CN108989805A - Image processing method and device based on WebP image compression algorithm - Google Patents

Abstract

The invention discloses an image processing method, device, device and computer-readable storage medium based on a WebP image compression algorithm, comprising: acquiring YUV image data of an image to be processed, and dividing the Y data channel of the YUV image data into multiple After Y macroblocks of a preset size, each Y macroblock is divided into sub-blocks of a plurality of preset sizes; according to the dependency relationship between the sub-blocks in the Y macroblock, the traversal of the sub-blocks in the Y macroblock Reorganize in sequence; process the subblocks in each Y macroblock according to the subblock traversal order after reorganization, so as to obtain the data processing results of each Y macroblock in the Y data channel; use each of the Y data channels The data processing result of the Y macroblock is used to obtain the brightness value of the image to be detected. The image processing method, device, equipment and computer-readable storage medium provided by the present invention enable the sub-blocks in the macro block to be executed in a pipeline, and improve the throughput performance of the Web image compression algorithm.

Description

Image processing method and device based on WebP image compression algorithm

technical field

The present invention relates to the technical field of image processing, in particular to an image processing method, device, equipment and computer-readable storage medium based on a WebP image compression algorithm.

Background technique

With the development of image acquisition equipment such as mobile phones, tablets, and digital cameras, and the increase in image pixel scale, the scale of Internet image data has grown exponentially. The latest research shows that from 2016 to 2021, the data storage scale on data center servers will increase four-fold, from 663EB to 2.6ZB, and most of the data storage comes from images and videos. For example, in 2013, Facebook users uploaded more than 250 billion pictures. By 2015, Facebook added nearly 2 billion pictures every day; Added 8 billion image storage capacity. This will bring serious challenges to the data storage and network bandwidth of the data center server.

Since the compression coding algorithm optimization of the current JPEG image file format has almost reached the extreme in theory, in order to reduce the size of image storage, the image file format WebP with a higher compression ratio is often used to replace the existing JPEG image file. Compared with the lossy compression algorithm, the WebP image lossy compression algorithm can reduce the file size by about 30%. Using the WebP image compression algorithm on the data center server to encode and convert JPEG files can effectively relieve the pressure on data center storage and network bandwidth access brought about by the increase in image size.

However, the data processing process of the WebP image lossy compression algorithm has high computational complexity, and the Y data channel and the UV data channel are processed separately. The UV data channel only needs to perform prediction, transformation, quantization, and compression coding processes at the macroblock level. The Y channel requires the process of prediction, transformation, quantization, and compression coding for each macroblock level, and also needs to perform a repeated prediction, transformation, quantization, and compression coding process for each sub-block in each macroblock. When processing the Y data channel, first, process at the macroblock level, use the macroblock boundary to predict the macroblock data, select the optimal macroblock prediction mode, and calculate the macroblock original data and Predict the residual value of the data to obtain the image residual data macroblock that needs to be compressed and encoded; secondly, use DCT (Discrete Cosine Transform) transformation, WHT (Walsh-Hadamard Transform) transformation, and quantization steps to calculate the compressed residual difference coefficient. When the Y data channel is processed at the sub-block level, a separate prediction process is performed for each sub-block, the optimal prediction mode of each sub-block is selected, and the ratio between the original data and the predicted data of each sub-block is calculated according to the optimal prediction mode of each sub-block. residual value; secondly, the DCT transform, WHT transform, and quantization steps are used to calculate the residual coefficient of each sub-block after compression. By comparing the residual coefficients obtained at the macroblock level and the subblock level, the optimal subblock or macroblock prediction mode is selected. If the sub-block prediction mode is finally selected, a separate prediction is required for each sub-block in the Y macroblock, and inverse quantization, DCT inverse transform, and WHT inverse transform are performed on the residual coefficient of each sub-block to obtain the adjacent sub-block Boundary value required for block prediction.

When implementing the WebP algorithm based on OpenCL, for example, the third Y macroblock can only be processed after the second Y macroblock is processed, and the fourth Y macroblock can only be started after the third Y macroblock is processed; and Y The sub-block processing flow of the data channel can only be executed serially in this way, and the pipeline execution between sub-blocks cannot be realized, resulting in a low overall throughput rate and algorithm performance cannot be improved.

From the above, it can be seen that how to implement pipeline execution between sub-blocks in Y macroblock is a problem to be solved at present.

Contents of the invention

The object of the present invention is to provide a kind of image processing method, device, equipment and computer-readable storage medium based on WebP image compression algorithm, have solved the problem that cannot realize the flow execution between the sub-blocks in the macroblock when Webp algorithm is executed in the prior art.

In order to solve the above technical problems, the present invention provides an image processing method based on the WebP image compression algorithm, comprising: acquiring YUV image data of the image to be processed, and dividing the Y data channel of the YUV image data into a plurality of preset sizes After the Y macroblock, each Y macroblock is divided into sub-blocks of a plurality of preset sizes; according to the dependency between the sub-blocks in the Y macroblock, the traversal order of the sub-blocks in the Y macroblock is reorganized; according to The reorganized sub-block traversal sequence processes the sub-blocks in each Y macroblock, thereby obtaining the data processing results of each Y macroblock in the Y data channel; using the data of each Y macroblock in the Y data channel As a result of processing, the brightness value of the image to be detected is obtained.

Preferably, said processing the sub-blocks in each Y macroblock according to the reorganized sub-block traversal order includes:

According to the dependency relationship between the right boundary of the first subblock and the left boundary of the second subblock in the Y macroblock, after the processing of the first subblock is completed, the second subblock is processed; After the processing of the second sub-block, while processing the third sub-block of the Y macro-block, process the fourth sub-block of the Y macro-block; wherein, the left side of the Y macro-block is selected The common sub-block in the boundary and the upper boundary is used as the first sub-block; the left boundary of the second sub-block is adjacent to the right boundary of the first sub-block; the left boundary of the third sub-block is adjacent to the The right boundary of the second sub-block is adjacent; the upper boundary of the fourth sub-block is adjacent to the lower boundary of the first sub-block.

Preferably, after the processing of the second sub-block is completed, processing the third sub-block of the Y macroblock while processing the fourth sub-block of the Y macroblock includes:

After the processing of the third sub-block is completed, the fifth sub-block of the Y macroblock is processed; after the processing of the third sub-block and the fourth sub-block are both completed, the Y macroblock is processed The sixth sub-block of the Y macroblock is processed; after the processing of the sixth sub-block is completed, the seventh sub-block and the eighth sub-block of the Y macroblock are processed at the same time; wherein, the left boundary of the fifth sub-block Adjacent to the right boundary of the third macroblock, the left boundary of the sixth subblock is adjacent to the right boundary of the fourth subblock; the left boundary of the seventh subblock is adjacent to the sixth subblock right borders of the blocks are adjacent; the upper border of the eighth sub-block is adjacent to the lower border of the fourth sub-block.

Preferably, after the processing of the sixth sub-block is completed, processing the seventh sub-block and the eighth sub-block of the Y macroblock at the same time includes:

After the seventh sub-block is processed, the ninth sub-block and the tenth sub-block of the Y macroblock are processed simultaneously; after the tenth sub-block is processed, the tenth sub-block of the Y macroblock is processed. A sub-block and the twelfth sub-block are processed at the same time; after the eleventh sub-block is processed, the thirteenth sub-block and the fourteenth sub-block of the Y macroblock are simultaneously processed; wherein, the The left boundary of the ninth sub-block is adjacent to the right boundary of the seventh macroblock, and the left boundary of the tenth sub-block is adjacent to the right boundary of the eighth sub-block; the eleventh sub-block The left boundary of the tenth sub-block is adjacent to the right boundary; the upper boundary of the twelfth sub-block is adjacent to the lower boundary of the eighth sub-block; the left boundary of the thirteenth sub-block is adjacent to the The right boundary of the eleventh sub-block is adjacent; the left boundary of the fourteenth sub-block is adjacent to the right boundary of the twelfth sub-block.

Preferably, after obtaining the YUV image data of the image to be processed, after dividing the Y data channel of the YUV image data into a plurality of Y macroblocks of a preset size, each Y macroblock is divided into a plurality of preset sizes The subblocks include:

After obtaining the YUV image data of the image to be processed, every 16×16 pixel Y data in the Y data channel of the YUV image data is formed into a Y macroblock, wherein each Y macroblock occupies 16 pixel positions horizontally and vertically It occupies 16 pixel positions; after the Y data channel is divided into multiple Y macroblocks with a size of 16×16, each Y macroblock is divided into 16 sub-blocks with a size of 4×4.

The present invention also provides an image processing device based on the WebP image compression algorithm, including:

The acquisition module acquires the YUV image data of the image to be processed, divides the Y data channel of the YUV image data into a plurality of Y macroblocks of a preset size, and then divides each Y macroblock into a plurality of subscales of a preset size piece;

A reorganization module, configured to reorganize the traversal order of the subblocks in the Y macroblock according to the dependency relationship between the subblocks in the Y macroblock;

The processing module is used to process the sub-blocks in each Y macro-block according to the reorganized sub-block traversal order, so as to obtain the data processing results of each Y macro-block in the Y data channel;

A calculation module, configured to use the data processing results of each Y macroblock in the Y data channel to obtain the brightness value of the image to be detected.

Preferably, the processing module is specifically used for:

According to the dependency relationship between the right boundary of the first subblock and the left boundary of the second subblock in the Y macroblock, after the processing of the first subblock is completed, the second subblock is processed; After the processing of the second sub-block, while processing the third sub-block of the Y macro-block, process the fourth sub-block of the Y macro-block; wherein, the left side of the Y macro-block is selected The common sub-block in the boundary and the upper boundary is used as the first sub-block; the left boundary of the second sub-block is adjacent to the right boundary of the first sub-block; the left boundary of the third sub-block is adjacent to the The right boundary of the second sub-block is adjacent; the upper boundary of the fourth sub-block is adjacent to the lower boundary of the first sub-block.

Preferably, after the processing of the second sub-block is completed, processing the third sub-block of the Y macroblock while processing the fourth sub-block of the Y macroblock includes:

After the processing of the third sub-block is completed, the fifth sub-block of the Y macroblock is processed; after the processing of the third sub-block and the fourth sub-block are both completed, the Y macroblock is processed The sixth sub-block of the Y macroblock is processed; after the processing of the sixth sub-block is completed, the seventh sub-block and the eighth sub-block of the Y macroblock are processed at the same time; wherein, the left boundary of the fifth sub-block Adjacent to the right boundary of the third macroblock, the left boundary of the sixth subblock is adjacent to the right boundary of the fourth subblock; the left boundary of the seventh subblock is adjacent to the sixth subblock right borders of the blocks are adjacent; the upper border of the eighth sub-block is adjacent to the lower border of the fourth sub-block.

The present invention also provides an image processing device based on the WebP image compression algorithm, including:

The memory is used to store the computer program; the processor is used to implement the steps of the above-mentioned image processing method based on the WebP image compression algorithm when the computer program is executed.

The present invention also provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned image processing method based on the WebP image compression algorithm are realized .

In the image processing method based on the WebP image compression algorithm provided by the present invention, after obtaining the YUV image data of the image to be processed, after dividing the Y data channel in the YUV image data into a plurality of Y macroblocks of a preset size, the Each Y macroblock is divided into multiple sub-blocks of preset size. Reorganize the traversal order of the subblocks in the Y macroblock according to the dependencies among the subblocks in the Y macroblock, and process the subblocks in each Y macroblock according to the reorganized subblock traversal order, so as to obtain The data processing result of each Y macroblock in the Y data channel; using the data processing result of each Y macroblock in the Y data channel to obtain the brightness value of the image to be detected. In the image processing method provided by the present invention, the sub-blocks that cause dependencies on adjacent sub-blocks are preferentially processed, so that the sub-blocks in the Y macroblock can be executed in a pipeline, and the throughput performance of the Web image compression algorithm is improved.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only For some embodiments of the present invention, those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is the flow chart of the first specific embodiment of a kind of image processing method based on WebP image compression algorithm provided by the present invention;

Fig. 2 is the flowchart of the second specific embodiment of the image processing method based on WebP image compression algorithm provided by the present invention;

Fig. 3 is a schematic diagram of the dependence of sub-block data on boundary data;

FIG. 4 is a schematic diagram of dependencies among sub-blocks in a macroblock;

FIG. 5 is a schematic diagram of the actual traversal sequence of sub-blocks in a macroblock after sub-block traversal sequence reorganization;

Fig. 6 is a structural block diagram of an image processing device based on a WebP image compression algorithm provided by an embodiment of the present invention.

Detailed ways

The core of the present invention is to provide an image processing method, device, equipment and computer-readable storage medium based on the WebP image compression algorithm, so that the sub-blocks in the macroblock can be executed in a pipeline, and the throughput performance of the Web image compression algorithm is improved.

In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Apparently, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

Please refer to Fig. 1, Fig. 1 is the flow chart of the first kind of specific embodiment of the image processing method based on WebP image compression algorithm provided by the present invention; Concrete operation steps are as follows:

Step S101: Acquire the YUV image data of the image to be processed, divide the Y data channel of the YUV image data into a plurality of Y macroblocks of a preset size, and then divide each Y macroblock into a plurality of sub-blocks of a preset size piece;

After obtaining the YUV image data of the image to be processed, every 16×16 pixel Y data in the Y data channel of the YUV image data is formed into a Y macroblock, wherein each Y macroblock occupies 16 pixel positions horizontally and vertically Occupies 16 pixel positions; for the data whose image boundary does not meet the integer multiple of 16, it is filled by copying the boundary pixels.

After the Y data channel is divided into multiple Y macroblocks with a size of 16×16, each Y macroblock is divided into 16 sub-blocks with a size of 4×4. Each sub-block occupies 4 pixel positions horizontally and 4 pixel positions vertically in the Y pixel macroblock data coordinates of the image, and each Y macroblock contains 16 4×4 sub-blocks in total.

Step S102: Reorganize the traversal order of the sub-blocks in the Y macro-block according to the dependency between the sub-blocks in the Y macro-block;

Step S103: Process the sub-blocks in each Y macro-block according to the reorganized sub-block traversal order, so as to obtain the data processing results of each Y macro-block in the Y data channel;

Step S104: using the data processing results of each Y macroblock in the Y data channel to obtain the brightness value of the image to be detected.

The image processing method based on the WebP image compression algorithm provided by this embodiment solves the problem that the sub-blocks in the macroblock can only be executed serially when implementing the FPGA hardware circuit mapping of the WebP image lossy compression algorithm using OpenCL; The traversal order of the sub-blocks in the macro block is reorganized to alleviate the impact of the boundary data dependencies between sub-blocks on the pipeline between sub-blocks during program execution, improve the pipeline execution performance of the sub-blocks in the macro block, and thus improve the energy efficiency of the FPGA heterogeneous accelerated WebP image compression algorithm .

Based on the above embodiment, in this embodiment, according to the boundary dependency between the sub-blocks in the Y macroblock, after the first sub-block and the second sub-block in the Y macroblock, the third sub-block can be realized And the simultaneous processing of the 4th sub-block, please refer to Fig. 2, Fig. 2 is the flow chart of the second specific embodiment of the image processing method based on WebP image compression algorithm provided by the present invention; Concrete operation steps are as follows:

Step S201: obtain the YUV image data of the image to be processed, after the Y data channel is divided into a plurality of Y macroblocks of 16 × 16 sizes, each Y macroblock is divided into 16 sub-blocks of 4 × 4 sizes;

Step S202: According to the dependency relationship between the right boundary of the first subblock and the left boundary of the second subblock in the Y macroblock, after the processing of the first subblock is completed, process the second subblock ;

As shown in Figure 3 and Figure 4, Figure 3 is a schematic diagram of the dependency relationship between sub-block data and boundary data; Figure 4 is a schematic diagram of the dependency relationship between sub-blocks in a macroblock; the sub-block index in the original Y macroblock is defined as y_sub[index ], y_sub[i] indicates that when traversing the sub-blocks in the Y macroblock, the i-th sub-block index value to be processed is y_sub[i]. Among them, the maximum value of index is equal to 16, which means that the Y macroblock contains 16 4×4 sub-blocks, and the original sub-block order traversal method in the Y macroblock is: y_sub_old[i]={1,2,3,4,5,6 ,7,8,9,10,11,12,13,14,15,16}.

Step S203: After completing the processing of the second sub-block, while processing the third sub-block of the Y macro-block, process the fourth sub-block of the Y macro-block;

The traversal order of the sub-blocks in the macroblock after reorganization is shown in Figure 5, and the sub-block numbers in Figure 5 represent the traversal order of the sub-block positions in the Y macroblock after reorganization. Select the common sub-block in the left boundary and the upper boundary of the Y macroblock as the first sub-block; the left boundary of the second sub-block is adjacent to the right boundary of the first sub-block; The left border of the third sub-block is adjacent to the right border of the second sub-block; the upper border of the fourth sub-block is adjacent to the lower border of the first sub-block.

Step S204: After processing the third sub-block, process the fifth sub-block of the Y macroblock;

Step S205: After the processing of the third sub-block and the fourth sub-block is completed, process the sixth sub-block of the Y macroblock;

Wherein, the left boundary of the fifth sub-block is adjacent to the right boundary of the third macroblock, and the left boundary of the sixth sub-block is adjacent to the right boundary of the fourth sub-block; the seventh The left boundary of the sub-block is adjacent to the right boundary of the sixth sub-block; the upper boundary of the eighth sub-block is adjacent to the lower boundary of the fourth sub-block.

Step S206: After processing the seventh sub-block, process the ninth sub-block and the tenth sub-block of the Y macroblock simultaneously;

Step S207: After the processing of the tenth sub-block is completed, the eleventh sub-block and the twelfth sub-block of the Y macroblock are processed simultaneously;

Step S208: After the eleventh sub-block is processed, simultaneously process the thirteenth and fourteenth sub-blocks of the Y macroblock;

Wherein, the left boundary of the ninth sub-block is adjacent to the right boundary of the seventh macroblock, and the left boundary of the tenth sub-block is adjacent to the right boundary of the eighth sub-block; the tenth The left boundary of a sub-block is adjacent to the right boundary of the tenth sub-block; the upper boundary of the twelfth sub-block is adjacent to the lower boundary of the eighth sub-block; the thirteenth sub-block The left boundary is adjacent to the right boundary of the eleventh sub-block; the left boundary of the fourteenth sub-block is adjacent to the right boundary of the twelfth sub-block.

Step S209: After completing the processing of the fourteenth sub-block, sequentially process the fifteenth sub-block and the sixteenth sub-block, so as to obtain the data processing result of the Y macroblock;

Wherein, the left boundary of the fifteenth sub-block is adjacent to the right boundary of the fourteenth sub-block, and the left boundary of the sixteenth sub-block is adjacent to the right boundary of the fifteenth sub-block.

Step S210: Obtain the data processing results of each Y macroblock in the Y data channel, and then use the data processing results of each Y macroblock in the Y data channel to obtain the brightness value of the image to be detected.

In this embodiment, after reorganizing and mapping the traversal order of the 4×4 sub-blocks in the Y macroblock, the traversal order of the sub-blocks in the Y macroblock is:

y_sub_new[i]={y_sub_old[1,2,3], y_sub_old[5], y_sub_old[4],

y_sub_old[6,7], y_sub_old[9], y_sub_old[8], y_sub_old[10,11],

y_sub_old[13], y_sub_old[12], ? y_sub_old[14,15,16]}

According to the mapping order of y_sub_new[i], the 4×4 sub-blocks of the image Y macroblock data are reorganized in traversal order. After the first three sub-blocks are serially processed, the pipeline execution between the remaining sub-blocks can be realized, avoiding all sub-blocks. Blocks are executed serially, which can improve the performance of macro block processing on FPGA.

The image processing method based on the WebP image compression algorithm provided by this embodiment can realize the pipeline execution among the sub-blocks in the Y macroblock by reorganizing and mapping the traversal order of the sub-blocks in the Y macroblock, and the sub-blocks in the Y macroblock Improve the inefficiency of serial processing between sub-blocks in a macro block caused by the data dependency between the boundaries, realize the pipeline of the processing process between sub-blocks in a macro block, and improve the throughput performance of the WebP image compression algorithm.

Please refer to Fig. 6, Fig. 6 is a structural block diagram of an image processing device based on a WebP image compression algorithm provided by an embodiment of the present invention; the specific device may include:

The image processing device based on the WebP image compression algorithm of this embodiment is used to implement the aforementioned image processing method based on the WebP image compression algorithm, so the specific implementation in the image processing device based on the WebP image compression algorithm can be seen in the previous article based on WebP image The embodiment part of the image processing method of the compression algorithm, for example, the acquisition module 100, the reorganization module 200, the processing module 300, and the calculation module 400 are respectively used to realize steps S101, S102, and S103 in the above-mentioned image processing method based on the WebP image compression algorithm and S104, therefore, for its specific implementation, reference may be made to the descriptions of the corresponding partial embodiments, and details are not repeated here.

A specific embodiment of the present invention also provides an image processing device based on a WebP image compression algorithm, including: a memory for storing a computer program; a processor for implementing the above-mentioned WebP image compression algorithm when executing the computer program The steps of the image processing method.

A specific embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned image processing based on the WebP image compression algorithm is realized method steps.

Each embodiment in this specification is described in a progressive manner, each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other. For the device disclosed in the embodiment, because it corresponds to the method disclosed in the embodiment, it is relatively simple to describe, and for relevant parts, please refer to the description of the method part.

Professionals can further realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the possible For interchangeability, in the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are implemented by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be directly implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known storage medium.

The image processing method, device, equipment and computer-readable storage medium based on the WebP image compression algorithm provided by the present invention have been introduced in detail above. In this paper, specific examples are used to illustrate the principles and implementation methods of the present invention, and the descriptions of the above embodiments are only used to help understand the method and core idea of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims (10)

1. an image processing method based on WebP image compression algorithm, is characterized in that, comprises: Obtain the YUV image data of the image to be processed, divide the Y data channel of the YUV image data into a plurality of Y macroblocks of preset size, and divide each Y macroblock into subblocks of a plurality of preset sizes; Reorganizing the traversal order of the sub-blocks in the Y macro-block according to the dependencies among the sub-blocks in the Y macro-block; Processing the sub-blocks in each Y macro-block according to the reorganized sub-block traversal order, so as to obtain the data processing results of each Y macro-block in the Y data channel; The brightness value of the image to be detected is obtained by using the data processing results of each Y macroblock in the Y data channel. 2. The image processing method according to claim 1, wherein said processing the sub-blocks in each Y macroblock according to the reorganized sub-block traversal order comprises: According to the dependency relationship between the right boundary of the first subblock and the left boundary of the second subblock in the Y macroblock, after the processing of the first subblock is completed, process the second subblock; After the processing of the second sub-block is completed, while processing the third sub-block of the Y macro-block, process the fourth sub-block of the Y macro-block; Wherein, the common sub-block in the left boundary and the upper boundary of the Y macroblock is selected as the first sub-block; the left boundary of the second sub-block is adjacent to the right boundary of the first sub-block; The left boundary of the third sub-block is adjacent to the right boundary of the second sub-block; the upper boundary of the fourth sub-block is adjacent to the lower boundary of the first sub-block. 3. The image processing method according to claim 2, characterized in that, after the processing of the second sub-block is completed, while the third sub-block of the Y macroblock is processed, the After processing, the fourth sub-block of the Y macroblock includes: After processing the third sub-block, process the fifth sub-block of the Y macroblock; After the processing of the third sub-block and the fourth sub-block is completed, process the sixth sub-block of the Y macroblock; After the sixth sub-block is processed, simultaneously process the seventh sub-block and the eighth sub-block of the Y macroblock; Wherein, the left boundary of the fifth sub-block is adjacent to the right boundary of the third macroblock, and the left boundary of the sixth sub-block is adjacent to the right boundary of the fourth sub-block; the seventh The left boundary of the sub-block is adjacent to the right boundary of the sixth sub-block; the upper boundary of the eighth sub-block is adjacent to the lower boundary of the fourth sub-block. 4. The image processing method according to claim 3, wherein after the processing of the sixth sub-block is completed, the seventh sub-block and the eighth sub-block of the Y macroblock are processed simultaneously include: After processing the seventh sub-block, simultaneously process the ninth sub-block and the tenth sub-block of the Y macroblock; After the processing of the tenth sub-block is completed, simultaneously process the eleventh sub-block and the twelfth sub-block of the Y macroblock; After the processing of the eleventh sub-block is completed, simultaneously process the thirteenth sub-block and the fourteenth sub-block of the Y macroblock; Wherein, the left boundary of the ninth sub-block is adjacent to the right boundary of the seventh macroblock, and the left boundary of the tenth sub-block is adjacent to the right boundary of the eighth sub-block; the tenth The left boundary of a sub-block is adjacent to the right boundary of the tenth sub-block; the upper boundary of the twelfth sub-block is adjacent to the lower boundary of the eighth sub-block; the thirteenth sub-block The left boundary is adjacent to the right boundary of the eleventh sub-block; the left boundary of the fourteenth sub-block is adjacent to the right boundary of the twelfth sub-block. 5. The image processing method according to claim 1, characterized in that, after the YUV image data of the image to be processed is obtained, the Y data channel of the YUV image data is divided into Y macroblocks of a plurality of preset sizes , dividing each Y macroblock into sub-blocks of a plurality of preset sizes includes: After the YUV image data of the image to be processed is obtained, every 16×16 pixel Y data in the Y data channel of the YUV image data is formed into a Y macroblock, wherein each Y macroblock occupies 16 pixel positions horizontally and vertically Occupies 16 pixel positions; After the Y data channel is divided into multiple Y macroblocks with a size of 16×16, each Y macroblock is divided into 16 sub-blocks with a size of 4×4. 6. An image processing device based on a WebP image compression algorithm, characterized in that it comprises: The acquisition module acquires the YUV image data of the image to be processed, divides the Y data channel of the YUV image data into a plurality of Y macroblocks of a preset size, and divides each Y macroblock into a plurality of preset size sub-blocks piece; A reorganization module, configured to reorganize the traversal order of the subblocks in the Y macroblock according to the dependency relationship between the subblocks in the Y macroblock; A processing module, configured to process the subblocks in each Y macroblock according to the reorganized subblock traversal order, so as to obtain the data processing results of each Y macroblock in the Y data channel; A calculation module, configured to use the data processing results of each Y macroblock in the Y data channel to obtain the brightness value of the image to be detected. 7. The image processing device according to claim 6, wherein the processing module is specifically used for: According to the dependency relationship between the right boundary of the first subblock and the left boundary of the second subblock in the Y macroblock, after the processing of the first subblock is completed, process the second subblock; After the processing of the second sub-block is completed, while processing the third sub-block of the Y macro-block, process the fourth sub-block of the Y macro-block; Wherein, the common sub-block in the left boundary and the upper boundary of the Y macroblock is selected as the first sub-block; the left boundary of the second sub-block is adjacent to the right boundary of the first sub-block; The left boundary of the third sub-block is adjacent to the right boundary of the second sub-block; the upper boundary of the fourth sub-block is adjacent to the lower boundary of the first sub-block. 8. The image processing device according to claim 7, characterized in that, after the processing of the second sub-block is completed, while the third sub-block of the Y macroblock is being processed, the After processing, the fourth sub-block of the Y macroblock includes: After processing the third sub-block, process the fifth sub-block of the Y macroblock; After the processing of the third sub-block and the fourth sub-block is completed, process the sixth sub-block of the Y macroblock; After the sixth sub-block is processed, simultaneously process the seventh sub-block and the eighth sub-block of the Y macroblock; Wherein, the left boundary of the fifth sub-block is adjacent to the right boundary of the third macroblock, and the left boundary of the sixth sub-block is adjacent to the right boundary of the fourth sub-block; the seventh The left boundary of the sub-block is adjacent to the right boundary of the sixth sub-block; the upper boundary of the eighth sub-block is adjacent to the lower boundary of the fourth sub-block. 9. An image processing device based on a WebP image compression algorithm, characterized in that, comprising: memory for storing computer programs; The processor is configured to implement the steps of an image processing method based on the WebP image compression algorithm as described in any one of claims 1 to 5 when executing the computer program. 10. A computer-readable storage medium, characterized in that, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program according to any one of claims 1 to 5 is implemented. The steps of the image processing method based on the WebP image compression algorithm.