An IoT terminal with efficient video data storage function

Abstract

The present invention relates to the technical field of data compression, and in particular to an Internet of Things terminal with a video data efficient storage function, comprising a data acquisition module, used for acquiring video data; a video compression module, used for respectively acquiring adaptive quantization parameters corresponding to each video frame image in the video data according to information content and display clarity in each video frame image in the video data, and compressing the video data according to the adaptive quantization parameters corresponding to each video frame image in the video data to obtain compressed video data; a quantum security encryption module, used for encrypting the compressed video data to obtain encrypted data; an edge proxy module, used for transmitting the encrypted data; and a video storage module, used for storing the encrypted data transmitted by the edge proxy module, and by setting appropriate quantization parameters, a better balance between video compression efficiency and quality can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data compression, and in particular to an Internet of Things terminal with a video data efficient storage function. Background Art

[0002] In the field of Internet of Things (IoT), more and more application scenarios require the collection, processing and storage of video data, including intelligent monitoring systems, drone monitoring, industrial automation, etc. The quantum security IoT terminal is a terminal system with a comprehensive solution that integrates IP resource management, video compression, quantum encryption and decryption, and transmission. The terminal system uses advanced video compression and quantum encryption and decryption technology to achieve efficient compression and security protection of monitoring video data, while supporting flexible storage and transmission methods to meet monitoring needs in various complex environments.

[0003] During the video acquisition and transmission process, the quantum secure IoT terminal generally needs to compress the acquired original video stream using an efficient compression method, thereby optimizing the video transmission bandwidth and saving storage space, etc. When compressing the acquired original video stream, the terminal system generally uses existing H.264 and other video compression technologies to compress the original video stream. Among them, the H.264 algorithm achieves a certain degree of lossy compression of the original video stream by configuring quantization parameters (QP) and other methods. However, the video type and content complexity acquired by the terminal system may vary in different scenarios and times, which may affect the compression effect of some videos when the acquired video stream information is compressed using the H.264 algorithm, resulting in a poor balance between the compressed volume and the video quality, thereby affecting the storage efficiency and playback effect of the video.

[0004] Therefore, how to improve the compression effect of video data in quantum secure IoT terminals to increase video storage efficiency has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides an Internet of Things terminal with a video data efficient storage function to solve the problem of how to improve the compression effect of video data in a quantum secure Internet of Things terminal to increase video storage efficiency.

[0006] An embodiment of the present invention provides an IoT terminal with a video data efficient storage function, the IoT terminal comprising:

[0007] A data acquisition module, used for acquiring video data;

[0008] A video compression module, configured to obtain, according to the information content and display definition of each video frame image in the video data, an adaptive quantization parameter corresponding to each video frame image in the video data, and perform H.264 encoding compression on the video data according to the adaptive quantization parameter corresponding to each video frame image in the video data to obtain compressed video data;

[0009] A quantum security encryption module, used for encrypting the compressed video data to obtain encrypted data;

[0010] An edge proxy module, used for transmitting the encrypted data;

[0011] The video storage module is used to store the encrypted data transmitted by the edge proxy module.

[0012] Preferably, the step of respectively acquiring the adaptive quantization parameter corresponding to each video frame image in the video data according to the information content and display clarity in each video frame image in the video data comprises:

[0013] For any video frame image in the video data, a grayscale image corresponding to the video frame image is obtained, and the importance of the video frame image is obtained according to the grayscale distribution difference in the grayscale image corresponding to the video frame image and its adjacent video frame images, and the display clarity of the video frame image is obtained according to the grayscale distribution characteristics of the grayscale image corresponding to the video frame image;

[0014] According to the importance and display clarity of the video frame image, the degree of information retention when compressing the video frame image is obtained, the PPI value of the video frame image is obtained, and according to the degree of information retention and the PPI value, the adaptive quantization parameter corresponding to the video frame image is obtained.

[0015] Preferably, obtaining the importance of the video frame image according to the grayscale distribution difference in the grayscale image corresponding to the video frame image and its adjacent video frame images includes:

[0016] Using a sliding window of a preset size, sliding in the grayscale image corresponding to the video frame image according to a preset sliding step length, obtaining the grayscale value mean of all pixels in each sliding window, and forming a grayscale value mean sequence;

[0017] Obtaining a front adjacent video frame image and a rear adjacent video frame image of the video frame image, obtaining a first DTW distance between a grayscale value mean sequence corresponding to the front adjacent video frame image and a grayscale value mean sequence corresponding to the video frame image, and obtaining a second DTW distance between a grayscale value mean sequence corresponding to the rear adjacent video frame image and a grayscale value mean sequence corresponding to the video frame image;

[0018] The average DTW distance between the first DTW distance and the second DTW distance is calculated, the absolute value of the difference between each two adjacent gray value means in the gray value mean sequence corresponding to the video frame image is obtained, and the average of the absolute values ​​of the difference is obtained, and the importance of the video frame image is obtained according to the average DTW distance and the average of the absolute values ​​of the difference.

[0019] Preferably, acquiring the display clarity of the video frame image according to the grayscale distribution characteristics of the grayscale image corresponding to the video frame image includes:

[0020] Clustering the gray value mean sequence corresponding to the video frame image to obtain multiple clusters, respectively obtaining the average value of all elements in each cluster, and for any cluster, obtaining the absolute value of the difference between the average value of the cluster and the average value of each cluster except the cluster, obtaining the average absolute value of the difference, which is recorded as the color difference degree of the cluster;

[0021] The color difference degree of each of the clusters is obtained, and the display clarity of the video frame image is obtained according to the average value of the color difference degrees of all the clusters.

[0022] Preferably, obtaining the degree of information retention when compressing the video frame image according to the importance and display clarity of the video frame image includes:

[0023] The reciprocal of the sum of the display clarity and a preset value is obtained, and the product of the reciprocal and the importance is normalized to obtain the degree of information retention when the video frame image is compressed.

[0024] Preferably, obtaining the adaptive quantization parameter corresponding to the video frame image according to the information retention degree and the PPI value includes:

[0025] The PPI value is normalized to obtain a corresponding normalized value, a subtraction result of the information retention degree and a constant 1 is obtained, and a weighted summation result of the subtraction result and the normalized value is rounded to obtain an adaptive quantization parameter corresponding to the video frame image.

[0026] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0027] The present invention provides an IoT terminal with a video data efficient storage function, including a data acquisition module for acquiring video data; a video compression module for respectively acquiring adaptive quantization parameters corresponding to each video frame image in the video data according to the information content and display clarity in each video frame image in the video data, and performing H.264 encoding compression on the video data according to the adaptive quantization parameters corresponding to each video frame image in the video data to obtain compressed video data; a quantum security encryption module for encrypting the compressed video data to obtain encrypted data; an edge proxy module for transmitting the encrypted data; and a video storage module for storing the encrypted data transmitted by the edge proxy module. Among them, when performing H.264 encoding compression on the collected video data, by setting appropriate quantization parameters, the video volume can be effectively reduced, while retaining the quality of the compressed video to a greater extent, so as to achieve a better balance between video compression efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, 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 labor.

[0029] Figure 1 This is a structural block diagram of an Internet of Things terminal with a video data efficient storage function provided by Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, but should not be understood as limiting the present disclosure.

[0031] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0032] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0033] See also Figure 1 , is a structural block diagram of an IoT terminal with a video data efficient storage function provided by the first embodiment of the present invention, such as Figure 1 As shown, the IoT terminal may include:

[0034] The data acquisition module 11 is used to acquire video data.

[0035] The video compression module 12 is used to obtain the adaptive quantization parameters corresponding to each video frame image in the video data according to the information content and display clarity of each video frame image in the video data, and perform H.264 encoding compression on the video data according to the adaptive quantization parameters corresponding to each video frame image in the video data to obtain compressed video data.

[0036] The quantum secure encryption module 13 is used to encrypt the compressed video data to obtain encrypted data.

[0037] The edge proxy module 14 is used to transmit the encrypted data.

[0038] The video storage module 15 is used to store the encrypted data transmitted by the edge proxy module.

[0039] In one embodiment, it can be known that the quantum secure IoT terminal includes a data acquisition module, a video compression module, a quantum secure encryption module, an edge proxy module, and a video storage module, etc., and the main workflow of the quantum secure IoT terminal is: collect video data through the data acquisition module, compress the collected video data using the video compression module, encrypt the compressed video data through the quantum secure encryption module, and the edge proxy module transmits the encrypted video data to the designated video storage module through the network, and the video storage module receives the encrypted video data and stores it. It is worth noting that the quantum secure IoT terminal belongs to the prior art and will not be described in detail here.

[0040] It is known that the video compression module in the quantum security IoT terminal uses the H.264 algorithm to compress the collected video data. When the H.264 algorithm is used to compress the collected video data, the size of the quantization parameter (QP) has a certain influence on the quality and volume of the compressed video, which is mainly manifested as: the quantization parameter is inversely proportional to the size of the compressed video and the quality of the video frame content. It can be seen that the quantization parameter configured in the H.264 algorithm may affect the balance between the compression effect and the video quality of video data of different types or acquired at different times. Therefore, the embodiment of the present invention provides an IoT terminal with a video data efficient storage function, which is used to improve the balance between the compression effect and the video quality of the video data. Among them, the IoT terminal is obtained by partially improving the quantum security IoT terminal, and the improved part is to set an adaptive quantization parameter for the H.264 algorithm in the video encryption module of the quantum security IoT terminal, while keeping the other parts of the quantum security IoT terminal unchanged, thereby forming an IoT terminal with a video data efficient storage function of the present invention.

[0041] Specifically, for a video encryption module in an IoT terminal with a video data efficient storage function of the present invention, when the video data collected by the data acquisition module is compressed by H.264 encoding, in order to reduce the amount of calculation in the quantization parameter estimation process during video compression, the acquired video data is first copied, and the copied video data is used as the analysis object, and then the copied video data is converted into a video frame image, and each video frame image is grayed to obtain a gray image corresponding to each video frame image. Among them, the graying process belongs to the prior art and will not be repeated here. In an embodiment of the present invention, when compressing the video data, in order to retain the information data in the video frame image with greater importance to a greater extent, the information data in the video frame image with less importance is compressed using a larger lossy compression ratio, thereby reducing its storage space and reducing the subsequent video storage end The impact on the perception of the compressed video during playback, an adaptive quantization parameter is set for each video frame image of the video data, wherein the method for obtaining the adaptive quantization parameter is as follows:

[0042] For any video frame image in the video data, a grayscale image corresponding to the video frame image is obtained, and the importance of the video frame image is obtained according to the grayscale distribution difference in the grayscale image corresponding to the video frame image and its adjacent video frame images.

[0043] Specifically, a sliding window of a preset size is used to slide in the grayscale image corresponding to the video frame image according to a preset sliding step length to obtain the grayscale value mean of all pixels in each sliding window to form a grayscale value mean sequence;

[0044] Obtaining a front adjacent video frame image and a rear adjacent video frame image of the video frame image, obtaining a first DTW distance between a grayscale value mean sequence corresponding to the front adjacent video frame image and a grayscale value mean sequence corresponding to the video frame image, and obtaining a second DTW distance between a grayscale value mean sequence corresponding to the rear adjacent video frame image and a grayscale value mean sequence corresponding to the video frame image;

[0045] The average DTW distance between the first DTW distance and the second DTW distance is calculated, the absolute value of the difference between each two adjacent gray value means in the gray value mean sequence corresponding to the video frame image is obtained, and the average of the absolute values ​​of the difference is obtained, and the importance of the video frame image is obtained according to the average DTW distance and the average of the absolute values ​​of the difference.

[0046] In one embodiment, taking the i-th video frame image as an example, a sliding window of size 3×3 is established, and the sliding step is set to 2, that is, two pixels. The upper left corner of the grayscale image corresponding to the i-th video frame image is used as the starting point, and the sliding is performed from left to right and from top to bottom. During the sliding process, the grayscale value mean of each pixel in the sliding window is calculated. Then, for the grayscale image corresponding to the i-th video frame image, after the sliding window slides in the grayscale image, the grayscale value mean of each pixel in the sliding window obtains a sequence during the continuous sliding process, and the sequence is used as the grayscale value mean sequence. Similarly, each video frame image can obtain the corresponding grayscale value mean sequence.

[0047] For the i-th video frame image, the greater the degree of change of the gray value mean sequence, the more uneven the color distribution in the video frame image, which means that the information content in the video frame image may be richer and its importance may be greater. At the same time, the faster the position of the pixel points in the previous and subsequent frames of the video frame image changes, the video frame image may be a key frame for the motion change of the content information in the video data, and its importance may also be greater. Therefore, the DTW algorithm is used to obtain the DTW distance of the corresponding gray value mean sequence between the i-th video frame image and the i-1-th video frame image, which is recorded as the first DTW distance between the i-th video frame image and the i-1-th video frame image. Similarly, the second DTW distance between the i-th video frame image and the i+1-th video frame image is obtained. Among them, the DTW algorithm belongs to the prior art and will not be repeated here.

[0048] According to the first DTW distance, the second DTW distance and the gray value mean sequence of the i-th video frame image, the importance of the i-th video frame image is obtained:

[0049]

[0050] in, Indicates the importance of the i-th video frame image, represents the first DTW distance between the i-th video frame image and the i-1-th video frame image, represents the second DTW distance between the i-th video frame image and the i+1-th video frame image, Represents the number of grayscale mean values ​​in the grayscale mean sequence of the i-th video frame image, represents the sth grayscale mean in the grayscale mean sequence of the i-th video frame image, represents the s+1th grayscale value mean in the grayscale value mean sequence of the i-th video frame image, and || represents the absolute value sign.

[0051] It should be noted that It is used to characterize the difference in information content richness between the i-th video frame image and its adjacent video frames. The larger its value is, the greater the degree of position change of the pixel points in the i-th video frame image is. Then, the probability that the i-th video frame image is a key frame of the object motion change in the continuous frames is greater, and its importance is greater. The mean of the absolute value of the difference between each grayscale mean and the subsequent adjacent grayscale mean in the grayscale mean sequence used to characterize the i-th video frame image. The larger the value, the more uneven the color change of the content in the i-th video frame image, the richer its content may be, the more content information may be displayed, and the greater its importance.

[0052] Furthermore, considering the importance of the i-th video frame image during video compression, although some high-frequency information can be appropriately removed to reduce the video compression volume, the color richness and clarity of the content display of the i-th video frame image collected may be affected over time and ambient light. If a video frame image with lower display clarity is subjected to a greater degree of lossy compression, the video frame information may be over-smoothed, thereby affecting the quality of the video.

[0053] For example: although the importance of a certain video frame image is relatively low, the display clarity of its content may be affected by the ambient light, and the display clarity is poor. If it is subjected to a large degree of lossy compression, it may cause a large loss of detail information in the video frame image, thereby affecting the quality of the video after compression. On the contrary, when the importance of a certain video frame image is relatively high and its display clarity is also good, a certain degree of lossy compression of the video frame image can appropriately reduce the high-frequency information in the image, which will not affect the display quality of the video to a large extent, and can also reduce the volume of the video frame after compression. Therefore, the display clarity of the video frame image is obtained according to the grayscale distribution characteristics of the grayscale image corresponding to the video frame image.

[0054] The method for obtaining the display clarity is as follows:

[0055] Clustering the gray value mean sequence corresponding to the video frame image to obtain multiple clusters, respectively obtaining the average value of all elements in each cluster, and for any cluster, obtaining the absolute value of the difference between the average value of the cluster and the average value of each cluster except the cluster, obtaining the average absolute value of the difference, which is recorded as the color difference degree of the cluster;

[0056] The color difference degree of each of the clusters is obtained, and the display clarity of the video frame image is obtained according to the average value of the color difference degrees of all the clusters.

[0057] In one embodiment, the elements in the grayscale value mean sequence corresponding to the ith video frame image are arranged in descending order to obtain a sorted grayscale value mean sequence. This is not required here, and the sequence can also be arranged in ascending order. Then, the sorted grayscale value mean sequence is clustered using iterative self-organizing clustering (ISODATA) to obtain multiple cluster clusters, wherein iterative self-organizing clustering (ISODATA) belongs to the existing clustering algorithm and will not be described in detail here. Different cluster clusters represent different color areas in the ith video frame image. If the color difference between these color areas is large, it means that the display contrast and clarity of the ith video frame image are good. Then, when the ith video frame image is compressed, the degree of lossy compression is large, which can not only meet the requirement of a small compressed volume of the ith video frame image, but also reduce the loss of image information caused by lossy compression. Therefore, the average value of all grayscale value means in each cluster cluster is calculated respectively, and the average value of the rth cluster cluster corresponding to the ith video frame image is recorded as , according to the average value difference of all clusters, get the display clarity of the i-th video frame image:

[0058]

[0059] in, Indicates the display clarity of the i-th video frame image, Indicates the number of clusters corresponding to the i-th video frame image, represents the average value of the rth cluster corresponding to the i-th video frame image, represents the average value of the u-th cluster corresponding to the i-th video frame image, and || represents the absolute value symbol.

[0060] It should be noted that It is used to characterize the difference between the average value of the rth cluster and the average value of each remaining cluster. The larger the difference mean is, the greater the difference between the rth cluster and the remaining clusters is, which indirectly reflects that the display clarity between different color areas in the i-th video frame image is greater.

[0061] Furthermore, after determining the importance and display clarity of the i-th video frame image, the degree of information retention when the video frame image is compressed can be obtained based on the importance and display clarity of the video frame image. The specific acquisition method is: obtain the inverse of the sum of the display clarity and a preset value, normalize the product of the inverse and the importance, and obtain the degree of information retention when the video frame image is compressed.

[0062] In one embodiment, the calculation expression of the information retention degree of the i-th video frame image is:

[0063]

[0064] in, Indicates the degree of information retention of the i-th video frame image, norm() represents the normalization function, Indicates the importance of the i-th video frame image, Indicates the display clarity of the i-th video frame image, c represents a preset value used to prevent the denominator from being 0, and its value is 0.01.

[0065] It should be noted that The larger the value of , the greater the degree of information retention when compressing the i-th video frame image; The smaller the value of The larger the value of , the worse the display clarity of the i-th video frame image is. Therefore, when compressing the i-th video frame image, the greater the degree of information retention of the i-th video frame image is, so as to avoid the loss of image display details caused by excessive lossy compression.

[0066] Considering that when the video data is compressed by H.264 encoding, there is a certain correlation between the degree of video information retention and the quantization parameter, the PPI value of the i-th video frame image is further combined to obtain the corresponding quantization parameter when the i-th video frame image is compressed by H.264 encoding, that is, the adaptive quantization parameter corresponding to the i-th video frame image. It is worth noting that the PPI value (Pixels Per Inch) of an image is an important indicator for measuring the resolution of an image or display device. It indicates the number of pixels contained in each inch of length. It belongs to the prior art and will not be repeated here.

[0067] Among them, according to the information retention degree and the PPI value, the adaptive quantization parameter corresponding to the video frame image is obtained, including: normalizing the PPI value to obtain a corresponding normalized value, obtaining a subtraction result of the information retention degree and a constant 1, and rounding the weighted sum of the subtraction result and the normalized value to obtain an adaptive quantization parameter corresponding to the video frame image.

[0068] In one implementation, the calculation expression of the adaptive quantization parameter corresponding to the i-th video frame image is:

[0069]

[0070] in, represents the adaptive quantization parameter corresponding to the i-th video frame image, round() represents the rounding function, a represents the first weight, and norm() represents the normalization function. represents the PPI value of the i-th video frame image, b represents the second weight, Indicates the degree of information retention of the i-th video frame image, and 1 represents a constant.

[0071] It should be noted that the first weight a=0.2 and the second weight b=0.8 are set. The smaller it is, the lower the resolution of the current video frame is. When compressing, the smaller the quantization parameter is selected to avoid a large loss of detail information of the i-th video frame image, thereby affecting the video display quality. The larger the value of is, the smaller the corresponding quantization parameter is, and the greater the degree of retention of content details in the i-th video frame image.

[0072] At this point, the adaptive quantization parameter corresponding to the i-th video frame image when H.264 encoding compression is performed can be obtained. Similarly, the adaptive quantization parameter of each video frame image in the video data is obtained, which is used to perform H.264 encoding compression on the video data to obtain compressed video data. The application steps of the quantization parameter in the H.264 algorithm are as follows:

[0073] (1) Frame segmentation: Each frame of video is divided into macroblocks (16×16 pixel blocks).

[0074] (2) Intra-frame prediction: To encode a frame of video, first perform intra-frame prediction on the current block. Intra-frame prediction uses the already encoded adjacent blocks to predict the value of the current block.

[0075] (3) Inter-frame prediction: Inter-frame prediction is performed on the current block. Inter-frame prediction uses the blocks of the previous frame or the next frame that have been encoded to predict the value of the current block.

[0076] (4) Transformation and quantization: Transform and quantize the predicted block. Transformation transforms low-frequency information to the upper left corner and high-frequency information to the lower right corner. Quantization converts the transformed coefficients to smaller values, thereby reducing the amount of data. The specific steps of quantization are: input quantization parameters; generate quantization step table; apply transformation (DCT / IDCT); perform quantization operation; quantized coefficients.

[0077] (5) Entropy coding: Entropy coding is performed on the quantized coefficients. Entropy coding uses short codes to represent symbols with high frequency and long codes to represent symbols with low frequency, thereby reducing the amount of data.

[0078] (6) Output code stream: Output the code stream obtained in the above steps.

[0079] It is worth noting that the application of quantization parameters in the H.264 algorithm and H.264 encoding compression are both existing technologies and will not be described in detail here.

[0080] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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, and should be included in the protection scope of the present invention.

Claims

1. An IoT terminal with efficient video data storage function, characterized in that: The IoT terminal comprises: A data acquisition module, used for acquiring video data; A video compression module, configured to obtain, according to the information content and display definition of each video frame image in the video data, an adaptive quantization parameter corresponding to each video frame image in the video data, and perform H.264 encoding compression on the video data according to the adaptive quantization parameter corresponding to each video frame image in the video data to obtain compressed video data; A quantum security encryption module, used for encrypting the compressed video data to obtain encrypted data; An edge proxy module, used for transmitting the encrypted data; A video storage module, used for storing the encrypted data transmitted by the edge proxy module; According to the information content and display definition of each video frame image in the video data, respectively obtaining the adaptive quantization parameter corresponding to each video frame image in the video data, including: For any video frame image in the video data, a grayscale image corresponding to the video frame image is obtained, and the importance of the video frame image is obtained according to the grayscale distribution difference in the grayscale image corresponding to the video frame image and its adjacent video frame images, and the display clarity of the video frame image is obtained according to the grayscale distribution characteristics of the grayscale image corresponding to the video frame image; According to the importance and display clarity of the video frame image, obtain the degree of information retention when compressing the video frame image, obtain the PPI value of the video frame image, and according to the degree of information retention and the PPI value, obtain the adaptive quantization parameter corresponding to the video frame image; Obtaining the importance of the video frame image according to a grayscale distribution difference in a grayscale image corresponding to the video frame image and an adjacent video frame image, including: Using a sliding window of a preset size, sliding in the grayscale image corresponding to the video frame image according to a preset sliding step length, obtaining the grayscale value mean of all pixels in each sliding window, and forming a grayscale value mean sequence; Obtaining a front adjacent video frame image and a rear adjacent video frame image of the video frame image, obtaining a first DTW distance between a grayscale value mean sequence corresponding to the front adjacent video frame image and a grayscale value mean sequence corresponding to the video frame image, and obtaining a second DTW distance between a grayscale value mean sequence corresponding to the rear adjacent video frame image and a grayscale value mean sequence corresponding to the video frame image; The average DTW distance between the first DTW distance and the second DTW distance is calculated, the absolute value of the difference between each two adjacent gray value means in the gray value mean sequence corresponding to the video frame image is obtained, and the average of the absolute values ​​of the difference is obtained, and the importance of the video frame image is obtained according to the average DTW distance and the average of the absolute values ​​of the difference.

2. The IoT terminal with efficient video data storage function according to claim 1, characterized in that: The obtaining the display clarity of the video frame image according to the grayscale distribution feature of the grayscale image corresponding to the video frame image includes: Clustering the gray value mean sequence corresponding to the video frame image to obtain multiple clusters, respectively obtaining the average value of all elements in each cluster, and for any cluster, obtaining the absolute value of the difference between the average value of the cluster and the average value of each cluster except the cluster, obtaining the average absolute value of the difference, which is recorded as the color difference degree of the cluster; The color difference degree of each of the clusters is obtained, and the display clarity of the video frame image is obtained according to the average value of the color difference degrees of all the clusters.

3. The IoT terminal with efficient video data storage function according to claim 1, characterized in that: The obtaining, according to the importance and display clarity of the video frame image, the degree of information retention when compressing the video frame image includes: The reciprocal of the sum of the display clarity and a preset value is obtained, and the product of the reciprocal and the importance is normalized to obtain the degree of information retention when the video frame image is compressed.

4. The IoT terminal with efficient video data storage function according to claim 1, characterized in that: The step of obtaining the adaptive quantization parameter corresponding to the video frame image according to the information retention degree and the PPI value includes: The PPI value is normalized to obtain a corresponding normalized value, a subtraction result of the information retention degree and a constant 1 is obtained, and a weighted summation result of the subtraction result and the normalized value is rounded to obtain an adaptive quantization parameter corresponding to the video frame image.

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