WO2023231265A1 - Lossless compression algorithm and apparatus based on embedded system picture, and computer device and storage medium - Google Patents

Abstract

Provided in the present application are a lossless compression algorithm and apparatus based on an embedded system picture, and a computer device and a storage medium. The algorithm comprises: determining the least frequent data in source data or data in non-source data, and marking the data as node data; compiling statistics on a data length of compressible data in the source data and marking a position, so as to obtain statistical data; if the node data appears in the source data, inserting a specific character after the node data, and if the node data is the data in the non-source data, not performing any processing so as to obtain node identification data; and compressing the statistical data, and then embedding the compressed statistical data together with the node identification data into the source data, so as to obtain compressed storage data. In the present invention, clustering processing is performed on data of pictures, and the same adjacent data appearing repeatedly is replaced with data in a set format, such that the size of the data is greatly compressed; and no external storage is required for a miniature embedded system, which saves on costs and also reduces the size of a device.

Description

Lossless compression algorithm, device, computer equipment and storage medium based on embedded system images Technical field

The present application relates to the technical field of data compression algorithms, and in particular to a lossless compression algorithm, device, computer equipment and storage medium based on embedded system images.

Background technique

In tiny embedded systems, it is often necessary to store a large amount of data, such as pictures displayed on TFT color screens and OLED screens. If these pictures are stored in the form of original files, they will occupy a large amount of ROM space in the system, and even require the use of additional memory chips. Increases the size and cost of the equipment. In addition, algorithms such as dictionary, Huffman coding, and Lempel-Ziv also consume a large amount of system RAM when decoding, or there is a certain loss of data after compression. For tiny embedded systems, the RAM capacity is generally very small, which is not enough to support the RAM required for decoding the above algorithm; if there is data loss, there will be certain defects when displayed on the display.

Contents of the invention

Embodiments of the present application provide a lossless compression algorithm, device, computer equipment and storage medium based on embedded system pictures, aiming to solve the technical problem that existing pictures occupy a large space for storage in the WeChat embedded system.

In order to achieve the above objectives, the technical solutions proposed in this application are:

In the first aspect, this application provides a lossless compression algorithm based on embedded system images, including the following steps:

Determine the data with the lowest frequency of occurrence in the source data or the data in non-source data, and mark it as node data;

Count the data length of the compressible data in the source data and mark the location to obtain statistical data;

If the node data appears in the source data, insert specific characters after the node data. If the node data is data that is not in the source data, no processing is performed to obtain the node identification data;

The statistical data is compressed and embedded into the source data together with the node identification data to obtain compressed storage data.

Wherein, the step of counting the length and mark position of the compressible data in the source data includes the following steps:

Count the data length where the number of adjacent identical data in the source data is greater than the set value to obtain the data length value;

Position marks are performed on the positions of all compressible data segments to obtain the mark positions.

Wherein, the step of compressing the statistical data and embedding it into the source data together with the node identification data to obtain compressed storage data includes the following steps:

Replace the compressible data with: node data + data length value + one bit of source data to obtain partially compressed data;

Replace data that is the same as node data with: node data + specific characters to get node replacement data;

The local compressed data and the node replacement data are respectively inserted into corresponding mark positions of the source data.

The step of determining the data with the lowest frequency of occurrence in the source data or the data in the non-source data and marking it as node data also includes the step of performing modulo processing on the compressed image to obtain the source data.

In the second aspect, embodiments of the present application provide a lossless compression device based on embedded system pictures, which includes the following units:

The node data determination unit is used to determine the data with the lowest frequency of occurrence in the source data or the data in non-source data, and mark it as node data;

Compressed data statistics unit, used to count the data length and mark position of compressible data in source data;

The node data processing unit is used to insert specific characters after the node data if the node data appears in the source data. If the node data is not data in the source data, no processing is performed to obtain the node identifier. data;

A source data compression unit is configured to compress the statistical data and embed it into the source data together with the node identification data to obtain compressed storage data.

Wherein, the compressed data statistics unit includes:

The data length statistics unit is used to count data lengths where the number of adjacent identical data in the source data is greater than the set value to obtain the data length value;

The position marking unit is used to mark the positions of all compressible data segments to obtain the mark positions.

Wherein, the source data compression unit includes:

The compressed data replacement unit is used to replace compressible data with: node data + data length value + one bit of source data to obtain partially compressed data;

The node data replacement unit is used to replace data that is the same as node data with: node data + specific characters to obtain node replacement data;

A data embedding unit is configured to insert the local compressed data and the node replacement data into corresponding mark positions of the source data respectively.

The method further includes: a modulo unit, which is used to perform modulo processing on the compressed image to obtain source data.

In a third aspect, embodiments of the present application provide a computer device. The computer device includes a memory and a processor. A computer program is stored on the memory. When the processor executes the computer program, the above is implemented. Lossless compression algorithm based on embedded system images.

In the fourth aspect, embodiments of the present application provide a computer-readable storage medium that stores a computer program. When the computer program is executed by a processor, the computer program can realize the above-mentioned lossless processing based on embedded system pictures. Compression algorithm.

Compared with the existing technology, embodiments of the present application provide a lossless compression algorithm, device, computer equipment and storage medium based on embedded system pictures. The method includes: determining the data with the lowest frequency of occurrence in the source data or the non-lossless compression algorithm. Mark the data in the source data as node data; count the data length of the compressible data in the source data and mark the position to obtain statistical data; if the node data appears in the source data, insert it after the node data Specific characters, if the node data is non-source data, no processing is performed to obtain node identification data; the statistical data is compressed and embedded into the source data together with the node identification data to obtain compression Later stored data. By clustering the image data, the same data that appears multiple times adjacent to each other is replaced with data in a set format, which greatly compresses the size of the data. For micro-embedded systems, no external storage is required, which not only saves costs but also Reduced equipment size.

Description of the drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, which are of great significance to this field. Ordinary technicians can also obtain other drawings based on these drawings without exerting creative work.

Figure 1 is the main flow chart of the lossless compression algorithm based on embedded system pictures provided by the embodiment of the present application;

Figure 2 is a sub-flow chart of a lossless compression algorithm based on embedded system images provided by an embodiment of the present application;

Figure 3 is a sub-flow chart of the lossless compression algorithm based on embedded system pictures provided by the embodiment of the present application;

Figure 4 is a schematic diagram of a node matching device of a server provided by an embodiment of the present application; and

Figure 5 is a schematic block diagram of a computer device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

It should be understood that, when used in this specification and the appended claims, the terms "comprises" and "comprises" indicate the presence of described features, integers, steps, operations, elements and/or components but do not exclude the presence of one or The presence or addition of multiple other features, integers, steps, operations, elements, components and/or collections thereof.

It should also be understood that the terminology used in the specification of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly dictates otherwise.

It will be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. .

Please refer to the accompanying drawings 1 to 3. Figure 1 is the main flow chart of the lossless compression algorithm based on embedded system pictures provided by the embodiment of the present application. Figures 2 and 3 are schematic sub-flow diagrams. The embedded system-based lossless compression algorithm of the embodiment of the present application is The lossless compression algorithm for system images is mainly used in image storage of micro-embedded systems and display products driven by micro-embedded systems. Specifically, implementing the lossless compression algorithm based on embedded system images includes the following steps:

Step S100: Perform modulo processing on the compressed image to obtain source data. In the embedded microcontroller system, the storage of pictures is generally processed by modulo software to convert the pictures into ordinary data for storage. Modulo processing is to convert the pixels of the picture into corresponding binary data, in the form of data. storage. For example, TFT color screens generally use RGB565 or RGB888. Each pixel of RGB565 occupies 2 bytes (16 bit), and each pixel of RGB888 occupies 3 bytes (24 bit). In the absence of compression, a complete picture, assuming a width of 80 pixels and a length of 160 pixels, will occupy 80*160*2 byte (i.e. 25K byte) or 80*160*3 byte (37.5 K byte), just one picture consumes a lot of ROM space.

It should be noted that for solid-color images or images with only two colors, there is no need to perform the following compression algorithm. If the picture is monochrome, it can be represented by 0 or 1, and no ROM space is required for storage. If the picture has only two colors, use 0 to represent one color and 1 to represent the other color. At this time, one pixel only needs 1 bit to represent, that is, the data that originally required 16 bits to represent now only needs 1 bit, and the compression rate is (16-1)/16≈93%. If there are more than two colors in the picture, continue with the following algorithm processing:

Step S200: Determine the data with the lowest frequency of occurrence or the data that does not appear in the source data, and mark it as node data; after the image is subjected to modulo processing, it exists in the form of binary encoding. If the binary encoding of the same pixel is the same, select it. The data with the lowest frequency of occurrence is used as node data. Generally speaking, the data with the lowest frequency of occurrence does not need to be compressed and can be kept as is. Therefore, the data with the lowest frequency of occurrence is used as node data. This node data is used for subsequent compression algorithm processing. node identifier. In other embodiments, since the function of the node data is to serve as a node identifier, other data that does not appear in the source data can also be used. In this case, during subsequent compression processing, the node identifier needs to be distinguished and an identifier bit added. Will slightly increase the size of the compressed data. Among them, the node data is marked as SKP, and data that does not appear in the source data is given priority. Only one SKP is needed. On the one hand, when decoding and searching for SKP, the number of searches is as small as possible; on the other hand, it prevents SKP from being used in SKP. It appears too many times in the source data. Inserting specific characters after SKP will increase the number of source data.

Step S300: Count the data length of the compressible data in the source data and mark the position to obtain statistical data; that is, determine which data segments need to be compressed according to the compression rules, and mark the position of the compressed data segment in the source data so that Subsequently, the compressed data is embedded in the corresponding location. Through the screening in this step, the same data is prevented from being too small, which will increase the source data during subsequent data compression and replacement, and the purpose of compression cannot be achieved.

Specifically, please refer to Figure 2 again. The step S300 of counting the length and mark position of the compressible data in the source data includes the following steps:

Step S301: Count the number of adjacent identical data in the source data that is greater than the data length of the set value to obtain the data length value; for example, if the set value is 5, then count the number of adjacent identical data in the source data greater than or equal to 5 data segments, that is, data segments that meet the above conditions, need to be compressed. The above setting value can be determined according to the complexity of the source data. For source data with lower complexity, choose a smaller setting value, and vice versa, choose a larger setting value.

Step S302: Position mark the positions of all compressible data segments to obtain the mark positions. The purpose of marking the location of the statistically compressed data segment is to make it easier to find the corresponding location for subsequent replacement of compressed data, and to quickly restore the compressed data to its original location during decoding.

Step S400: If the node data is the same as the source data, then if the node data appears in the source data, insert a specific character after the node data. If the node data is not data in the source data, no operation will be performed. Any processing to get node identification data. Since the node data is part of the source data, it needs to be identified during the compression and pressurization processes. The identification also exists in the data in the source data, not just the node identification. If the node identifier uses data that is not the same as the source data, then the node data bits will be replaced with the corresponding source data during compression and decoding.

Step S500: Compress the statistical data and embed it into the source data together with the node identification data to obtain compressed storage data.

The step S500 of compressing the statistical data and embedding it into the source data together with the node identification data to obtain compressed storage data includes the following steps:

Step S501: Replace the compressible data with: node data + data length value + one bit of source data to obtain partially compressed data; wherein, the node data bit identifies that subsequent data is compressed, and the data length value is described in detail Determines the data length of the compressed data, and one source data bit describes the original data type of the compressed segment, such as red. After processing through this step, adjacent identical data segments can be simplified into three-digit data combinations, greatly reducing the amount of data.

Step S502: Replace data that is the same as the node data with: node data + specific characters to obtain node replacement data; if the node data is the same data as the source data, then the specific characters after the node data can describe the existence of the source data. Data segments that are the same as node data need to be supplemented accordingly when unlocking. If the node data is not part of the source data, then the node data is directly replaced with the same data as the subsequent corresponding one-bit source data.

Step S503: Insert the local compressed data and the node replacement data into corresponding mark positions of the source data respectively. The uncompressed data segments are still retained in the original order. After replacing the compressed data segments according to the above rules, the storage data can be generated again.

Referring to Figure 4, an embodiment of the present application provides a lossless compression device 100 based on embedded system pictures, which includes the following units:

The modulus unit 101 is used to perform modulo processing on the compressed image to obtain source data.

The node data determining unit 102 is used to determine the data with the lowest frequency of occurrence in the source data or the data in the non-source data, and mark it as node data;

The compressed data statistics unit 103 is used to count the data length and mark position of the compressible data in the source data;

Wherein, the compressed data statistics unit 103 includes:

The data length statistics unit 1031 is used to count the data length in the source data where the number of adjacent identical data is greater than the set value to obtain the data length value;

The position marking unit 1032 is used to mark the positions of all compressible data segments to obtain the mark positions.

The node data processing unit 104 is used to insert specific characters after the node data if the node data appears in the source data. If the node data is not data in the source data, no processing is performed to obtain the node. identification data;

The source data compression unit 105 is configured to compress the statistical data and embed it into the source data together with the node identification data to obtain compressed storage data.

Wherein, the source data compression unit 105 includes:

The compressed data replacement unit 1051 is used to replace compressible data with: node data + data length value + one bit of source data to obtain partially compressed data;

The node data replacement unit 1052 is used to replace data that is the same as the node data with: node data + specific characters to obtain node replacement data;

The data embedding unit 1053 is configured to insert the local compressed data and the node replacement data into corresponding mark positions of the source data respectively.

Please refer to Figure 5. An embodiment of the present application provides a computer device. The computer device includes a memory and a processor. A computer program is stored on the memory. When the processor executes the computer program, the above-mentioned steps are implemented. method. The program instructions include:

Step S100: Perform modulo processing on the compressed image to obtain source data.

Step S200: Determine the data with the lowest frequency of occurrence or the data that does not appear in the source data, and mark it as node data.

Step S300: Count the data length of the compressible data in the source data and mark the position to obtain statistical data.

Step S400: If the node data is the same as the source data, then if the node data appears in the source data, insert a specific character after the node data. If the node data is not data in the source data, no operation will be performed. Any processing to get node identification data.

Step S500: Compress the statistical data and embed it into the source data together with the node identification data to obtain compressed storage data.

The computer device may be a terminal or a server, where the terminal may be an electronic device with communication functions such as a smartphone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, a wearable device, etc. The server can be an independent server or a server cluster composed of multiple servers.

The computer device 500 includes a processor 502, a memory, and a network interface 505 connected through a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, can cause the processor 502 to execute a lossless compression algorithm based on embedded system images.

The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, it can cause the processor 502 to execute a lossless compression algorithm based on embedded system images. .

The network interface 505 is used for network communication with other devices. Those skilled in the art can understand that the structure shown in Figure 4 is only a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than shown, some combinations of components, or a different arrangement of components.

Embodiments of the present application also provide a storage medium that stores a computer program. The computer program includes program instructions. When executed by a processor, the program instructions can implement the above method. The program instructions include the following steps:

Step S100: Perform modulo processing on the compressed image to obtain source data. In the embedded microcontroller system, the storage of pictures is generally processed by modulo software to convert the pictures into ordinary data for storage. Modulo processing is to convert the pixels of the picture into corresponding binary data, in the form of data. storage. For example, TFT color screens generally use RGB565 or RGB888. Each pixel of RGB565 occupies 2 bytes (16 bit), and each pixel of RGB888 occupies 3 bytes (24 bit). In the absence of compression, a complete picture, assuming a width of 80 pixels and a length of 160 pixels, will occupy 80*160*2 byte (i.e. 25K byte) or 80*160*3 byte (37.5 K byte), just one picture consumes a lot of ROM space.

It should be noted that for excellent pictures or pictures with only two colors, there is no need to perform the following compression algorithm. If the picture is monochrome, it can be represented by 0 or 1, and no ROM space is required for storage. If the picture has only two colors, use 0 to represent one color and 1 to represent the other color. At this time, one pixel only needs 1 bit to represent, that is, the data that originally required 16 bits to represent now only needs 1 bit, and the compression rate is (16-1)/16≈93%. If there are more than two colors in the picture, continue with the following algorithm processing:

Step S200: Determine the data with the lowest frequency of occurrence or the data that does not appear in the source data, and mark it as node data; after the image is subjected to modulo processing, it exists in the form of binary encoding. If the binary encoding of the same pixel is the same, select it. The data with the lowest frequency of occurrence is used as node data. Generally speaking, the data with the lowest frequency of occurrence does not need to be compressed and can be kept as is. Therefore, the data with the lowest frequency of occurrence is used as node data. This node data is used for subsequent compression algorithm processing. node identifier. In other embodiments, since the function of the node data is to serve as a node identifier, other data that does not appear in the source data can also be used. In this case, during subsequent compression processing, the node identifier needs to be distinguished and an identifier bit added. Will slightly increase the size of the compressed data. Among them, the node data is marked as SKP, and data that does not appear in the source data is given priority. Only one SKP is needed. On the one hand, when decoding and searching for SKP, the number of searches is as small as possible. On the other hand, it prevents SKP from being used in the source data. There are too many occurrences in the data. Inserting specific characters after SKP will increase the source data.

Step S300: Count the data length of the compressible data in the source data and mark the position to obtain statistical data; that is, determine which data segments need to be compressed according to the compression rules, and mark the position of the compressed data segment in the source data so that Subsequently, the compressed data is embedded in the corresponding location. Through the screening in this step, the same data is prevented from being too small, which will increase the source data during subsequent data compression and replacement, and the purpose of compression cannot be achieved.

Specifically, please refer to Figure 2 again. The step S300 of counting the length and mark position of the compressible data in the source data includes the following steps:

Step S301: Count the number of adjacent identical data in the source data that is greater than the data length of the set value to obtain the data length value; for example, if the set value is 5, then count the number of adjacent identical data in the source data greater than or equal to 5 data segments, that is, data segments that meet the above conditions, need to be compressed. The above setting value can be determined according to the complexity of the source data. For source data with lower complexity, choose a smaller setting value, and vice versa, choose a larger setting value.

Step S302: Position mark the positions of all compressible data segments to obtain the mark positions. The purpose of marking the location of the statistically compressed data segment is to make it easier to find the corresponding location for subsequent replacement of compressed data, and to quickly restore the compressed data to its original location during decoding.

Step S400: If the node data is the same as the source data, then if the node data appears in the source data, insert a specific character after the node data. If the node data is not data in the source data, no operation will be performed. Any processing to obtain the node identification data; since the node data is part of the source data, it needs to be identified during the compression and pressurization process. The identification also exists in the data in the source data, rather than simply the node identification. If the node identifier uses data that is not the same as the source data, then the node data bits will be replaced with the corresponding source data during compression and decoding.

Step S500: Compress the statistical data and embed it into the source data together with the node identification data to obtain compressed storage data.

The step S500 of compressing the statistical data and embedding it into the source data together with the node identification data to obtain compressed storage data includes the following steps:

Step S501: Replace the compressible data with: node data + data length value + one bit of source data to obtain partially compressed data; wherein, the node data bit identifies that subsequent data is compressed, and the data length value is described in detail Determines the data length of the compressed data, and one source data bit describes the original data type of the compressed segment, such as red. After processing through this step, adjacent identical data segments can be simplified into three-digit data combinations, greatly reducing the amount of data.

Step S502: Replace data that is the same as the node data with: node data + specific characters to obtain node replacement data; if the node data is the same data as the source data, then the specific characters after the node data can describe the existence of the source data. Data segments that are the same as node data need to be supplemented accordingly when unlocking. If the node data is not part of the source data, then the node data is directly replaced with the same data as the subsequent corresponding one-bit source data.

Step S503: Insert the local compressed data and the node replacement data into corresponding mark positions of the source data respectively. The uncompressed data segments are still retained in the original order. After replacing the compressed data segments according to the above rules, the storage data can be generated again.

Compared with the existing technology, embodiments of the present application provide a lossless compression algorithm, device, computer equipment and storage medium based on embedded system pictures. By clustering the data of the pictures, multiple adjacent images can be processed. The same data is replaced with data in a set format, which greatly compresses the size of the data. For micro-embedded systems, no external storage is needed, which not only saves costs but also reduces the size of the equipment.

The above content is only a preferred embodiment of the present application and is not intended to limit the implementation of the present application. Those of ordinary skill in the art can easily make corresponding modifications or modifications according to the main concept and spirit of the present application. Therefore, the present application The scope of protection applied for shall be subject to the scope of protection required by the claims.

Claims (20)

A lossless compression algorithm based on embedded system images, which includes the following steps: Determine the data with the lowest frequency of occurrence in the source data or the data in non-source data, and mark it as node data; Count the data length of the compressible data in the source data and mark the location to obtain statistical data; If the node data appears in the source data, insert specific characters after the node data. If the node data is data that is not in the source data, no processing is performed to obtain the node identification data; The statistical data is compressed and embedded into the source data together with the node identification data to obtain compressed storage data. The lossless compression algorithm based on embedded system pictures as claimed in claim 1, wherein the step of counting the length and mark position of the compressible data in the source data to obtain the statistical data includes the following steps: Count the data length where the number of adjacent identical data in the source data is greater than the set value to obtain the data length value; Position marks are performed on the positions of all compressible data segments to obtain the mark positions. The lossless compression algorithm based on embedded system pictures according to claim 1, wherein the step of compressing the statistical data and embedding it into the source data together with the node identification data to obtain compressed storage data Includes the following steps: Replace the compressible data with: node data + data length value + one bit of source data to obtain partially compressed data; Replace data that is the same as node data with: node data + specific characters to get node replacement data; The local compressed data and the node replacement data are respectively inserted into corresponding mark positions of the source data. The lossless compression algorithm based on embedded system pictures according to claim 1, wherein the step of determining the data with the lowest frequency of occurrence in the source data or the data in the non-source data and marking it as node data also includes: The compressed image is subjected to modulo processing to obtain the source data. The lossless compression algorithm based on embedded system pictures according to claim 2, wherein the step of determining the data with the lowest frequency of occurrence in the source data or the data in the non-source data and marking it as node data also includes: The compressed image is subjected to modulo processing to obtain the source data. A lossless compression device based on embedded system pictures, which includes the following units: The node data determination unit is used to determine the data with the lowest frequency of occurrence in the source data or the data in non-source data, and mark it as node data; Compressed data statistics unit, used to count the data length and mark position of compressible data in source data; The node data processing unit is used to insert specific characters after the node data if the node data appears in the source data. If the node data is not data in the source data, no processing is performed to obtain the node identifier. data; A source data compression unit is configured to compress the statistical data and embed it into the source data together with the node identification data to obtain compressed storage data. The lossless compression device based on embedded system pictures as claimed in claim 6, wherein the compressed data statistics unit includes: The data length statistics unit is used to count data lengths where the number of adjacent identical data in the source data is greater than the set value to obtain the data length value; The position marking unit is used to mark the positions of all compressible data segments to obtain the mark positions. The lossless compression device based on embedded system pictures according to claim 6, wherein the source data compression unit includes: The compressed data replacement unit is used to replace compressible data with: node data + data length value + one bit of source data to obtain partially compressed data; The node data replacement unit is used to replace data that is the same as node data with: node data + specific characters to obtain node replacement data; A data embedding unit is configured to insert the local compressed data and the node replacement data into corresponding mark positions of the source data respectively. The lossless compression device based on embedded system pictures according to claim 6, further comprising: a modulo taking unit, the modulo taking unit is used to perform modulo processing on the compressed pictures to obtain source data. The lossless compression device based on embedded system pictures according to claim 7, further comprising: a modulo taking unit, the modulo taking unit is used to perform modulo processing on the compressed pictures to obtain source data. The lossless compression device based on embedded system pictures according to claim 8, further comprising: a modulo taking unit, the modulo taking unit is used to perform modulo processing on the compressed pictures to obtain source data. A computer device, wherein the computer device includes a memory and a processor, a computer program is stored on the memory, and the following steps are implemented when the processor executes the computer program: Determine the data with the lowest frequency of occurrence in the source data or the data in non-source data, and mark it as node data; Count the data length of the compressible data in the source data and mark the location to obtain statistical data; If the node data appears in the source data, insert specific characters after the node data. If the node data is data that is not in the source data, no processing is performed to obtain the node identification data; The statistical data is compressed and embedded into the source data together with the node identification data to obtain compressed storage data. The computer device according to claim 12, wherein the step of counting the length and mark position of the compressible data in the source data to obtain the statistical data includes the following steps: Count the data length where the number of adjacent identical data in the source data is greater than the set value to obtain the data length value; Position marks are performed on the positions of all compressible data segments to obtain the mark positions. The computer device according to claim 12, wherein the step of embedding the compressed statistical data into the source data together with the node identification data to obtain compressed storage data includes the following steps: Replace the compressible data with: node data + data length value + one bit of source data to obtain partially compressed data; Replace data that is the same as node data with: node data + specific characters to get node replacement data; The local compressed data and the node replacement data are respectively inserted into corresponding mark positions of the source data. The computer device according to claim 12, wherein the step of determining the data with the lowest frequency of occurrence in the source data or the data in the non-source data and marking it as node data further includes taking a modulo of the compressed picture. Steps to process to obtain source data. The computer device according to claim 13, wherein the step of determining the data with the lowest frequency of occurrence in the source data or the data in the non-source data and marking it as node data further includes taking a modulus of the compressed picture. Steps to process to obtain source data. A computer-readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a processor, the following steps can be implemented: Determine the data with the lowest frequency of occurrence in the source data or the data in non-source data, and mark it as node data; Count the data length of the compressible data in the source data and mark the location to obtain statistical data; If the node data appears in the source data, insert specific characters after the node data. If the node data is data that is not in the source data, no processing is performed to obtain the node identification data; The statistical data is compressed and embedded into the source data together with the node identification data to obtain compressed storage data. The computer-readable storage medium according to claim 17, wherein the step of counting the length and mark position of the compressible data in the source data to obtain the statistical data includes the following steps: Count the data length where the number of adjacent identical data in the source data is greater than the set value to obtain the data length value; Position marks are performed on the positions of all compressible data segments to obtain the mark positions. The computer-readable storage medium according to claim 17, wherein the step of compressing the statistical data and embedding it into source data together with the node identification data to obtain compressed storage data includes the following steps: Replace the compressible data with: node data + data length value + one bit of source data to obtain partially compressed data; Replace data that is the same as node data with: node data + specific characters to get node replacement data; The local compressed data and the node replacement data are respectively inserted into corresponding mark positions of the source data. The computer-readable storage medium according to claim 17, wherein the step of determining the data with the lowest frequency of occurrence in the source data or the data in the non-source data and marking it as node data further includes: Perform modulo processing to obtain source data.

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