necessary to solve the Wiener-Hopf equations associated with each type of pixel block), and the computational complexity of rate-distortion optimization for the set of filtering coefficients (the number of groups is less than the number of types of pixel blocks) can be reduced, thereby improving the encoding efficiency.

[0119] Please refer to Figure 3 , Figure 3 which is a flowchart of another data processing method provided by an embodiment of the present application. This data processing method can be executed by a computer device; specifically, the computer device can be the Figure 1d server 102 shown in Figure 3 . As

[0120] shown, this data processing method may include but is not limited to S301 - S307:

[0121] S301. Obtain the Wiener-Hopf equations associated with pixel blocks of various types in the image frame to be encoded.

[0122] In one implementation, the computer device obtains the image frame to be encoded. The image frame includes M pixel blocks, and each pixel block is composed of at least one pixel point, where M is an integer greater than 1. The computer device classifies the M pixel blocks according to the characteristics of each pixel block. The characteristics of each pixel block include a direction factor and a quantization activity factor. After obtaining the types of the M pixel blocks, based on the association relationship between the types of pixel blocks and the Wiener-Hopf equations, the Wiener-Hopf equations associated with pixel blocks of various types in the image frame are determined, and the Wiener-Hopf equations associated with different types of pixel blocks are different.

[0123] S302. Group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks.

[0124] For the specific implementation manners of S302 and S303, reference can be made to the implementation manners of S202 and S203 in Figure 2 , which will not be elaborated here.

[0125] S304. Generate N combined results based on N groups of pixel blocks.

[0126] The k-th combined result includes N - k + 1 groups of pixel blocks, where k is a positive integer less than or equal to N. When k = 1, the first combined result includes the N groups of pixel blocks. When k is greater than 1 and less than or equal to N, the k-th combined result is obtained by combining k groups of pixel blocks among the N groups of pixel blocks. Specifically, when k = 2, the second combined result is obtained by combining two groups of pixel blocks among the N groups of pixel blocks in (the first combined result) (that is, by combining two groups of pixel blocks among the N groups of pixel blocks in the first combined result to obtain N - 1 groups of pixel blocks). The combined rate distortion optimization of the two groups of pixel blocks being combined is the smallest among the pairwise combinations of the N groups of pixel blocks. The pixel block group obtained by combining (that is, the combined pixel block group) includes the pixel blocks in the two groups of pixel blocks being combined. The Wiener - Hoff equation associated with the pixel block group obtained by combining (that is, the combined pixel block group) is obtained by combining the Wiener - Hoff equations associated with the two groups of pixel blocks being combined. Similarly, when k = 3, the third combined result is obtained by combining two groups of pixel blocks among the N - 1 groups of pixel blocks in (the second combined result) (that is, by combining two groups of pixel blocks among the N - 1 groups of pixel blocks in the second combined result to obtain N - 2 groups of pixel blocks). The combined rate distortion optimization of the two groups of pixel blocks being combined is the smallest among the pairwise combinations of the N - 1 groups of pixel blocks. When k = N, the N-th combined result is obtained by combining two groups of pixel blocks in (the N - 1 - th combined result).

[0127] In one implementation, the process by which the computer device generates N combined results based on the N groups of pixel blocks includes: On the one hand, the computer device combines the i-th group of pixel blocks and the j-th group of pixel blocks to obtain the combined i-th group of pixel blocks (that is, the combined pixel block group). The combined rate distortion optimization associated with the i-th group of pixel blocks and the j-th group of pixel blocks is the smallest among the combined rate distortion optimizations associated with any two groups of pixel blocks in the N groups of pixel blocks. On the other hand, the computer device combines the Wiener - Hoff equation associated with the i-th group of pixel blocks and the Wiener - Hoff equation associated with the j-th group of pixel blocks to obtain the Wiener - Hoff equation associated with the combined i-th group of pixel blocks (that is, the combined pixel block group).

[0128] S305. Calculate the rate distortion optimization corresponding to the N combined results through the Wiener - Hoff equations associated with the N groups of pixel blocks.

[0129] In one implementation, the computer device solves the Wiener-Hopf equation associated with N groups of pixel blocks to obtain the filtering coefficients associated with each group of pixel blocks, and calculates the rate-distortion optimization of each group of pixel blocks based on the filtering coefficients associated with each group of pixel blocks. In one embodiment, the computer device calculates the distortion of each type of pixel block in the h-th group of pixel blocks based on the filtering coefficients associated with the h-th group of pixel blocks, where h is a positive integer less than or equal to N - k + 1. Specifically, the computer device filters the pixel blocks to be filtered (such as the compression result of the pixel blocks) based on the filtering coefficients associated with the h-th group of pixel blocks to obtain the reconstruction results of each type of pixel block in the h-th group of pixel blocks, and calculates the distortion of each type of pixel block through the difference between the reconstruction result of each type of pixel block and the pixel block in the original image.

[0130] Optionally, before filtering the pixel blocks to be filtered through the filtering coefficients, the computer device can also perform a geometric transformation on the filtering coefficients and the clipping value according to the gradient value of the pixel blocks to be filtered (including at least one of the horizontal, vertical, diagonal, and skew-diagonal gradient values). The geometric transformation includes 4 types of transformations: identity, diagonal transformation, vertical flip, and rotation. Applying the geometric transformation to the filtering parameters is equivalent to performing the corresponding geometric transformation on the pixel blocks to be filtered and then filtering them when the parameters remain unchanged. By performing a geometric transformation on the filtering coefficients, the directionality of the filtering operation can be made closer, enabling more pixel points to share the same filtering parameters, reducing distortion without encoding more filtering parameters, and thus improving the overall coding efficiency.

[0131] According to the above implementation, the computer device can calculate the distortion of each type of pixel block in each group of pixel blocks. After obtaining the distortion of each type of pixel block in each group of pixel blocks, the computer device calculates the rate-distortion optimization of each group of pixel blocks through the bitrate overhead of the filtering coefficients associated with each group of pixel blocks and the distortion of each type of pixel block in each group of pixel blocks. Specifically, the computer device calculates the rate-distortion optimization of the h-th group of pixel blocks through the bitrate overhead of the filtering coefficients associated with the h-th group of pixel blocks and the distortion of each type of pixel block in the h-th group of pixel blocks. In one implementation, there are Q types of pixel blocks in the h-th group of pixel blocks, where Q is a positive integer. The computer device calculates the rate-distortion optimization of the r-th type of pixel block based on the distortion of the r-th type of pixel block and the bitrate overhead of the filtering coefficients associated with the h-th group of pixel blocks, where r is a positive integer less than or equal to Q. After obtaining the rate-distortion optimization of Q types of pixel blocks, the computer device sums up the rate-distortion optimization of Q types of pixel blocks to obtain the rate-distortion optimization of the h-th group of pixel blocks.

[0132] According to the above implementation manner, after obtaining the rate-distortion optimization of N-k+1 groups of pixel blocks, the computer device calculates the rate-distortion optimization corresponding to the k-th combination result through the rate-distortion optimization of the N-k+1 groups of pixel blocks. In one embodiment, the computer device accumulates the rate-distortion optimizations of the N-k+1 groups of pixel blocks to obtain the rate-distortion optimization corresponding to the k-th combination result.

[0133] S306. Determine the filter coefficient group associated with the target merge result as the filter coefficient group corresponding to the image frame.

[0134] The target merge result is the merge result with the minimum rate-distortion optimization among the N merge results. The filter coefficient group associated with the target merge result is obtained by solving the Wiener-Hopf equations associated with each group of pixel blocks in the target merge result. In one embodiment, the k-th combination result includes N-k+1 groups of pixel blocks, and the computer device respectively solves the Wiener-Hopf equations associated with the N-k+1 groups of pixel blocks to obtain N-k+1 groups of filter coefficients, and determines these N-k+1 groups of filter coefficients as the filter coefficient group corresponding to the image frame.

[0135] S307. Generate the bitstream data of the image frame based on the filter coefficient group corresponding to the image frame.

[0136] In one implementation manner, the computer device generates a strip header based on the filter coefficient group corresponding to the image frame, and generates the bitstream data of the image frame through the strip header. After obtaining the bitstream data of the image frame, the computer device sends the bitstream data of the image frame to a decoding end (such as a terminal device). Correspondingly, after the decoding end obtains the bitstream data of the image frame, it decodes the bitstream data and presents the image frame based on the decoding result of the bitstream data. The decoding process includes performing a filtering process on the reconstructed pixel points based on the filter coefficient group to obtain the filtered pixel points, which can be specifically expressed as:

[0137]

[0138] Among them, R(I,j) represents the reconstructed pixel point, R′(I,j) represents the filtered pixel point, f(k,l) represents the filter parameter corresponding to the pixel point to be filtered (obtained by solving the Wiener-Hopf equation associated with the target group of pixel blocks, the target group of pixel blocks includes pixel points of the target type, and the target type is the type of the pixel point to be filtered), K(x,y) is the clipping value formula, K(x,y) = min(y, max(-y,x)) = Clip3(-y,y,x). c(k,l) is the solved clipping value parameter, and the variables k and l take values in the range of [-L / 2,L / 2], where L is the filter length. It should be noted that the clipping value operation introduces a non-linear characteristic, which can effectively reduce the interference of the surrounding pixel values on the filtered output of the current pixel when the surrounding pixel values are quite different from the current pixel.

[0139] In the embodiments of the present application, Wiener-Hopf equations associated with pixel blocks of various types in an image frame to be encoded are obtained, the pixel blocks in the image frame are grouped according to a preset grouping rule to obtain N groups of pixel blocks, the Wiener-Hopf equations associated with pixel blocks of different types in a target group are merged, and the merged Wiener-Hopf equation is used as the Wiener-Hopf equation associated with the target group. Based on the Wiener-Hopf equations associated with the N groups of pixel blocks, a set of filtering coefficients corresponding to the image frame is determined, and the set of filtering coefficients is used to generate the bitstream data of the image frame. It can be seen that by grouping the pixel blocks in the image frame and merging the Wiener-Hopf equations associated with pixel blocks of different types in the same group, the number of Wiener-Hopf equations to be solved in the encoding process can be reduced (it is not necessary to solve the Wiener-Hopf equations associated with pixel blocks of each type), and the computational complexity of rate-distortion optimization of the set of filtering coefficients (the number of groups is less than the number of types of pixel blocks) can be reduced, thereby improving the encoding efficiency. In addition, by performing a geometric transformation on the filtering coefficients, the directionality of the filtering operation can be made closer, enabling more pixel points to share the same filtering parameters, reducing distortion without encoding more filtering parameters, and thus improving the overall encoding efficiency.

[0140] The method of the embodiments of the present application is described in detail above. To facilitate better implementation of the above solutions of the embodiments of the present application, correspondingly, the device of the embodiments of the present application is provided below.

[0141] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a data processing device provided by an embodiment of the present application. This device can be mounted on a computer device, and the computer device can specifically be Figure 1d the server 102 shown in Figure 4 The data processing device shown in Figure 2 and Figure 3 can be used to execute some or all of the functions in the method embodiments described above. Please refer to Figure 4 ,and the detailed descriptions of each unit are as follows:

[0142] An obtaining unit 401, configured to obtain Wiener-Hopf equations associated with pixel blocks of various types in an image frame to be encoded;

[0143] A processing unit 402, configured to group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks. There is a target group among the N groups of pixel blocks, and the target group includes at least two types of pixel blocks. N is an integer greater than 1;

[0144] and configured to merge the Wiener-Hopf equations associated with pixel blocks of different types in the target group, and use the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group;

[0145] and determining, based on the Wiener-Hopf equations associated with N groups of pixel blocks, a group of filtering coefficients corresponding to the image frame, where the group of filtering coefficients is used to generate the bitstream data of the image frame.

[0146] In one implementation, the feature of each pixel block includes a direction factor or a quantization activity factor; the processing unit 402 is configured to group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, specifically:

[0147] Grouping the pixel blocks with the same direction factor into one group to obtain N groups of pixel blocks; or,

[0148] Grouping the pixel blocks with the same quantization activity factor into one group to obtain N groups of pixel blocks.

[0149] In one implementation, the feature of each pixel block includes a direction factor and a quantization activity factor; the processing unit 402 is configured to group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, specifically:

[0150] Calculating the feature value of each pixel block based on the direction factor and the quantization activity factor of the pixel block;

[0151] Grouping the pixel blocks whose feature values belong to the same numerical range into one group to obtain N groups of pixel blocks.

[0152] In one implementation, the target grouping includes M types of pixel blocks, where M is an integer greater than 1; the processing unit 402 is configured to merge the Wiener-Hopf equations associated with different types of pixel blocks in the target grouping, and use the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target grouping, specifically:

[0153] Performing a fusion process on the left equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a first fusion result;

[0154] Performing a fusion process on the right equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a second fusion result;

[0155] Constructing a target Wiener-Hopf equation based on the first fusion result and the second fusion result, where the left equality of the target Wiener-Hopf equation is the first fusion result and the right equality of the target Wiener-Hopf equation is the second fusion result;

[0156] Using the target Wiener-Hopf equation as the Wiener-Hopf equation associated with the target grouping.

[0157] In one implementation, the processing unit 402 is configured to determine, based on the Wiener-Hopf equations associated with N groups of pixel blocks, a group of filtering coefficients corresponding to the image frame, specifically:

[0158] Based on N groups of pixel blocks, N combined results are generated. The k-th combined result includes N - k + 1 groups of pixel blocks, where k is a positive integer less than or equal to N;

[0159] Calculate the rate-distortion optimization corresponding to the N combined results through the Wiener - Hoff equations associated with the N groups of pixel blocks;

[0160] Determine the filter coefficient group associated with the target combined result as the filter coefficient group corresponding to the image frame. The target combined result is the combined result with the minimum rate-distortion optimization among the N combined results.

[0161] In one implementation, the process by which the processing unit 402 generates N combined results based on N groups of pixel blocks includes:

[0162] Merge the i-th group of pixel blocks and the j-th group of pixel blocks to obtain the merged i-th group of pixel blocks. The combined rate-distortion optimization associated with the i-th group of pixel blocks and the j-th group of pixel blocks is the minimum among the combined rate-distortion optimizations associated with any two groups of pixel blocks in the N groups of pixel blocks;

[0163] Merge the Wiener - Hoff equation associated with the i-th group of pixel blocks and the Wiener - Hoff equation associated with the j-th group of pixel blocks to obtain the merged Wiener - Hoff equation associated with the i-th group of pixel blocks.

[0164] In one implementation, the processing unit 402 is used to calculate the rate-distortion optimization corresponding to the N combined results through the Wiener - Hoff equations associated with the N groups of pixel blocks. Specifically, it is used for:

[0165] Solve the Wiener - Hoff equations associated with N - k + 1 groups of pixel blocks to obtain the filter coefficients associated with each group of pixel blocks;

[0166] Based on the filter coefficients associated with each group of pixel blocks, calculate the rate-distortion optimization of this group of pixel blocks;

[0167] Calculate the rate-distortion optimization corresponding to the k-th combined result through the rate-distortion optimizations of N - k + 1 groups of pixel blocks.

[0168] In one implementation, the processing unit 402 is used to calculate the rate-distortion optimization of each group of pixel blocks based on the filter coefficients associated with each group of pixel blocks. Specifically, it is used for:

[0169] Based on the filter coefficients associated with the h-th group of pixel blocks, calculate the distortion of each type of pixel block in the h-th group of pixel blocks, where h is a positive integer less than or equal to N - k + 1;

[0170] Calculate the rate-distortion optimization of the h-th group of pixel blocks through the bit rate overhead of the filter coefficients associated with the h-th group of pixel blocks and the distortion of each type of pixel block in the h-th group of pixel blocks.

[0171] In one embodiment, the processing unit 402 is configured to calculate the rate-distortion optimization corresponding to the k-th combination result through rate-distortion optimization of N-k+1 groups of pixel blocks, specifically:

[0172] Accumulate the rate-distortion optimizations of N-k+1 groups of pixel blocks to obtain the rate-distortion optimization corresponding to the k-th combination result.

[0173] In one embodiment, the processing unit 402 is configured to obtain the Wiener-Hopf equation associated with each type of pixel block in the image frame to be encoded, specifically:

[0174] Obtain the image frame to be encoded, where the image frame includes M pixel blocks, each pixel block consists of at least one pixel point, and M is an integer greater than 1;

[0175] Classify the M pixel blocks according to the characteristics of each pixel block, where the characteristics of each pixel block include a direction factor and a quantization activity factor;

[0176] Based on the association relationship between the type of pixel block and the Wiener-Hopf equation, determine the Wiener-Hopf equation associated with each type of pixel block in the image frame.

[0177] In one embodiment, the processing unit 402 is further configured to:

[0178] If there is a group in the N groups of pixel blocks that only includes pixel blocks of one type, determine the Wiener-Hopf equation associated with the pixel blocks in the group as the Wiener-Hopf equation associated with the group.

[0179] In one embodiment, the processing unit 402 is further configured to:

[0180] Generate the bitstream data of the image frame based on the filter coefficient group corresponding to the image frame;

[0181] Send the bitstream data to the decoding end so that the decoding end can present the image frame based on the bitstream data.

[0182] According to an embodiment of the present application, Figure 2 and Figure 3 Some of the steps involved in the data processing method shown can be executed by each unit in the Figure 4 shown data processing device. For example, Figure 2 S201 shown in Figure 4 can be executed by the acquisition unit 401 shown, and S202-S204 can be executed by the Figure 4 shown processing unit 402. Figure 3 S301 shown in Figure 4 can be executed by the acquisition unit 401 shown, and S302-S307 can be executed by the Figure 4 shown processing unit 402. Figure 4Each unit in the data processing device shown can be separately or entirely combined into one or several other units to form, or a certain one (or some) of the units can be further split into multiple smaller units with more specific functions to form. This can achieve the same operations without affecting the realization of the technical effects of the embodiments of this application. The above units are divided based on logical functions. In practical applications, the function of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of this application, the data processing device can also include other units. In practical applications, these functions can also be assisted and realized by other units, and can be realized through the cooperation of multiple units.

[0183] According to another embodiment of this application, it can be achieved by running a computer program (including program code) that can execute the respective steps involved in the corresponding methods shown in Figure 2 and Figure 3 on a general computing device such as a computer that includes processing elements and storage elements such as a central processing unit (CPU), a random access storage medium (RAM), and a read-only storage medium (ROM), to construct a data processing device as shown in Figure 4 and to implement the data processing method of the embodiments of this application. The computer program can be recorded on, for example, a computer-readable recording medium, and be loaded into the above computing device through the computer-readable recording medium and run therein.

[0184] Based on the same inventive concept, the principle and beneficial effects of the data processing device provided in the embodiments of this application for solving problems are similar to those of the data processing method in the method embodiments of this application. For the principle and beneficial effects of the method implementation, reference can be made, and for the sake of brevity of description, they will not be elaborated here.

[0185] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided in the embodiments of this application. As shown in Figure 5As shown in the figure, the computer device at least includes a processor 501, a communication interface 502, and a memory 503. Among them, the processor 501, the communication interface 502, and the memory 503 can be connected through a bus or other means. The processor 501 (or the Central Processing Unit (CPU)) is the computing core and control core of the computer device. It can parse various instructions in the computer device and process various data of the computer device. For example, the CPU can be used to parse the power-on and power-off instructions sent by the user to the computer device and control the computer device to perform power-on and power-off operations; Another example is that the CPU can transfer various types of interaction data between the internal structures of the computer device, and so on. The communication interface 502 may optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.), and under the control of the processor 501, it can be used to send and receive data; The communication interface 502 can also be used for the transmission and interaction of internal data of the computer device. The memory 503 (Memory) is the memory device in the computer device and is used to store programs and data. It can be understood that the memory 503 here can include both the built-in memory of the computer device and, of course, the extended memory supported by the computer device. The memory 503 provides a storage space, and this storage space stores the operating system of the computer device, which may include but is not limited to: Android system, iOS system, Windows Phone system, etc. This application does not make any limitations in this regard.

[0186] The embodiment of this application also provides a computer-readable storage medium (Memory). The computer-readable storage medium is the memory device in the computer device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, and this storage space stores the processing system of the computer device. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by the processor 501, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; Optionally, it can also be at least one computer-readable storage medium located far from the aforementioned processor.

[0187] In one embodiment, the computer device can specifically be Figure 1d the server 102 shown in the figure. The processor 501 executes the following operations by running the executable program code in the memory 503:

[0188] Obtain the Wiener-Hopf equations associated with pixel blocks of various types in the image frame to be encoded;

[0189] Group the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks. Among the N groups of pixel blocks, there is a target group, and the target group includes at least two types of pixel blocks. N is an integer greater than 1;

[0190] Merge the Wiener-Hopf equations associated with pixel blocks of different types in the target group, and use the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group;

[0191] Based on the Wiener-Hopf equations associated with the N groups of pixel blocks, determine the filter coefficient group corresponding to the image frame. The filter coefficient group is used to generate the bitstream data of the image frame.

[0192] As an optional embodiment, the feature of each pixel block includes a direction factor or a quantization activity factor; a specific embodiment in which the processor 501 groups the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks is:

[0193] Group the pixel blocks with the same direction factor into one group to obtain N groups of pixel blocks; or,

[0194] Group the pixel blocks with the same quantization activity factor into one group to obtain N groups of pixel blocks.

[0195] As an optional embodiment, the feature of each pixel block includes a direction factor and a quantization activity factor; a specific embodiment in which the processor 501 groups the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks is:

[0196] Based on the direction factor and quantization activity factor of each pixel block, calculate the feature value of the pixel block;

[0197] Group the pixel blocks whose feature values belong to the same numerical interval into one group to obtain N groups of pixel blocks.

[0198] As an optional embodiment, the target group includes M types of pixel blocks, and M is an integer greater than 1; a specific embodiment in which the processor 501 merges the Wiener-Hopf equations associated with pixel blocks of different types in the target group and uses the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group is:

[0199] Perform a fusion process on the left equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a first fusion result;

[0200] Perform a fusion process on the right equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a second fusion result;

[0201] Construct a target Wiener-Hopf equation based on the first fusion result and the second fusion result. The left side of the target Wiener-Hopf equation is the first fusion result, and the right side of the target Wiener-Hopf equation is the second fusion result;

[0202] Use the target Wiener-Hopf equation as the Wiener-Hopf equation associated with the target grouping.

[0203] As an alternative embodiment, a specific example of the processor 501 determining the filter coefficient group corresponding to the image frame based on the Wiener-Hopf equations associated with N groups of pixel blocks is as follows:

[0204] Generate N combined results based on N groups of pixel blocks. The k-th combined result includes N - k + 1 groups of pixel blocks, where k is a positive integer less than or equal to N;

[0205] Calculate the rate-distortion optimization corresponding to the N combined results through the Wiener-Hopf equations associated with N groups of pixel blocks;

[0206] Determine the filter coefficient group associated with the target combined result as the filter coefficient group corresponding to the image frame. The target combined result is the combined result with the minimum rate-distortion optimization among the N combined results.

[0207] As an alternative embodiment, the process by which the processor 501 generates N combined results based on N groups of pixel blocks includes:

[0208] Merge the i-th group of pixel blocks and the j-th group of pixel blocks to obtain the merged i-th group of pixel blocks. The combined rate-distortion optimization associated with the i-th group of pixel blocks and the j-th group of pixel blocks is the minimum among the combined rate-distortion optimizations associated with any two groups of pixel blocks in the N groups of pixel blocks;

[0209] Merge the Wiener-Hopf equation associated with the i-th group of pixel blocks and the Wiener-Hopf equation associated with the j-th group of pixel blocks to obtain the merged Wiener-Hopf equation associated with the i-th group of pixel blocks.

[0210] As an alternative embodiment, a specific example of the processor 501 calculating the rate-distortion optimization corresponding to the N combined results through the Wiener-Hopf equations associated with N groups of pixel blocks is as follows:

[0211] Solve the Wiener-Hopf equation associated with N - k + 1 groups of pixel blocks to obtain the filter coefficients associated with each group of pixel blocks;

[0212] Calculate the rate-distortion optimization of the group of pixel blocks based on the filter coefficients associated with each group of pixel blocks;

[0213] Calculate the rate-distortion optimization corresponding to the k-th combined result through the rate-distortion optimizations of N - k + 1 groups of pixel blocks.

[0214] As an alternative embodiment, a specific embodiment in which the processor 501 calculates the rate-distortion optimization of each group of pixel blocks based on the filtering coefficients associated with each group of pixel blocks is as follows:

[0215] Based on the filtering coefficients associated with the h-th group of pixel blocks, calculate the distortion of each type of pixel block in the h-th group of pixel blocks, where h is a positive integer less than or equal to N - k + 1;

[0216] Calculate the rate-distortion optimization of the h-th group of pixel blocks through the bitrate overhead of the filtering coefficients associated with the h-th group of pixel blocks and the distortion of each type of pixel block in the h-th group of pixel blocks.

[0217] As an alternative embodiment, a specific embodiment in which the processor 501 calculates the rate-distortion optimization corresponding to the k-th combined result through the rate-distortion optimization of N - k + 1 groups of pixel blocks is as follows:

[0218] Accumulate the rate-distortion optimizations of N - k + 1 groups of pixel blocks to obtain the rate-distortion optimization corresponding to the k-th combined result.

[0219] As an alternative embodiment, a specific embodiment in which the processor 501 obtains the Wiener-Hopf equation associated with each type of pixel block in the image frame to be encoded is as follows:

[0220] Obtain the image frame to be encoded, where the image frame includes M pixel blocks, each pixel block is composed of at least one pixel point, and M is an integer greater than 1;

[0221] Classify the M pixel blocks according to the characteristics of each pixel block, where the characteristics of each pixel block include a direction factor and a quantization activity factor;

[0222] Based on the association relationship between the type of pixel block and the Wiener-Hopf equation, determine the Wiener-Hopf equation associated with each type of pixel block in the image frame.

[0223] As an alternative embodiment, the processor 501 further performs the following operations by running the executable program code in the memory 503:

[0224] If there is a group among the N groups of pixel blocks that includes only one type of pixel block, determine the Wiener-Hopf equation associated with the pixel blocks in that group as the Wiener-Hopf equation associated with that group.

[0225] As an alternative embodiment, the processor 501 further performs the following operations by running the executable program code in the memory 503:

[0226] Generate the bitstream data of the image frame based on the filtering coefficient group corresponding to the image frame;

[0227] Send the bitstream data to the decoding end so that the decoding end presents the image frame based on the bitstream data.

[0228] Based on the same inventive concept, the principles and beneficial effects of the computer device provided in the embodiments of the present application for solving problems are similar to those of the data processing method in the method embodiments of the present application. For the principles and beneficial effects of the method implementation, reference can be made, and for the sake of brevity of description, they will not be elaborated here.

[0229] The embodiments of the present application further provide a computer-readable storage medium, in which a computer program is stored, and the computer program is adapted to be loaded and executed by a processor to perform the data processing method in the above method embodiments.

[0230] The embodiments of the present application further provide a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above data processing method.

[0231] The steps in the method embodiments of the present application can be adjusted, combined, and deleted according to actual needs.

[0232] The modules in the device embodiments of the present application can be combined, divided, and deleted according to actual needs.

[0233] In the embodiments of the present application, the "module" or "unit" involved refers to a computer program or a part of a computer program with a predetermined function, which works together with other relevant parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit including the functions of the module or unit.

[0234] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium, and the readable storage medium can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0235] The above-disclosed is only a preferred embodiment of the present application. Of course, the scope of the rights of the present application cannot be limited thereby. Those of ordinary skill in the art can understand the entire or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present application still fall within the scope covered by the application.

Claims

1. A data processing method, characterized in that, The method includes: Obtaining Wiener-Hopf equations associated with pixel blocks of various types in an image frame to be encoded; Grouping the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks, where there is a target group among the N groups of pixel blocks, the target group includes at least two types of pixel blocks, and N is an integer greater than 1; Merging the Wiener-Hopf equations associated with different types of pixel blocks in the target group, and using the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group; Determining a filter coefficient group corresponding to the image frame based on the Wiener-Hopf equations associated with the N groups of pixel blocks, where the filter coefficient group is used to generate bitstream data of the image frame.

2. The method according to claim 1, wherein The feature of each pixel block includes a direction factor or a quantization activity factor; the grouping the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks includes: Grouping pixel blocks with the same direction factor into one group to obtain N groups of pixel blocks; or, Grouping pixel blocks with the same quantization activity factor into one group to obtain N groups of pixel blocks.

3. The method according to claim 1, characterized in that, The feature of each pixel block includes a direction factor and a quantization activity factor; The grouping the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks includes: Calculating a feature value of each pixel block based on the direction factor and quantization activity factor of the pixel block; Grouping pixel blocks with feature values belonging to the same numerical interval into one group to obtain N groups of pixel blocks.

4. The method according to claim 1, wherein The target group includes M types of pixel blocks, where M is an integer greater than 1; the merging the Wiener-Hopf equations associated with different types of pixel blocks in the target group, and using the merged Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group includes: Performing a fusion process on the left equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a first fusion result; Performing a fusion process on the right equalities of the Wiener-Hopf equations associated with the M types of pixel blocks to obtain a second fusion result; Constructing a target Wiener-Hopf equation based on the first fusion result and the second fusion result, where the left equality of the target Wiener-Hopf equation is the first fusion result, and the right equality of the target Wiener-Hopf equation is the second fusion result; Using the target Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group.

5. The method according to claim 1, wherein The determining the filter coefficient group corresponding to the image frame based on the Wiener-Hopf equations associated with the N groups of pixel blocks includes: Generating N combined results based on the N groups of pixel blocks, where the k-th combined result includes N - k + 1 groups of pixel blocks, and k is a positive integer less than or equal to N; Calculating the rate-distortion optimization corresponding to the N combined results through the Wiener-Hopf equations associated with the N groups of pixel blocks; Determining the filter coefficient group associated with the target combined result as the filter coefficient group corresponding to the image frame, where the target combined result is the combined result with the minimum rate-distortion optimization among the N combined results.

6. The method according to claim 5, wherein The process of generating N combined results based on the N groups of pixel blocks includes: Merge the i-th group of pixel blocks and the j-th group of pixel blocks to obtain the merged i-th group of pixel blocks. The rate-distortion optimization associated with the merging of the i-th group of pixel blocks and the j-th group of pixel blocks is the smallest among the rate-distortion optimizations associated with the merging of any two groups of pixel blocks in the N groups of pixel blocks. Merge the Wiener-Hopf equation associated with the i-th group of pixel blocks and the Wiener-Hopf equation associated with the j-th group of pixel blocks to obtain the Wiener-Hopf equation associated with the merged i-th group of pixel blocks.

7. The method according to claim 5, characterized in that Calculating the rate-distortion optimization corresponding to the N merging results through the Wiener-Hopf equations associated with the N groups of pixel blocks includes: Solve the Wiener-Hopf equations associated with the N - k + 1 groups of pixel blocks to obtain the filtering coefficients associated with each group of pixel blocks. Based on the filtering coefficients associated with each group of pixel blocks, calculate the rate-distortion optimization of this group of pixel blocks. Through the rate-distortion optimizations of the N - k + 1 groups of pixel blocks, calculate the rate-distortion optimization corresponding to the k-th merging result.

8. The method according to claim 7, wherein The calculating the rate-distortion optimization of this group of pixel blocks based on the filtering coefficients associated with each group of pixel blocks includes: Based on the filtering coefficients associated with the h-th group of pixel blocks, calculate the distortion of each type of pixel block in the h-th group of pixel blocks, where h is a positive integer less than or equal to N - k + 1. Through the bitrate overhead of the filtering coefficients associated with the h-th group of pixel blocks and the distortion of each type of pixel block in the h-th group of pixel blocks, calculate the rate-distortion optimization of the h-th group of pixel blocks.

9. The method according to claim 7, characterized in that The calculating the rate-distortion optimization corresponding to the k-th merging result through the rate-distortion optimizations of the N - k + 1 groups of pixel blocks includes: Accumulate the rate-distortion optimizations of the N - k + 1 groups of pixel blocks to obtain the rate-distortion optimization corresponding to the k-th merging result.

10. The method according to claim 1, characterized in that, The obtaining the Wiener-Hopf equations associated with each type of pixel block in the image frame to be encoded includes: Obtain the image frame to be encoded. The image frame includes M pixel blocks, and each pixel block is composed of at least one pixel point, where M is an integer greater than 1. Classify the M pixel blocks according to the characteristics of each pixel block. The characteristics of each pixel block include a direction factor and a quantization activity factor. Based on the association relationship between the type of pixel block and the Wiener-Hopf equation, determine the Wiener-Hopf equations associated with each type of pixel block in the image frame.

11. The method according to claim 1, characterized in that, The method further includes: If there is a group in the N groups of pixel blocks that only includes pixel blocks of one type, then determine the Wiener-Hopf equation associated with the pixel blocks in this group as the Wiener-Hopf equation associated with this group.

12. The method according to claim 1, characterized in that The method further includes: Generate the bitstream data of the image frame based on the filtering coefficient group corresponding to the image frame. Send the bitstream data to the decoding end so that the decoding end presents the image frame based on the bitstream data.

13. A data processing device, characterized in that, The data processing device includes: An acquisition unit for acquiring the Wiener-Hopf equations associated with each type of pixel block in the image frame to be encoded. A processing unit for grouping the pixel blocks in the image frame according to a preset grouping rule to obtain N groups of pixel blocks. There is a target group in the N groups of pixel blocks, and the target group includes at least two types of pixel blocks, where N is an integer greater than 1. And for combining the Wiener-Hopf equations associated with different types of pixel blocks in the target group, and using the combined Wiener-Hopf equation as the Wiener-Hopf equation associated with the target group; And for determining, based on the Wiener-Hopf equations associated with the N groups of pixel blocks, a filter coefficient group corresponding to the image frame, where the filter coefficient group is used to generate the bitstream data of the image frame.

14. A computer device, characterized in that, Comprising: A memory storing a computer program; A processor for loading the computer program to implement the data processing method according to any one of claims 1-12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is adapted to be loaded and executed by the processor to implement the data processing method according to any one of claims 1-12.

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