Hierarchical storage compression method and device, electronic equipment and storage medium

Abstract

The invention discloses a hierarchical storage compression method and device, electronic equipment and a storage medium, and the method comprises the steps: detecting one or more ROI regions of an original image, and carrying out the extraction to obtain one or more corresponding ROI region images; and carrying out hierarchical coding, storage and decoding on the ROI region and the original image. Aiming at the coding limitation of the JPEG format, by fully utilizing the metadata storage capability in the JPEG file format, differential storage and on-demand display of the ROI of the image are realized on the premise of keeping complete compatibility with the existing JPEG format. According to the scheme, the display quality of an important area can still be guaranteed while the file size is greatly reduced, the basic image content can also be displayed even in a traditional image viewer which does not support analysis of additional metadata, and the maximum compatibility is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of image compression coding, and in particular to a layered storage compression method, device, electronic equipment and storage medium. Background Art

[0002] With the popularity of high-resolution images, the pressure on the storage and transmission bandwidth of image files is increasing. As one of the most widely used image compression formats, JPEG has obvious technical limitations: it can only use a single global quality factor to control the compression quality of the entire image, and cannot adopt differentiated compression strategies for different areas of the image. This results in either overall high-quality compression resulting in a larger file size, or low-quality compression resulting in loss of details in important areas. In practical applications, different areas of an image often have different levels of importance, and users have different quality requirements for these areas. Existing technologies cannot solve this contradiction well. Summary of the invention

[0003] Since the existing methods have the above problems, the embodiments of the present invention provide a hierarchical storage compression method, device, electronic device and storage medium.

[0004] Specifically, the embodiment of the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides a hierarchical storage compression method, comprising:

[0006] One or more ROI regions of the original image are detected, and one or more corresponding ROI region images are extracted.

[0007] Layered coding: compressing and coding the ROI region with a first quality parameter to obtain ROI region image data, and compressing and coding the original image with a second quality parameter to obtain main image data.

[0008] Wherein, the first quality parameter is greater than the second quality parameter.

[0009] Hierarchical storage: storing the ROI area image data in a custom extension field of a JPEG file.

[0010] A layered decoding strategy is executed to decode the ROI region image data and / or the main image data as required.

[0011] In a second aspect, an embodiment of the present invention provides a hierarchical storage compression device, including:

[0012] The detection module is used to detect one or more ROI regions of the original image and extract one or more corresponding ROI region images.

[0013] The encoding module is used for hierarchical encoding, compressing and encoding the ROI region with a first quality parameter to obtain ROI region image data, and compressing and encoding the original image with a second quality parameter to obtain main image data.

[0014] Wherein, the first quality parameter is greater than the second quality parameter.

[0015] The storage module is used for hierarchical storage and stores the ROI area image data in a custom extension field of a JPEG file.

[0016] The decoding module is used to execute a layered decoding strategy to decode the ROI area image data and / or the main image data as needed.

[0017] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the hierarchical storage compression method as described in the first aspect is implemented.

[0018] In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the hierarchical storage compression method as described in the first aspect.

[0019] It can be seen from the above technical solution that the present invention has the following benefits: The present invention aims at the encoding limitations of the JPEG format, and by making full use of the metadata storage capability in the JPEG file format, the present invention realizes the differentiated storage and on-demand display of the important regions (ROI) of the image while maintaining full compatibility with the existing JPEG format. This solution can ensure the display quality of the important regions while greatly reducing the file size. In particular, even in traditional image viewers that do not support parsing additional metadata, the basic image content can be displayed, ensuring maximum compatibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 The figure is a flowchart of a hierarchical storage compression method provided by an embodiment of the present invention.

[0022] Figure 2It is a structural diagram of a hierarchical storage compression device provided by an embodiment of the present invention.

[0023] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0025] Figure 1 FIG. 1 is a flowchart of a hierarchical storage compression method provided by an embodiment of the present invention. Figure 1 As shown, the hierarchical storage compression method provided by the embodiment of the present invention specifically includes the following contents:

[0026] Step 1: detect one or more ROI regions of the original image, and extract one or more corresponding ROI region images.

[0027] ROI (Region of Interest) is the area of ​​interest that the human eye focuses on in the image. In this step, it should be noted that the specific content of the ROI area is pre-set according to actual needs. For example, the ROI area can be a face, a vehicle, a moving object, a building, important object details, etc.

[0028] In a possible implementation, the ROI region detection method may be an edge-based detection method, which uses a sudden change in pixel grayscale values ​​in an image to detect the edge of the target and then determine the ROI region. Common edge detection algorithms include the Sobel algorithm and the Canny algorithm.

[0029] In another possible implementation, the ROI area detection method can be a threshold-based detection method, which sets one or more thresholds according to the grayscale value or other characteristic value of the image pixel, and then compares the grayscale value of the image pixel with the threshold. The area with a pixel value higher or lower than the threshold is determined to be the ROI area, and the rest is the background.

[0030] In another possible implementation, the ROI region detection method can be a deep learning-based detection method, which uses a large amount of sample data containing the ROI region to pre-train the neural network model so that it can fully learn the various features and patterns of the ROI region. After the training is completed, with the help of the powerful feature extraction and recognition capabilities of the neural network model, the ROI region in the input image is accurately identified, its position and range are clarified, and the ROI region is accurately detected.

[0031] In this step, it should be noted that the embodiment of the present invention does not limit the specific method for detecting the ROI region of the original image, and the method can be selected according to the actual situation and the advantages and disadvantages of the specific ROI region detection method.

[0032] Step 2, hierarchical coding, compressing and coding the ROI region with a first quality parameter to obtain ROI region image data, and compressing and coding the original image with a second quality parameter to obtain main image data.

[0033] Wherein, the first quality parameter is greater than the second quality parameter.

[0034] In this step, it should be noted that the quality parameters are a series of indicators used to measure and control the quality of the encoding process. The lower the quality parameter value is set, the smaller the bitstream obtained after compression and the smaller the image size, but the image quality obtained after decompression will also be worse. In the field of visual perception, there is a significant visual law for images. Usually, the focus of the human eye is often focused on the ROI area, that is, the region of interest, which may contain the most critical, representative elements in the image that can convey core information. On the contrary, the human eye does not pay the same attention to non-ROI areas. These areas may be the background part of the image, or some relatively minor elements that are not closely related to the core information. They play a more auxiliary role in the visual cognition process. Although they also exist in the image, they will not attract people's main attention like the ROI area, and are not as important for the acquisition of information.

[0035] Therefore, correspondingly, the ROI area and the original image are hierarchically encoded, and when compressing the ROI, a quality parameter higher than that used when compressing the original image is used. The embodiment of the present invention does not limit the specific quality parameter items and specific quality parameter values. In practical applications, they need to be set according to actual conditions. It is recommended to balance the relationship between the bit rate limit and the image quality when setting.

[0036] Furthermore, in a possible implementation, in order to save more code stream overhead, the original image is blurred to obtain a first image, and then the first image is compressed and encoded with the second quality parameter to obtain main image data.

[0037] The fuzzy processing method includes but is not limited to: mean fuzzy processing method, Gaussian fuzzy processing method, median fuzzy processing method, bilateral fuzzy processing method, etc. The specific processing method can be selected according to the actual situation and is not limited in the embodiment of the present invention.

[0038] Furthermore, in another possible implementation, based on factors such as content importance or visual significance, priorities of different types of ROIs are preset according to actual application scenarios, ROI areas of different priorities are marked with priorities, and compression encoding is performed using differentiated quality parameters.

[0039] Among them, a region with a higher ROI region priority has a higher quality parameter set accordingly, and more bitrate is allocated for encoding.

[0040] Exemplarily, the pre-set priorities of different types of ROIs include: the priority of faces is level 1, the priority of vehicles is level 2, and the priority of moving objects is level 3, and the smaller the number, the higher the priority.

[0041] Step 3: hierarchical storage: storing the ROI area image data in a custom extension field of a JPEG file.

[0042] Preferably, in order to prevent compatibility issues, the ROI area image data can be stored in APP segments in the JPEG file. APP segments are part of the JPEG file structure and are used to store various non-image data information related to the image, such as copyright information, image shooting date, camera model and other metadata. For example, in a JPEG photo taken by a digital camera, the camera's brand, model, aperture, shutter speed and other parameters during shooting may be recorded in a specific location in APPsegments, making it convenient for users to view the shooting background information of the photo. The present invention makes full use of the custom extension field in the JPEG file to store the image data of the ROI area in a hierarchical manner.

[0043] Step 4: Execute a layered decoding strategy to decode the ROI region image data and / or the main image data as needed.

[0044] The layered decoding strategy includes:

[0045] Set the region size threshold T.

[0046] When viewing a picture, if there is no ROI area displayed on the terminal screen, the decoding strategy is: only the main image data is decoded to obtain the main image, and the ROI area is not decoded.

[0047] When viewing a picture, if there is an ROI area displayed on the terminal screen and there is no ROI area whose size is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, and do not decode the ROI area.

[0048] When viewing a picture, if there is an ROI area displayed on the terminal screen and the size of the ROI area is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, decode the ROI area whose size is greater than the threshold T, and do not decode the ROI area whose size is not greater than the threshold T.

[0049] Exemplarily, for example, the original image contains an ROI area, and enlarging the original image causes the ROI area to not be displayed on the terminal screen. At this time, the content displayed on the terminal screen does not contain the ROI area. In this case, only the main image data is decoded to obtain the main image, and the ROI area contained in the original image is not decoded.

[0050] For another example, the original image contains an ROI area and is displayed on the terminal screen, and the size of the displayed ROI area is smaller than a threshold T (for example, 30×30 pixels), then only the main image data is decoded to obtain the main image, and the ROI area contained in the original image is not decoded.

[0051] For another example, the original image contains an ROI area and is displayed on the terminal screen, and the size of the displayed ROI area is greater than a threshold T (for example, 30×30 pixels). In this case, the main image data is decoded to obtain the main image, and the image data of the ROI area stored in the custom extension field of the JPEG file is extracted and decoded.

[0052] For another example, the original image contains three ROI areas. When the image is viewed on a terminal, the size of the first ROI area displayed on the screen is greater than the threshold T (for example, 30×30 pixels), the size of the second ROI area is greater than the threshold T (for example, 30×30 pixels), and the size of the third ROI area is less than the threshold T (for example, 30×30 pixels). In this case, the main image data is decoded to obtain the main image, and the image data of the first ROI area and the image data of the second ROI area stored in the custom extension field of the JPEG file are extracted and decoded, and the third ROI area is not decoded.

[0053] Finally, the decoded ROI area is fused with the decoded main image.

[0054] Furthermore, in order to make the transition between the decoded ROI region and the main image more natural, before the decoded ROI region is fused with the decoded main image, the edge of the decoded ROI region is blurred.

[0055] The fuzzy processing method includes but is not limited to: mean fuzzy processing method, Gaussian fuzzy processing method, median fuzzy processing method, bilateral fuzzy processing method, etc. The specific processing method can be selected according to the actual situation and is not limited in the embodiment of the present invention.

[0056] Figure 2 FIG. 1 shows a flowchart of a hierarchical storage compression device provided by an embodiment of the present invention. Figure 2 As shown, the hierarchical storage compression device provided by the embodiment of the present invention specifically includes the following contents:

[0057] The detection module is used to detect one or more ROI regions of the original image and extract one or more corresponding ROI region images.

[0058] ROI (Region of Interest) is the area of ​​interest that the human eye focuses on in the image. In this step, it should be noted that the specific content of the ROI area is pre-set according to actual needs. For example, the ROI area can be a face, a vehicle, a moving object, a building, important object details, etc.

[0059] In a possible implementation, the ROI region detection method may be an edge-based detection method, which uses a sudden change in pixel grayscale values ​​in an image to detect the edge of the target and then determine the ROI region. Common edge detection algorithms include the Sobel algorithm and the Canny algorithm.

[0060] In another possible implementation, the ROI area detection method can be a threshold-based detection method, which sets one or more thresholds according to the grayscale value or other characteristic value of the image pixel, and then compares the grayscale value of the image pixel with the threshold. The area with a pixel value higher or lower than the threshold is determined to be the ROI area, and the rest is the background.

[0061] In another possible implementation, the ROI region detection method can be a deep learning-based detection method, which uses a large amount of sample data containing the ROI region to pre-train the neural network model so that it can fully learn the various features and patterns of the ROI region. After the training is completed, with the help of the powerful feature extraction and recognition capabilities of the neural network model, the ROI region in the input image is accurately identified, its position and range are clarified, and the ROI region is accurately detected.

[0062] In this step, it should be noted that the embodiment of the present invention does not limit the specific method for detecting the ROI region of the original image, and the method can be selected according to the actual situation and the advantages and disadvantages of the specific ROI region detection method.

[0063] The encoding module is used for hierarchical encoding, compressing and encoding the ROI region with a first quality parameter to obtain ROI region image data, and compressing and encoding the original image with a second quality parameter to obtain main image data.

[0064] Wherein, the first quality parameter is greater than the second quality parameter.

[0065] In this step, it should be noted that the ROI area and the original image are hierarchically encoded, and when the ROI is compressed and encoded, a quality parameter higher than that used when the original image is compressed is used. The embodiment of the present invention does not limit the specific quality parameter items and specific quality parameter values. In practical applications, they need to be set according to actual conditions. It is recommended to balance the relationship between the bit rate limit and the image quality when setting.

[0066] Furthermore, in a possible implementation, in order to save more code stream overhead, the original image is blurred to obtain a first image, and then the first image is compressed and encoded with the second quality parameter to obtain main image data.

[0067] The fuzzy processing method includes but is not limited to: mean fuzzy processing method, Gaussian fuzzy processing method, median fuzzy processing method, bilateral fuzzy processing method, etc. The specific processing method can be selected according to the actual situation and is not limited in the embodiment of the present invention.

[0068] Furthermore, in another possible implementation, based on factors such as content importance or visual significance, priorities of different types of ROIs are preset according to actual application scenarios, ROI areas of different priorities are marked with priorities, and compression encoding is performed using differentiated quality parameters.

[0069] Among them, a region with a higher ROI region priority has a higher quality parameter set accordingly, and more bitrate is allocated for encoding.

[0070] Exemplarily, the pre-set priorities of different types of ROIs include: the priority of faces is level 1, the priority of vehicles is level 2, and the priority of moving objects is level 3, and the smaller the number, the higher the priority.

[0071] The storage module is used for hierarchical storage and stores the ROI area image data in a custom extension field of a JPEG file.

[0072] Preferably, in order to prevent compatibility issues, the ROI area image data can be stored in APP segments in the JPEG file. APP segments are part of the JPEG file structure and are used to store various non-image data information related to the image, such as copyright information, image shooting date, camera model and other metadata. For example, in a JPEG photo taken by a digital camera, the camera's brand, model, aperture, shutter speed and other parameters during shooting may be recorded in a specific location in APPsegments, making it convenient for users to view the shooting background information of the photo. The present invention makes full use of the custom extension field in the JPEG file to store the image data of the ROI area in a hierarchical manner.

[0073] The decoding module is used to execute a layered decoding strategy to decode the ROI area image data and / or the main image data as needed.

[0074] The layered decoding strategy includes:

[0075] Set the region size threshold T.

[0076] When viewing a picture, if there is no ROI area displayed on the terminal screen, the decoding strategy is: only the main image data is decoded to obtain the main image, and the ROI area is not decoded.

[0077] When viewing a picture, if there is an ROI area displayed on the terminal screen and there is no ROI area whose size is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, and do not decode the ROI area.

[0078] When viewing a picture, if there is an ROI area displayed on the terminal screen and the size of the ROI area is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, decode the ROI area whose size is greater than the threshold T, and do not decode the ROI area whose size is not greater than the threshold T.

[0079] Exemplarily, for example, the original image contains an ROI area, and enlarging the original image causes the ROI area to not be displayed on the terminal screen. At this time, the content displayed on the terminal screen does not contain the ROI area. In this case, only the main image data is decoded to obtain the main image, and the ROI area contained in the original image is not decoded.

[0080] For another example, the original image contains an ROI area and is displayed on the terminal screen, and the size of the displayed ROI area is smaller than a threshold T (for example, 30×30 pixels), then only the main image data is decoded to obtain the main image, and the ROI area contained in the original image is not decoded.

[0081] For another example, the original image contains an ROI area and is displayed on the terminal screen, and the size of the displayed ROI area is greater than a threshold T (for example, 30×30 pixels). In this case, the main image data is decoded to obtain the main image, and the image data of the ROI area stored in the custom extension field of the JPEG file is extracted and decoded.

[0082] For another example, the original image contains three ROI areas. When the image is viewed on a terminal, the size of the first ROI area displayed on the screen is greater than the threshold T (for example, 30×30 pixels), the size of the second ROI area is greater than the threshold T (for example, 30×30 pixels), and the size of the third ROI area is less than the threshold T (for example, 30×30 pixels). In this case, the main image data is decoded to obtain the main image, and the image data of the first ROI area and the image data of the second ROI area stored in the custom extension field of the JPEG file are extracted and decoded, and the third ROI area is not decoded.

[0083] Finally, the decoded ROI area is fused with the decoded main image.

[0084] Furthermore, in order to make the transition between the decoded ROI region and the main image more natural, before the decoded ROI region is fused with the decoded main image, the edge of the decoded ROI region is blurred.

[0085] The fuzzy processing method includes but is not limited to: mean fuzzy processing method, Gaussian fuzzy processing method, median fuzzy processing method, bilateral fuzzy processing method, etc. The specific processing method can be selected according to the actual situation and is not limited in the embodiment of the present invention.

[0086] Based on the same inventive concept, another embodiment of the present invention provides an electronic device, such as Figure 3 As shown, the electronic device specifically includes the following contents: a processor 301 , a memory 302 , a communication interface 303 and a communication bus 304 .

[0087] The processor 301 , the memory 302 , and the communication interface 303 communicate with each other via the communication bus 304 ; the communication interface 303 is used to implement information transmission between various devices.

[0088] The processor 301 is used to call the computer program in the memory 302, and the processor implements all steps of the above-mentioned layered storage compression method when executing the computer program.

[0089] Based on the same inventive concept, another embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, all steps of the above-mentioned hierarchical storage compression method are implemented.

[0090] In addition, in the present invention, relational terms such as "first" and "second" are only used to distinguish one specified data or operation from another specified data or operation, and do not necessarily require or imply any actual relationship or order between these data or operations. The term "and / or" in the above embodiments of the present invention describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hierarchical storage compression method, characterized in that: include: Detect one or more ROI regions of the original image, and extract one or more corresponding ROI region images; hierarchical coding, compressing and coding the ROI region with a first quality parameter to obtain ROI region image data, and compressing and coding the original image with a second quality parameter to obtain main image data; wherein the first quality parameter is greater than the second quality parameter; Hierarchical storage, storing the ROI area image data in a custom extension field of a JPEG file; A layered decoding strategy is executed to decode the ROI region image data and / or the main image data as required.

2. The hierarchical storage compression method according to claim 1, characterized in that: After detecting one or more ROI regions of the original image and extracting the corresponding one or more ROI region images, priority marking is performed on different types of ROI regions according to the pre-set priorities of different types of ROIs; the first quality parameter is set to correspond to different quality parameters according to the different priorities of the ROI regions, and the higher the priority of the ROI region, the greater the quality parameter.

3. The hierarchical storage compression method according to claim 1, characterized in that: Before compressing and encoding the original image with the second quality parameter, blurring is performed on the original image.

4. The hierarchical storage compression method according to claim 1, characterized in that: The layered decoding strategy includes: Set the region size threshold T; When viewing an image, if there is no ROI area displayed on the terminal screen, the decoding strategy is: only decode the main image data to obtain the main image, and do not decode the ROI area; When viewing a picture, if there is an ROI area displayed on the terminal screen and there is no ROI area whose size is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, and do not decode the ROI area; When viewing an image, if there is an ROI area displayed on the terminal screen and the size of the ROI area is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, decode the ROI area whose size is greater than the threshold T, and do not decode the ROI area whose size is not greater than the threshold T; Finally, the decoded ROI area is fused with the decoded main image.

5. The hierarchical storage compression method according to claim 4, characterized in that: Before fusing the decoded ROI region with the decoded main image, blurring is performed on the edge of the decoded ROI region.

6. A hierarchical storage compression device, characterized in that: include: A detection module, used to detect one or more ROI regions of the original image, and extract one or more corresponding ROI region images; An encoding module, used for hierarchical encoding, compressing and encoding the ROI region with a first quality parameter to obtain ROI region image data, and compressing and encoding the original image with a second quality parameter to obtain main image data; wherein the first quality parameter is greater than the second quality parameter; A storage module, used for hierarchical storage, storing the ROI area image data in a custom extension field of a JPEG file; The decoding module is used to execute a layered decoding strategy to decode the ROI area image data and / or the main image data as needed.

7. The hierarchical storage compression device according to claim 6, characterized in that: After detecting one or more ROI regions of the original image and extracting the corresponding one or more ROI region images, priority marking is performed on different types of ROI regions according to the pre-set priorities of different types of ROIs; the first quality parameter is set to correspond to different quality parameters according to the different priorities of the ROI regions, and the higher the priority of the ROI region, the greater the quality parameter.

8. The hierarchical storage compression device according to claim 6, characterized in that: Before compressing and encoding the original image with the second quality parameter, blurring is performed on the original image.

9. The hierarchical storage compression device according to claim 6, characterized in that: The layered decoding strategy includes: Set the region size threshold T; When viewing an image, if there is no ROI area displayed on the terminal screen, the decoding strategy is: only decode the main image data to obtain the main image, and do not decode the ROI area; When viewing a picture, if there is an ROI area displayed on the terminal screen and there is no ROI area whose size is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, and do not decode the ROI area; When viewing an image, if there is an ROI area displayed on the terminal screen and the size of the ROI area is greater than the threshold T, the decoding strategy is: decode the main image data to obtain the main image, decode the ROI area whose size is greater than the threshold T, and do not decode the ROI area whose size is not greater than the threshold T; Finally, the decoded ROI area is fused with the decoded main image.

10. The hierarchical storage compression device according to claim 9, characterized in that: Before fusing the decoded ROI region with the decoded main image, blurring is performed on the edge of the decoded ROI region.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the layered storage compression method according to any one of claims 1 to 5 is implemented.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the hierarchical storage compression method according to any one of claims 1 to 5 is implemented.

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