Lossless correlation encoding methods, systems, devices, and media

Abstract

The application provides a lossless correlation coding method, system, device and medium based on eigenmode decomposition for cloud desktop, the adjacent frames in the image display channel under the cloud desktop scene have high correlation, by using the scene characteristics, combining the macro block sequence multi-dimensional frequency and other characteristics constructed by the eigenmode decomposition method, the correlation information between the images can be greatly used to improve the compression rate, the data transmission amount is effectively reduced, and the user experience under the low bandwidth environment is improved. The application uses the cloud desktop scene characteristics, combines the macro block sequence multi-dimensional frequency and other characteristics constructed by the eigenmode decomposition method, the correlation information between the images can be greatly used to improve the compression rate, the data transmission amount is effectively reduced, and the user experience under the weak network environment is improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a lossless correlation coding method, system, device and medium based on intrinsic mode decomposition for cloud desktops. Background Technology

[0002] Common use cases of cloud desktops: The use of cloud desktops is inseparable from the display interface images. Regular page scrolling and PPT slide switching scenarios are highly related to the context. In addition, image display depends on the encoder encoding and transmission in the remote server, which has high requirements for the network. This method can reduce the amount of data transmission in low bandwidth environments.

[0003] Independent image compression only operates on a single frame, resulting in poor compression performance and low compression ratio, making it unsuitable for low-bandwidth environments. In contrast, adjacent frames in the image display channel of cloud desktop scenarios are highly correlated, and current mainstream compression coding does not fully utilize the macroblock frequency feature information in image coding, making it difficult to efficiently associate the same lossless macroblocks.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may contain information that is not part of the prior art known to those skilled in the art. Summary of the Invention

[0005] This application provides a lossless correlation coding method, system, device, and medium based on intrinsic mode decomposition for cloud desktops, in order to solve the problems existing in the prior art.

[0006] Firstly, this application provides a lossless correlation coding method based on intrinsic mode decomposition for cloud desktops, including:

[0007] S1. Obtain an image from the cloud desktop display channel as a data source, divide the image into macroblocks of a preset size according to the preset size, and then calculate and obtain the grayscale value of each macroblock.

[0008] S2. Based on the gray values ​​of each macroblock, multi-order IMF components are obtained by decomposing them using the complete empirical mode decomposition method CEEMDAN, and the first feature is obtained based on the multi-order IMF components.

[0009] S3. Obtain the second feature based on the sum of the gray values ​​of each macroblock;

[0010] S4. Combine the first feature with the second feature to obtain macroblock features;

[0011] S5. Determine whether the macroblock feature exists in the cache. If it exists, send the information corresponding to the macroblock feature in the cache to the client. If it does not exist, cache and send the information corresponding to the macroblock feature to the client.

[0012] In some embodiments, S2, the first feature is obtained by the following formula:

[0013]

[0014] Where f1 represents the first feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and IMF1 and IMF2 represent high-frequency components.

[0015] In some embodiments, in S3, the second feature is obtained by the following formula:

[0016]

[0017] Where f2 represents the second feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and gray represents the second feature. m This represents the grayscale value.

[0018] In some embodiments, S4, macroblock features are obtained using the following formula:

[0019] f = (f1 << 18) + f2

[0020] Where f represents macroblock feature, f1 represents first feature, and f2 represents second feature.

[0021] Secondly, this application provides a lossless correlation coding system based on intrinsic mode decomposition for cloud desktops, comprising:

[0022] The acquisition module is used to acquire images from the cloud desktop display channel as a data source, divide the images into macroblocks of a preset size according to a preset size, and then calculate the grayscale value of each macroblock.

[0023] The decomposition module is used to decompose each macroblock according to its gray value using the complete empirical mode decomposition method CEEMDAN to obtain multi-order IMF components, and to obtain the first feature based on the multi-order IMF components.

[0024] The processing module is used to obtain a second feature based on the sum of the grayscale values ​​of each macroblock; combine the first feature and the second feature to obtain a macroblock feature; determine whether the macroblock feature exists in the cache; if it exists, send the information corresponding to the macroblock feature in the cache to the client; if it does not exist, cache and send the information corresponding to the macroblock feature to the client.

[0025] In some embodiments, the decomposition module is used to obtain the first feature using the following formula:

[0026]

[0027] Where f1 represents the first feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and IMF1 and IMF2 represent high-frequency components.

[0028] In some embodiments, the processing module is used to obtain the second feature using the following formula:

[0029]

[0030] Where f2 represents the second feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and gray represents the second feature. m This represents the grayscale value.

[0031] In some embodiments, the processing module is used to obtain macroblock features using the following formula:

[0032] f = (f1 << 18) + f2

[0033] Where f represents macroblock feature, f1 represents first feature, and f2 represents second feature.

[0034] Thirdly, this application provides a terminal device, including a memory and a processor:

[0035] A memory is used to store computer programs; a processor is used to read the computer programs from the memory and execute the aforementioned lossless association coding method based on intrinsic mode decomposition for cloud desktops.

[0036] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the aforementioned lossless association coding method for cloud desktops based on intrinsic mode decomposition.

[0037] This application provides a lossless correlation coding method, system, device, and medium based on intrinsic mode decomposition (IMD) for cloud desktops. In cloud desktop scenarios, adjacent frames in the image display channel are highly correlated. Utilizing this scenario characteristic, and combining the multi-dimensional frequency characteristics of macroblock sequences constructed by the IMD method, the compression ratio can be greatly improved by leveraging the correlation information between images, effectively reducing data transmission volume and enhancing the user experience in low-bandwidth environments. This invention utilizes the characteristics of cloud desktop scenarios, combined with the multi-dimensional frequency characteristics of macroblock sequences constructed by the IMD method, to greatly leverage the correlation information between images, improve the compression ratio, effectively reduce data transmission volume, and enhance the user experience in weak network environments. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] Figure 1 A schematic diagram of a lossless correlation coding method based on intrinsic mode decomposition for cloud desktops provided in an embodiment of this application;

[0040] Figure 2 This is a schematic diagram illustrating the decomposition of multiple-order IMF components using the complete empirical mode decomposition method CEEMDAN.

[0041] Figure 3 A schematic diagram of a lossless correlation coding system based on intrinsic mode decomposition for cloud desktops provided in an embodiment of this application;

[0042] Figure 4 Specific application example diagrams provided for embodiments of this application;

[0043] Figure 5 An example diagram of macroblock feature matching provided for embodiments of this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms "a" and "the" as used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise.

[0047] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" or "several" means two or more, unless otherwise explicitly specified.

[0048] It should be understood that the term "and / or" used in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Furthermore, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0049] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0050] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0051] In recent years, the rapid development of cloud computing technology and its applications has led to a continuous increase in the number of users in the data center industry and the ongoing upgrades of enterprise-level applications. Virtualization, as a key technology of cloud computing, separates physical computing devices into one or more virtual nodes, providing applications with a flexible and easily scalable service platform. As a typical application of the cloud computing era, cloud desktops distribute resources to various terminals in a desktop-like manner, decoupling the terminals from service nodes. Cloud desktops offer advantages such as flexible access, centralized resources, ease of maintenance, high security, and low cost. However, cloud desktop display relies on remote server encoders for encoding and transmission, placing high demands on the network. In low-bandwidth environments, this can lead to display latency and stuttering issues.

[0052] Network environments vary significantly among users. To address image encoding and transmission in low-bandwidth environments during cloud computing usage, effective compression methods are necessary. Based on the recoverability of the compressed image, encoding can be categorized as reversible or irreversible, i.e., lossless and lossy. Lossy encoding offers high compression ratios, but the process is irreversible; the compressed and decoded image differs from the original. This method is particularly problematic for scenarios requiring high image precision. Lossless encoding, on the other hand, can completely restore the original image from the compressed data, eliminating image distortion, but at the cost of a lower compression ratio. In the image domain, lossless compression techniques can be further divided into dictionary-based compression and statistical compression. The former uses a symbol to represent a string of characters, forming a fixed-length source code; the latter uses shorter codes to represent frequently occurring characters and longer codes to represent less frequently occurring characters, thus compressing pixels. It is worth noting that the two lossless encoding techniques mentioned above are for compressing individual images. However, in the use of cloud desktops, there is often a very high correlation between consecutive frames. Taking scrolling down a webpage as an example, assuming the frame rate is 20, the frame image at time t and the frame image at time t-1 are only about 50 milliseconds apart in time. At this time, the content displayed on the user's desktop has only been shifted upwards. The frame image at time t and the frame image at time t-1 are very likely to have a lot of the same content.

[0053] Macroblocks are the basic units in image and video compression, dividing a large image into multiple smaller images, which are called macroblocks. Typically, a macroblock is NxN pixels in size. Dividing an image into multiple macroblocks makes it easier to correlate related information between them. Extracting feature information from macroblocks with limited usable information is particularly crucial. Traditional methods are generally limited to the positional and structural features of macroblocks. However, if the pixel information of the entire macroblock is treated as a sequence, multidimensional frequency information can be extracted. Dr. Huang E, a Chinese-American scientist at NASA, proposed an empirical mode decomposition (EMD) method, a time-frequency analysis method suitable for processing nonlinear and non-stationary signals. It can decompose complex sequences into a series of intrinsic mode components, each with different physical meanings from high frequency to low frequency.

[0054] Based on the above explanation, in cloud desktop scenarios, there is a significant amount of dependency information between image encoding, rather than being limited to direct compression encoding of a single frame. Fully utilizing the correlation between macroblocks between consecutive frames can achieve more substantial image compression encoding.

[0055] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0056] Figure 1 A schematic diagram of a lossless correlation coding method based on intrinsic mode decomposition for cloud desktops provided in this application embodiment is shown below. Figure 1 As shown, this application provides a lossless correlation coding method based on intrinsic mode decomposition for cloud desktops, including:

[0057] S1. Obtain an image from the cloud desktop display channel as a data source, divide the image into macroblocks of a preset size, and then calculate the grayscale value of each macroblock. m (m represents the macroblock index number); for example, an image can be divided into NxN macroblocks (N can be 8, 16, 32, or 64) according to a preset size.

[0058] S2. Based on the gray values ​​of each macroblock, multi-order IMF components are obtained by decomposing them using the complete empirical mode decomposition method CEEMDAN, and the first feature is obtained based on the multi-order IMF components.

[0059] For example, Figure 2 This is a schematic diagram illustrating the decomposition of multiple-order IMF components using the complete empirical mode decomposition method CEEMDAN, as shown below. Figure 2 As shown, this macroblock can be decomposed into a total of 8 IMF components.

[0060] S3. Obtain the second feature based on the sum of the gray values ​​of each macroblock;

[0061] S4. Combine the first feature with the second feature to obtain macroblock features;

[0062] S5. Determine whether the macroblock feature exists in the cache. If it exists, send the information corresponding to the macroblock feature in the cache to the client. If it does not exist, cache and send the information corresponding to the macroblock feature to the client.

[0063] This application provides a lossless correlation coding method, system, device, and medium based on intrinsic mode decomposition (IMD) for cloud desktops. In cloud desktop scenarios, adjacent frames in the image display channel are highly correlated. Utilizing this scenario characteristic, and combining the multi-dimensional frequency characteristics of macroblock sequences constructed by the IMD method, the compression ratio can be greatly improved by leveraging the correlation information between images, effectively reducing data transmission volume and enhancing the user experience in low-bandwidth environments. This invention utilizes the characteristics of cloud desktop scenarios, combined with the multi-dimensional frequency characteristics of macroblock sequences constructed by the IMD method, to greatly leverage the correlation information between images, improve the compression ratio, effectively reduce data transmission volume, and enhance the user experience in weak network environments.

[0064] Unlike other independent image compression methods, adjacent frames in the image display channel of a cloud desktop scenario are highly correlated. Taking advantage of this scenario characteristic and combining the multidimensional frequency and other features of the macroblock sequence constructed by the intrinsic mode decomposition method, this patent application proposes a lossless correlation coding method based on intrinsic mode decomposition for cloud desktops. This method can greatly utilize the correlation information between images to improve the compression ratio, effectively reduce the amount of data transmission, and improve the user experience in low-bandwidth environments.

[0065] Compared with existing technologies, the main advantages of this application are: 1. It flattens the two-dimensional macroblock structure into one dimension, making fuller use of macroblock association data information; 2. The constructed features integrate grayscale and high-order frequency information, reducing macroblock representation conflicts while improving compression rate.

[0066] Compared with existing technologies, the main innovation lies in the fact that the intrinsic mode decomposition method is applied to macroblock features for the first time, constructing multidimensional frequency features of macroblock sequences, and proposing a lossless correlation coding method for cloud desktops, which can efficiently represent macroblocks and improve compression ratio.

[0067] The technical solutions of this application may be applied in the following scenarios: 1. Using cloud computers in low-bandwidth and weak network environments; 2. Cloud desktop scenarios with high requirements for image display accuracy, such as AutoCAD engineering drawing, medical images, remote sensing images, and high-precision image analysis.

[0068] In some embodiments, S2, the first feature is obtained by the following formula:

[0069]

[0070] Where f1 represents the first feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and IMF1 and IMF2 represent high-frequency components.

[0071] In some embodiments, in S3, the second feature is obtained by the following formula:

[0072]

[0073] Where f2 represents the second feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and gray represents the second feature. m This represents the grayscale value.

[0074] In some embodiments, S4, macroblock features are obtained using the following formula:

[0075] f = (f1 << 18) + f2

[0076] Where f represents macroblock feature, f1 represents first feature, and f2 represents second feature.

[0077] Figure 3 A schematic diagram of a lossless correlation coding system based on intrinsic mode decomposition for cloud desktops provided in this application embodiment is shown below. Figure 3 As shown, this application provides a lossless correlation coding system based on intrinsic mode decomposition for cloud desktops, comprising:

[0078] The acquisition module 10 is used to acquire images from the cloud desktop display channel as a data source, divide the images into macroblocks of a preset size according to a preset size, and then calculate the grayscale value of each macroblock.

[0079] The decomposition module 20 is used to decompose each macroblock according to its gray value using the complete empirical mode decomposition method CEEMDAN to obtain multi-order IMF components, and to obtain the first feature based on the multi-order IMF components.

[0080] The processing module 30 is used to obtain a second feature based on the sum of the grayscale values ​​of each macroblock; combine the first feature and the second feature to obtain a macroblock feature; determine whether the macroblock feature exists in the cache; if it exists, send the information corresponding to the macroblock feature in the cache to the client; if it does not exist, cache and send the information corresponding to the macroblock feature to the client.

[0081] In some embodiments, the decomposition module is used to obtain the first feature using the following formula:

[0082]

[0083] Where f1 represents the first feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and IMF1 and IMF2 represent high-frequency components.

[0084] In some embodiments, the processing module is used to obtain the second feature using the following formula:

[0085]

[0086] Where f2 represents the second feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and gray represents the second feature. m This represents the grayscale value.

[0087] In some embodiments, the processing module is used to obtain macroblock features using the following formula:

[0088] f = (f1 << 18) + f2

[0089] Where f represents macroblock feature, f1 represents first feature, and f2 represents second feature.

[0090] The following are specific application examples of this application:

[0091] For example, with Figure 4 Briefly explain that adjacent frames in the image display channel are highly correlated in a cloud desktop scenario. Figure 4 The left and right frames are adjacent frames. Assuming the left frame is frame a and the right frame is frame b, we can see that frame b is generated after frame a is scrolled down. The part within the box contains the same content, so this part can be cached and looked up.

[0092] For example, Figure 5 Example diagram of macroblock feature matching, such as Figure 5 As shown, the technical solution of this application can realize an encoder that uses the macroblock features to achieve buffering between adjacent frames, and it has been verified that it can indeed meet the requirements, such as... Figure 5 Macroblock a can be composed of the lower part of macroblock b in the cache and the upper part of macroblock c.

[0093] In some embodiments, this application provides a terminal device, including a memory and a processor:

[0094] A memory is used to store computer programs; a processor is used to read the computer programs from the memory and execute the aforementioned lossless association coding method based on intrinsic mode decomposition for cloud desktops.

[0095] In some embodiments, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described lossless association coding method for cloud desktops based on intrinsic mode decomposition.

[0096] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0097] In some embodiments, this application provides a terminal device, including a memory and a processor:

[0098] A memory is used to store computer programs; a processor is used to read the computer programs from the memory and execute the aforementioned lossless association coding method based on intrinsic mode decomposition for cloud desktops.

[0099] In some embodiments, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described lossless association coding method for cloud desktops based on intrinsic mode decomposition.

[0100] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0101] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0102] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A lossless correlation coding method based on intrinsic mode decomposition for cloud desktops, characterized in that, include: S1. Obtain an image from the cloud desktop display channel as a data source, divide the image into macroblocks of a preset size according to the preset size, and then calculate and obtain the grayscale value of each macroblock. S2. Based on the gray values ​​of each macroblock, multi-order IMF components are obtained by decomposing them using the complete empirical mode decomposition method CEEMDAN, and the first feature is obtained based on the multi-order IMF components. S3. Obtain the second feature based on the sum of the gray values ​​of each macroblock; S4. Combine the first feature with the second feature to obtain macroblock features; S5. Determine whether the macroblock feature exists in the cache. If it exists, send the information corresponding to the macroblock feature in the cache to the client; if it does not exist, cache and send the information corresponding to the macroblock feature to the client. In S2, the first feature is obtained using the following formula: Where f1 represents the first feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and IMF1 and IMF2 represent high-frequency components.

2. The lossless association coding method for cloud desktops based on intrinsic mode decomposition according to claim 1, characterized in that, In S3, the second feature is obtained using the following formula: Where f2 represents the second feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and gray represents the second feature. m This represents the grayscale value.

3. The lossless association coding method for cloud desktops based on intrinsic mode decomposition as described in claim 2, characterized in that, In S4, macroblock features are obtained using the following formula: f = (f1 << 18) + f2 Where f represents macroblock feature, f1 represents first feature, and f2 represents second feature.

4. A lossless correlation coding system based on intrinsic mode decomposition for cloud desktops, characterized in that, include: The acquisition module is used to acquire images from the cloud desktop display channel as a data source, divide the images into macroblocks of a preset size according to a preset size, and then calculate the grayscale value of each macroblock. The decomposition module is used to decompose each macroblock according to its gray value using the complete empirical mode decomposition method CEEMDAN to obtain multi-order IMF components, and to obtain the first feature based on the multi-order IMF components. The processing module is used to obtain a second feature based on the sum of the gray values ​​of each macroblock; and to combine the first feature with the second feature to obtain a macroblock feature. Determine whether the macroblock feature exists in the cache. If it exists, send the information corresponding to the macroblock feature in the cache to the client; if it does not exist, cache the information corresponding to the macroblock feature and send it to the client. The decomposition module is used to obtain the first feature using the following formula: Where f1 represents the first feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and IMF1 and IMF2 represent high-frequency components.

5. The lossless association coding system for cloud desktops based on intrinsic mode decomposition according to claim 4, characterized in that, The processing module is used to obtain the second feature using the following formula: Where f2 represents the second feature, m represents the macroblock index number, n represents the number of pixels in the macroblock, x represents the pixel index in the macroblock, and gray represents the second feature. m This represents the grayscale value.

6. The lossless association coding system for cloud desktops based on intrinsic mode decomposition according to claim 5, characterized in that, The processing module is used to obtain macroblock features using the following formula: f = (f1 << 18) + f2 Where f represents macroblock feature, f1 represents first feature, and f2 represents second feature.

7. A terminal device, characterized in that, Including memory and processor: A memory for storing computer programs; a processor for reading the computer programs from the memory and executing the lossless association coding method for cloud desktops based on intrinsic mode decomposition as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the lossless association coding method for cloud desktops based on intrinsic mode decomposition as described in any one of claims 1-3.

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