Video image hierarchical storage method, terminal device and storage medium

Abstract

The application relates to a video image hierarchical storage method, a terminal device and a storage medium, and the method comprises the following steps: constructing an associated factor weight information base of a video image; setting a compression rule score and a storage rule score calculation formula of the video image in combination with each associated factor; constructing a compression rule corresponding table of the compression rule score and a storage rule corresponding table of the storage rule score; acquiring an associated factor value corresponding to a to-be-analyzed video image, and then calculating a compression rule score and a storage rule score of the to-be-analyzed video image; searching for a compression rule and a storage rule corresponding to the to-be-analyzed video image in the rule corresponding table; and based on the searched compression rule and storage rule, the to-be-analyzed video image is compressed and stored. The application optimizes the storage strategy of the video image, improves the storage and retrieval efficiency, and increases the security.

Description

Technical Field

[0001] The present invention relates to the field of video image storage, and in particular to a video image hierarchical storage method, terminal equipment and storage medium. Background Art

[0002] As video surveillance systems continue to expand, they often require the storage of vast amounts of video image data. To address this storage challenge, more efficient and reliable storage and management methods are needed. Currently, some cities have begun adopting cloud storage and distributed storage technologies to improve the storage and management efficiency of video surveillance data. Furthermore, some cities are exploring the application of intelligent video analysis technologies to enhance the value of video surveillance data.

[0003] In terms of video image storage, in addition to using large-scale storage processing technologies such as cloud storage, video image compression technology can also be used to effectively compress video images and improve video image storage efficiency. Video image compression methods mainly include distributed compression, deep learning compression, inter-frame compression, and region segmentation compression.

[0004] Common encoder-based compression storage methods include H.264, H.265, and AV1. The compression format affects video clarity, smoothness, and file size. Typically, video compression processing is performed on the camera itself or during access and forwarding of storage and streaming services. Different compression formats have different advantages and disadvantages, and the choice should be based on the specific application scenario. The main methods for video image compression storage include:

[0005] (1) Distributed compression: This method divides video image data into multiple segments and then stores them on different devices or servers. This method can reduce the storage pressure of a single device and facilitate data backup and disaster recovery.

[0006] (2) Compressed sensing: This method uses efficient random sampling technology at the acquisition end to convert the original video image data into a sparse representation, which is then compressed and stored. This method can significantly reduce the storage space of video image data without losing too much detail.

[0007] (3) Inter-frame compression: This method mainly uses the large amount of redundant information between adjacent frames in the video sequence for compression. By using motion estimation and compensation technology for temporally adjacent frames, the redundant information of the video sequence can be greatly reduced, thereby improving storage efficiency.

[0008] (4) Region segmentation compression: This method divides the video image into multiple regions and compresses each region independently. By analyzing and processing the pixel values ​​and texture information of different regions, efficient compression of the video image can be achieved.

[0009] (5) Transcoding compression: This method converts the original video image data into another format or encoding method to achieve data compression. Common transcoding methods include converting video image data from one encoding format to another, or converting video image data from one color space to another. This method can achieve good compression effects, but requires more computing resources and time.

[0010] Current video image compression mainly compresses the characteristics of the video stream itself. However, the storage value of video images actually varies depending on factors such as their installation location, equipment type, and construction age. If stored according to unified rules, the demand for back-end storage resources will be large. Therefore, there is currently a lack of methods to classify and store video images according to their quality, sensitivity, and importance. Summary of the Invention

[0011] In order to solve the above problems, the present invention proposes a video image hierarchical storage method, a terminal device and a storage medium.

[0012] The specific plan is as follows:

[0013] A method for hierarchical storage of video images comprises the following steps:

[0014] S1: Constructing a correlation factor weight information database of the video image, wherein the correlation factor weight information database includes a weight information table of all correlation factors;

[0015] S2: setting the calculation formula of the compression rule score and the storage rule score of the video image in combination with each correlation factor;

[0016] S3: Constructing a compression rule correspondence table for compression rule scores and a storage rule correspondence table for storage rule scores;

[0017] S4: Obtain the correlation factor value corresponding to the video image to be analyzed, and then calculate the compression rule score and storage rule score of the video image to be analyzed;

[0018] S5: searching for a corresponding compression rule from a compression rule corresponding table based on the compression rule score of the video image to be analyzed, and searching for a corresponding storage rule from a storage rule corresponding table based on the storage rule score of the video image to be analyzed;

[0019] S6: Based on the found compression rules and storage rules, compress and store the video image to be analyzed.

[0020] Furthermore, the associated factors of the video image include: point location type, monitoring point type, industry, image resolution, encoding format, equipment type and equipment construction age.

[0021] Furthermore, the calculation formula of the compression rule score is: compression rule score = image importance * (image quality score / 100) * encoding format weight coefficient * (device resolution / 100).

[0022] Furthermore, the calculation formula of the storage rule score is: storage rule score = image importance * (image quality score / 100) * equipment construction age weight coefficient.

[0023] Furthermore, the calculation formula for image importance is: Image importance = point location type weight score * point location type weight coefficient + industry weight score * industry weight coefficient + monitoring point type weight score * monitoring point type weight coefficient.

[0024] Furthermore, the calculation formula for device resolution is: device resolution = image resolution weight score * image resolution weight coefficient + device type weight score * device type weight coefficient.

[0025] Furthermore, it also includes: recording the compression and storage execution results within the set time interval at the time interval, and dynamically learning and updating the compression and storage rules based on the recorded results.

[0026] A video image hierarchical storage terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method described above are implemented.

[0027] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described above in an embodiment of the present invention.

[0028] The present invention adopts the above technical solution to optimize the storage strategy of video images, improve storage and retrieval efficiency, and increase security. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Shown is a flow chart of a method according to a first embodiment of the present invention.

[0030] Figure 2 Shown is a schematic diagram of the video image storage process in this embodiment.

[0031] Figure 3 Shown is a table of weight information of each correlation factor in this embodiment. DETAILED DESCRIPTION

[0032] To further illustrate various embodiments, the present invention provides accompanying drawings. These drawings form part of the present disclosure and are primarily used to illustrate the embodiments and, in conjunction with the relevant description in the specification, to explain the operating principles of the embodiments. By referring to these drawings, those skilled in the art will be able to understand other possible implementations and the advantages of the present invention.

[0033] The present invention will now be further described with reference to the accompanying drawings and specific embodiments.

[0034] Example 1:

[0035] The embodiment of the present invention provides a method for hierarchical storage of video images, such as Figure 1 and Figure 2 As shown, the method includes the following steps:

[0036] S1: Constructing a correlation factor weight information library of the video image, wherein the correlation factor weight information library includes a weight information table of all correlation factors.

[0037] In this embodiment, the associated factors of the video image include: point location type, monitoring point type, industry, image resolution, encoding format, equipment type and equipment construction age, etc. The corresponding weight information table is as follows: Figure 3 As shown, the weight information table corresponding to the point location type, monitoring point type, industry, image resolution and equipment type includes two fields: weight score and weight coefficient. The weight information table corresponding to the coding format and equipment construction year only includes the weight coefficient field.

[0038] S2: Combining various correlation factors, a calculation formula for the compression rule score and the storage rule score of the video image is set.

[0039] In this embodiment, the calculation formula for setting the compression rule score is: Compression rule score = Image importance * (Image quality score / 100) * Coding format weight coefficient * (Device resolution / 100). The calculation formula for the storage rule score is: Storage rule score = Image importance * (Image quality score / 100) * Equipment construction age weight coefficient. Where:

[0040] Image importance is calculated using the following formula: Image importance = point location type weight score * point location type weight coefficient + industry weight score * industry weight coefficient + monitoring point type weight score * monitoring point type weight coefficient.

[0041] Device resolution is calculated using the following formula: Device resolution = Image resolution weight points * Image resolution weight coefficient + Device type weight points * Device type weight coefficient.

[0042] The image quality score is obtained through existing video image quality algorithms, such as peak signal-to-noise ratio (PSNR), mean square error (MSE), structural similarity index (SSIM), etc. The total score is calculated based on 100 points.

[0043] S3: Construct a compression rule correspondence table of compression rule scores and a storage rule correspondence table of storage rule scores.

[0044] The two rule correspondence tables constructed in this embodiment are shown in Table 1 and Table 2, respectively. In the storage rules, different storage rules are used for videos and images. In the two rule correspondence tables, each score interval corresponds to a rule.

[0045] Table 1

[0046] Storage score interval Video Storage Rules Image storage rules [80,100] Video storage for 90 days Image 3 years [60,80) Video storage for 60 days Image 1 year (20，60] Video storage for 30 days Image half a year (0，20] No No

[0047] Table 2

[0048] Compressed score range rule [80,100] No compression [60,80) Compressed Sensing (40，60] Inter-frame compression (20，40] Region segmentation compression (0，20] Transcoding and compression

[0049] S4: Obtain the correlation factor value corresponding to the video image to be analyzed, and then calculate the compression rule score and storage rule score of the video image to be analyzed.

[0050] The value of the correlation factor corresponding to the video image to be analyzed can be obtained from the basic information of the camera device corresponding to the video image to be analyzed when it is registered.

[0051] S5: searching for a corresponding compression rule from a compression rule corresponding table based on the compression rule score of the video image to be analyzed, and searching for a corresponding storage rule from a storage rule corresponding table based on the storage rule score of the video image to be analyzed.

[0052] S6: Based on the found compression rules and storage rules, compress and store the video image to be analyzed.

[0053] Furthermore, this embodiment also includes: recording the compression and storage execution results within a set time interval (monthly, quarterly, or annually), and dynamically learning and updating the compression and storage rules based on the recorded results. The dynamic learning algorithm can use an existing machine learning algorithm and is not limited here. The weight scores and weight coefficients in the correlation factor weight information library constructed in step S1 can also be increased, decreased, or modified according to actual application requirements.

[0054] The embodiments of the present invention have the following beneficial effects:

[0055] (1) Improve storage efficiency: By classifying and storing the attributes of video images (such as importance, quality, real-time, etc.), storage space can be used more effectively and rationally, the utilization rate of storage resources can be improved, and storage efficiency can be improved.

[0056] (2) Improve retrieval efficiency: Images that need to be retrieved quickly can be stored on faster storage devices, while other less frequently used images can be stored on slower storage devices. This can improve retrieval efficiency and reduce retrieval time.

[0057] (3) Optimize storage strategy: By performing multi-factor hierarchical classification storage on video images, different storage strategies such as backup, compression, encryption, etc. can be adopted according to different categories of video images to optimize the use of storage resources.

[0058] (4) Enhanced security: By storing important images on encrypted storage devices, the security of images can be enhanced to prevent unauthorized access and leakage.

[0059] Example 2:

[0060] The present invention also provides a video image hierarchical storage terminal device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiment of the first embodiment of the present invention are implemented.

[0061] Furthermore, as an executable solution, the video image hierarchical storage terminal device can be a computing device such as a desktop computer, a laptop, a PDA, or a cloud server. The video image hierarchical storage terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above-mentioned component structure of the video image hierarchical storage terminal device is merely an example of a video image hierarchical storage terminal device and does not constitute a limitation on the video image hierarchical storage terminal device. The video image hierarchical storage terminal device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the video image hierarchical storage terminal device may also include input and output devices, network access devices, buses, etc., but this is not limited in the embodiments of the present invention.

[0062] Furthermore, as an executable solution, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices. The general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the video image hierarchical storage terminal device, and connects various parts of the entire video image hierarchical storage terminal device using various interfaces and lines.

[0063] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the video image hierarchical storage terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application required for a function; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0064] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method in the embodiment of the present invention are implemented.

[0065] If the module / unit integrated in the video image hierarchical storage terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution medium.

[0066] Although the present invention has been particularly shown and described in conjunction with preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made to the present invention without departing from the spirit and scope of the invention as defined in the appended claims, and all such changes are within the scope of protection of the present invention.

Claims

1. A method for hierarchical storage of video images, characterized in that: The following steps are involved: S1: Constructing a correlation factor weight information database of the video image, wherein the correlation factor weight information database includes a weight information table of all correlation factors; S2: setting the calculation formula of the compression rule score and the storage rule score of the video image in combination with each correlation factor; S3: Constructing a compression rule correspondence table for compression rule scores and a storage rule correspondence table for storage rule scores; S4: Obtain the correlation factor value corresponding to the video image to be analyzed, and then calculate the compression rule score and storage rule score of the video image to be analyzed; S5: searching for a corresponding compression rule from a compression rule corresponding table based on the compression rule score of the video image to be analyzed, and searching for a corresponding storage rule from a storage rule corresponding table based on the storage rule score of the video image to be analyzed; S6: compressing and storing the video image to be analyzed based on the found compression rule and storage rule; Among them, the associated factors of video images include: point location type, monitoring point type, industry, image resolution, encoding format, equipment type and equipment construction age; The calculation formula for the compression rule score is: Compression rule score = image importance * (image quality score / 100) * encoding format weight coefficient * (device resolution / 100); The calculation formula for the storage rule score is: Storage rule score = Image importance * (Image quality score / 100) * Equipment construction age weight coefficient; The calculation formula for image importance is: Image importance = point location type weight score * point location type weight coefficient + industry weight score * industry weight coefficient + monitoring point type weight score * monitoring point type weight coefficient; The calculation formula for device resolution is: Device resolution = Image resolution weight point * Image resolution weight coefficient + Device type weight point * Device type weight coefficient.

2. The video image hierarchical storage method according to claim 1, characterized in that: Also includes: The compression and storage execution results within the set time interval are recorded, and the compression and storage rules are dynamically learned and updated based on the recorded results.

3. A video image hierarchical storage terminal device, characterized by: The method comprises a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 2 when executing the computer program.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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