Management platform with audio and video local storage management function

Abstract

The invention relates to the technical field of audio and video management, in particular to a management platform with an audio and video local storage management function, and the management platform comprises a video mapping module which is used for obtaining data points in a three-dimensional space; the scanning traversal direction acquisition module is used for clustering the data points in the three-dimensional space according to the measurement distance between the data points in the three-dimensional space to obtain each cluster, obtaining a scanning traversal main direction, a scanning traversal secondary direction and a scanning traversal tail direction according to the number of neighborhood data points of the data points in each clustering cluster, the side length of the minimum circumscribed cube of each clustering cluster and the proportion of the data points in the corresponding minimum circumscribed cube; and the compression storage management module is used for carrying out scanning traversal coding on the data points in the three-dimensional space according to the scanning traversal main direction, the scanning traversal secondary direction and the scanning traversal end direction to obtain compressed data, and carrying out storage management on the compressed data. And the compression storage management effect of the video can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of audio and video management, and particularly relates to a management platform with audio and video local storage management function. BACKGROUND

[0002] A large amount of audio and video data will be generated in the fields of security monitoring, remote conference, medical imaging, etc., and in order to save storage space, etc., it is usually necessary to compress and store the audio and video data. In the prior art, a fixed scanning traversal path is usually used to scan and traverse the video images in the audio and video, the scanning sequence obtained is run-length coded to obtain compressed data, and the compressed data is stored and managed. However, the degree of continuous repetition of video images in different fields in different directions will be different, and run-length coding has a good compression effect on continuous repeated pixels. Therefore, the scanning and traversal of the images in the video according to the fixed scanning traversal path will result in a relatively low degree of continuous repetition of the obtained sequence, and when the degree of continuous repetition is low, the storage management effect of the video will be poor. Therefore, the compression and storage management effect of the video needs to be improved. SUMMARY

[0003] In order to solve the above problems, the present application provides a management platform with audio and video local storage management function, and the technical solution is as follows:

[0004] One embodiment of the present application provides a management platform with audio and video local storage management function, which comprises:

[0005] A video mapping module is configured to obtain a to-be-compressed video and map each frame image in the to-be-compressed video to a three-dimensional space to obtain data points in the three-dimensional space.

[0006] A scanning traversal direction obtaining module is configured to obtain a metric distance between the data points in the three-dimensional space according to the spatial distance and the gray difference between the data points in the three-dimensional space, cluster the data points in the three-dimensional space according to the metric distance between the data points to obtain each cluster, and obtain a main scanning traversal direction, a secondary scanning traversal direction and a terminal scanning traversal direction according to the number of neighborhood data points of the data points in each cluster, the edge length of the minimum circumscribed cube of each cluster and the proportion of the data points in the cluster in the corresponding minimum circumscribed cube.

[0007] The compression storage management module is configured to encode the data points in the three-dimensional space according to the main scanning traversal direction, the secondary scanning traversal direction and the end scanning traversal direction to obtain compressed data, and store and manage the compressed data.

[0008] Beneficial effects: The application comprises a video mapping module configured to obtain a video to be compressed and map each frame image in the video to be compressed to data points in a three-dimensional space; a scanning traversal direction obtaining module configured to obtain a metric distance between the data points in the three-dimensional space according to a spatial distance and a gray difference between the data points in the three-dimensional space, and cluster the data points in the three-dimensional space according to the metric distance between the data points in the three-dimensional space to obtain each cluster, and obtain the main scanning traversal direction, the secondary scanning traversal direction and the end scanning traversal direction according to a number of neighborhood data points of the data points in each cluster, a side length of a minimum circumscribed cube of each cluster and a proportion of intra-cluster data points in the corresponding minimum circumscribed cube; and a compression storage management module configured to encode the data points in the three-dimensional space according to the main scanning traversal direction, the secondary scanning traversal direction and the end scanning traversal direction to obtain compressed data, and store and manage the compressed data. The main scanning traversal direction, the secondary scanning traversal direction and the end scanning traversal direction determined according to the cluster distribution characteristics can make the sequence obtained by scanning traversal have a high degree of continuous repetition or similarity, thereby improving the compression storage management effect of the video. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0010] Figure 1 The structural block diagram of the management platform with the audio and video local storage management function. DETAILED DESCRIPTION

[0011] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the embodiments of the present application.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0013] This embodiment provides a management platform with local audio and video storage management functions, detailed in the following description:

[0014] like Figure 1 As shown in the figure, this embodiment provides a management platform with local audio and video storage management functions, including:

[0015] The video mapping module 01 is used to acquire the video to be compressed and map each frame of the video to be compressed into a three-dimensional space to obtain data points in the three-dimensional space.

[0016] Because the degree of continuous repetition or similarity of images in different videos varies in different directions, scanning images in a video using a fixed scanning traversal path will result in a low degree of continuous repetition or similarity of pixels in the obtained scanning sequence. This leads to poor encoding and compression effects on the scanning sequence, resulting in poor video storage management. In order to improve the compression effect of the video and ensure the effectiveness of video storage management, this embodiment will adaptively adjust the scanning traversal path based on video features to increase the degree of continuous repetition or similarity of pixel values ​​in the obtained scanning sequence, thereby improving the encoding and compression effect. That is, the higher the degree of continuous repetition or similarity of pixels in the scanning sequence, the better the effect or compression efficiency of subsequent run-length encoding of the differential sequence of the scanning sequence. In addition, for ease of understanding, this embodiment will subsequently use any video that needs to be compressed for compression storage management, and record the video that needs to be compressed as the video to be compressed. In this embodiment, the images in the video to be compressed are grayscale images, and the images in the video to be compressed are two-dimensional images. If a video is to be compressed and stored, the corresponding video needs to be transmitted to the management platform terminal of the video local storage management function. The subsequent process is also carried out on the management platform terminal of the video local storage management function.

[0017] After obtaining the video to be compressed, a three-dimensional space is constructed. The horizontal axis of this space represents the horizontal pixel position within a single frame, the vertical axis represents the vertical pixel position within a single frame, and the vertical axis represents the sequence number of the image within the video to be compressed. Then, each frame of the video to be compressed is mapped sequentially into the three-dimensional space according to its order within the video, resulting in multiple data points in the mapped three-dimensional space. The number of data points in the three-dimensional space is equal to the total number of pixels in the video to be compressed. During mapping, each frame is inserted into one layer of the three-dimensional space sequentially according to the order of the images in the video to be compressed. Each horizontal layer of the mapped three-dimensional space is a binary system. The 3D image and the mapped 3D space have multiple layers of images stacked along the vertical axis, with the number of stacked layers matching the total number of images in the video. The coordinates of any pixel in any image and the sequence number of the image to which that pixel belongs in the video can be mapped to a data point in 3D space. The gray value of this data point is the gray value of that pixel. In other words, each data point in 3D space corresponds to a pixel or a gray value. The spatial distance between any two adjacent data points in 3D space, whether horizontally, vertically, or in any other direction, is one unit length. That is, the spatial distance between any two adjacent data points along any coordinate axis in 3D space is one unit length.

[0018] In the mapped 3D space, the x-coordinate of a data point represents its position index in the horizontal direction of a single frame of the image. For example, if a frame has 800 pixels in width, the x-coordinate ranges from 1 to 800, indicating which column the corresponding data point is in horizontally. The y-coordinate represents its position index in the vertical direction of a single frame of the image. For example, if a frame has 600 pixels in height, the y-coordinate ranges from 1 to 600, indicating which row the corresponding data point is in vertically. The y-coordinate represents the sequence number of the video frame. For example, if the video to be compressed has 120 frames, that is, 120 images... The image, with its vertical coordinates ranging from 1 to 120, determines which frame of the video a given point corresponds to. For example, for any data point in three-dimensional space, if the coordinates of the data point are (1,2,3), then it means that the data point is mapped from the pixel at the position of the second row in the first column of the third frame of the video to be compressed. The grayscale value corresponding to this data point is the grayscale value of the pixel at the position of the second row in the first column of the third frame of the video to be compressed, or in other words, it means that the data point is mapped from the pixel at coordinates (1,2) in the third frame of the video to be compressed. It should be noted that the horizontal and vertical axes of the two-dimensional space in which the image is located before mapping are consistent with the horizontal and vertical axes of the three-dimensional space.

[0019] Therefore, this embodiment completes the conversion and mapping of the video to be compressed through the above process and obtains data points in three-dimensional space. The reason for mapping the video to be compressed into three-dimensional space in this embodiment is that not only may there be some similar or identical regions on a single frame image, but there may also be similar or identical regions between different frames. Therefore, mapping all images in the video into the same space takes into account the similarity or similarity between frames when determining the scanning traversal path, so as to select scanning traversal paths with higher repeatability, thereby further improving the compressibility limit of run-length encoding or further improving the compression effect of run-length encoding.

[0020] The scanning traversal direction acquisition module 02 is used to obtain the metric distance between data points in the three-dimensional space based on the spatial distance and grayscale difference between data points in the three-dimensional space, and to cluster the data points in the three-dimensional space according to the metric distance between data points in the three-dimensional space to obtain each cluster. Based on the number of neighboring data points of the data points in each cluster, the side length of the minimum bounding cube of each cluster, and the proportion of data points within the cluster in the corresponding minimum bounding cube, the main scanning traversal direction, the secondary scanning traversal direction, and the final scanning traversal direction are obtained.

[0021] After completing the video conversion and mapping, this embodiment next clusters the data points in three-dimensional space. The purpose of clustering is to facilitate the determination of the subsequent scanning traversal direction. Before clustering, it is necessary to determine the metric distance between data points based on the spatial distance and grayscale difference between them. The metric distance is the basis for clustering data points. Therefore, the specific process of obtaining the metric distance between data points in three-dimensional space based on the spatial distance and grayscale difference in this embodiment is as follows:

[0022] For data points a1 and a2 in 3D space, which are not the same data point, calculate the Euclidean distance between them and normalize it. The result of this normalization is recorded as the first distance value. The Euclidean distance between data points a1 and a2 is the Euclidean distance between the 3D coordinates of data point a1 and a2. Calculate the absolute value of the difference between the grayscale values ​​corresponding to data points a1 and a2, normalize this difference, and record the result as the second distance value. If a data point is mapped from a pixel in a frame of an image, then the grayscale value of that data point is the grayscale value corresponding to that pixel. Calculate the sum of the first and second distance values ​​and use this sum as the metric distance between data points a1 and a2. The expression for calculating the metric distance between data points a1 and a2 is as follows:

[0023]

[0024] in, Norm() is the normalization function used to measure the distance between data points a1 and a2. Let a be the Euclidean distance between data points a1 and a2. The gray value corresponding to data point a1 This represents the grayscale value corresponding to data point a2; and When it is larger, The larger, the opposite. and The smaller, The smaller, The smaller the value, the more similar the gray levels of the two data points are and the closer their spatial locations are. During clustering, the two data points are more likely to be clustered into the same cluster.

[0025] After obtaining the metric distance between data points in three-dimensional space, K-means clustering is performed on all data points in three-dimensional space based on the metric distance between data points in three-dimensional space to obtain each cluster. Given the metric distance between data points, the process of clustering using the K-means clustering algorithm is a well-known technique, so it will not be described in this embodiment. Furthermore, the clustering results obtained based on the metric distance consider both the distance distribution of data points in space and the similarity of the gray values ​​of the data points themselves. It can simultaneously cluster data points that are spatially close and have similar gray values ​​into one cluster. That is, the data points in the same cluster are spatially close and have similar gray values.

[0026] Since run-length encoding has higher compression efficiency for longer repetitive and similar data, this embodiment needs to make the continuous repetitive or similar parts in the scan traversal path as long as possible. Therefore, after obtaining the clusters by clustering, this embodiment needs to determine the primary, secondary, and final directions of the scan traversal based on the distribution characteristics of the clusters. Subsequently, the scan traversal is performed based on the obtained primary, secondary, and final directions. Performing the scan traversal based on the primary, secondary, and final directions can make the gray-level similarity points on the same frame and the gray-level similarity points between different frames as continuous as possible in the sequence obtained by the scan traversal, so as to improve the encoding compression effect.

[0027] Since a longer combined length of the same type of side lengths of the circumscribed cubes of all clusters in space indicates that the direction corresponding to that type of side better reflects the overall data or grayscale distribution trend in space, using this direction as the primary scanning direction during subsequent scanning traversal will result in a relatively larger number of consecutive repetitions or similar points in the final sequence compared to using other directions as the primary scanning traversal direction. Therefore, in this embodiment, the primary, secondary, and final scanning directions will be determined by the side length of the circumscribed cubes of the clusters. However, the distribution of data points within clusters in the circumscribed cubes of clusters varies. There may be clusters where the data points are relatively discrete but the overall distribution range of the clusters is large. The degree of consecutive repetition or similarity of data within these clusters is poor. If too much reference or over-reference is used when determining the primary, secondary, and final scanning directions, the results will be significantly affected. Relying solely on the side length of the outer cube of such clusters leads to low reliability in determining the primary, secondary, and final directions, resulting in continuous repetition or low similarity of data in the actual scanning traversal path. Therefore, this embodiment, when determining the primary, secondary, and final directions of scanning traversal based on the side length of the outer cube of the cluster, also needs to consider the actual distribution of data points within the cluster. In other words, before determining the primary, secondary, and final directions of scanning traversal based on the side length of the outer cube of the cluster, this embodiment needs to first obtain the direction confidence of each cluster based on the number of neighboring data points of each data point in each cluster and the proportion of intra-cluster data points in the smallest outer cube of each cluster. The direction confidence of each cluster can reflect the reference degree or importance of the corresponding cluster when determining the primary, secondary, and final directions of scanning traversal. The specific process for obtaining the direction confidence of each cluster is as follows:

[0028] First, in three-dimensional space, the minimum bounding cube of each cluster is constructed. In this embodiment, the edges of the minimum bounding cube of each cluster are required to be parallel to the coordinate axes of the three-dimensional space; that is, the length of the minimum bounding cube is parallel to the horizontal axis of the three-dimensional space, the width is parallel to the vertical axis, and the height is parallel to the vertical axis. Then, the total number of data points in each cluster, the volume of the minimum bounding cube of each cluster, the number of data points belonging to the preset neighborhood of a data point and belonging to the same cluster as the corresponding data point, and the cube volume are obtained. The calculation process is well-known. Based on the ratio of the total number of data points in each cluster to the minimum bounding cube volume of the corresponding cluster, the filling degree characterization value of each cluster is obtained. That is, the filling degree characterization value of any cluster is the ratio of the total number of data points in that cluster to the minimum bounding cube volume of that cluster. Then, based on the total number of data points belonging to the preset neighborhood of the data point and belonging to the same cluster as the corresponding data point, the intra-cluster distribution density characterization value of each cluster is obtained. Finally, the sum of the filling degree characterization value of each cluster and the intra-cluster distribution density characterization value of the corresponding cluster is recorded as the directional confidence of the corresponding cluster.

[0029] The specific process for obtaining the intra-cluster distribution density characterization value of each cluster based on the total number of data points belonging to the preset neighborhood of the data point and belonging to the same cluster is as follows:

[0030] The maximum total number of data points within a preset neighborhood of a data point is obtained and recorded as the total neighborhood count. The maximum total number of data points within a preset neighborhood is the same for all data points. In specific applications, the implementer needs to set the preset neighborhood according to the actual situation. For example, in this embodiment, the preset neighborhood can be set to 26 neighborhoods, so the maximum total number of data points within the preset neighborhood of any data point is 26, and the total neighborhood count is also 26. For any data point, the total number of data points belonging to the preset neighborhood of that data point and belonging to the same cluster as that data point is obtained and recorded as the intra-cluster neighborhood count of that data point. That is, if there are 5 data points in the cluster to which the data point belongs that data point and belong to the 26 neighborhood of that data point, then the intra-cluster neighborhood count of that data point is 5. The ratio of the intra-cluster neighborhood count of that data point to the total neighborhood count is calculated and recorded as the intra-cluster neighborhood proportion of that data point. The mean of the intra-cluster neighborhood proportions of all data points in each cluster is calculated and recorded as the intra-cluster distribution density characterization value of the corresponding cluster.

[0031] Furthermore, the specific expression for calculating the directional confidence score corresponding to any cluster q is as follows:

[0032]

[0033] in, The directional confidence level is the value of cluster q. Let q be the total number of data points in cluster q. Let be the volume of the minimum bounding cube of cluster q. This represents the filling degree of cluster q. Let Ni be the number of intra-cluster neighbors of the i-th data point in cluster q, and N0 be the total number of neighbors. Let be the intra-cluster distribution density representation value of cluster q. Since a larger proportion of data points in a cluster relative to its minimum bounding cube (the higher the fill factor of the minimum bounding cube), indicates that the minimum bounding cube better represents the cluster itself, and thus the cluster is more important in determining the primary, secondary, and final directions of the subsequent scan traversal, therefore, when... A larger value indicates that the minimum bounding cube of cluster q better represents cluster q itself, and also indicates that cluster q is more important in determining the primary, secondary, and final directions of the subsequent scan traversal. Higher importance corresponds to higher confidence in the direction of the cluster. When there are more gaps or more discrete data points within a cluster, the continuity and gray-level repetition similarity of the data points within the cluster will be weaker. Conversely, when the data points are more densely distributed and the number of gaps within a cluster is smaller, the continuity and gray-level repetition similarity of the data points within the cluster will be stronger, indicating that the corresponding cluster is more important in determining the primary, secondary, and final directions of the subsequent scan traversal. Therefore, when... A larger value indicates a denser distribution of data points within cluster q, fewer gaps between data points within cluster q, and greater importance of cluster q in determining the primary, secondary, and final directions of the subsequent scan traversal. A higher importance corresponds to a higher confidence level for the direction of the cluster. Conversely, when... and When it is larger, The larger, therefore when The larger the value, the more important the cluster q becomes in determining the primary, secondary, and final directions of the subsequent scan traversal. When the cluster q becomes more important in determining the primary, secondary, and final directions of the subsequent scan traversal, then the cluster q needs to be referenced more often in determining the primary, secondary, and final directions of the subsequent scan traversal, or in other words, the cluster q is more reliable in determining the primary, secondary, and final directions of the subsequent scan traversal.

[0034] After obtaining the orientation confidence scores for each cluster, this embodiment determines the main scan traversal direction, the secondary scan traversal direction, and the final scan traversal direction based on the orientation confidence scores and the side length of the minimum bounding cube of each cluster. These directions serve as the basis for subsequent scan traversals. The specific process for determining the main scan traversal direction, the secondary scan traversal direction, and the final scan traversal direction is as follows:

[0035] First, based on the directional confidence scores of each cluster and the side length of the minimum bounding cube of each cluster, the weighted length, weighted width, and weighted height of the minimum bounding cube of each cluster are obtained. The weighted length of the minimum bounding cube of any cluster is the product of the length of the minimum bounding cube and the directional confidence score of the corresponding cluster. The weighted width of the minimum bounding cube of any cluster is the product of the width of the minimum bounding cube and the directional confidence score of the corresponding cluster. The weighted height of the minimum bounding cube of any cluster is the product of the height of the minimum bounding cube and the directional confidence score of the corresponding cluster. The result of multiplication is calculated; then, the weighted lengths of the minimum bounding cubes of all clusters are summed, and the summation result is recorded as the representative value corresponding to the horizontal axis. The long side of the minimum bounding cube is parallel to the horizontal axis of the three-dimensional space. The weighted widths of the minimum bounding cubes of all clusters are summed, and the summation result is recorded as the representative value corresponding to the vertical axis. The width side of the minimum bounding cube is parallel to the vertical axis of the three-dimensional space. The weighted heights of the minimum bounding cubes of all clusters are summed, and the summation result is recorded as the representative value corresponding to the vertical axis. The height side of the minimum bounding cube is parallel to the vertical axis of the three-dimensional space. The specific expression for the representative value corresponding to the horizontal axis is:

[0036]

[0037] Where L is the representative value corresponding to the horizontal axis, and V0 is the total number of clusters. Let be the length of the minimum bounding cube of the j-th cluster. The direction confidence score is the value of the j-th cluster. The larger the value of L, the greater the participation of the length of the minimum bounding cube of the j-th cluster when calculating L. It is positively correlated with L, and the expressions for other representative values ​​are similar.

[0038] Then, the horizontal, vertical, and axial directions are sorted in descending order of their representation values. The sorting results are recorded as a direction sequence. The first direction in the direction sequence is selected as the main scanning direction, the second direction as the secondary scanning direction, and the third direction as the final scanning direction. For example, if the direction sequence is {vertical axis, horizontal axis, vertical axis}, then the vertical axis is the main scanning direction, the horizontal axis is the secondary scanning direction, and the vertical axis is the final scanning direction. The direction with the larger the representation value, the closer it is to the overall data or grayscale distribution trend in space. When performing subsequent scanning traversal, using the direction with the largest representation value as the main scanning direction will result in a relatively larger number of consecutive repetitions or consecutive similar points in the final sequence compared to using other directions as the main scanning direction.

[0039] The compressed storage management module 03 is used to scan and encode the data points in the three-dimensional space according to the main scanning direction, the secondary scanning direction, and the final scanning direction to obtain compressed data, and to store and manage the compressed data.

[0040] After obtaining the main direction, secondary direction, and final direction of the scan traversal, the data points in the three-dimensional space are scanned and encoded according to these directions to obtain compressed data. The specific process of scanning and encoding the data points in the three-dimensional space according to these directions to obtain compressed data is as follows:

[0041] The scanning sequence is obtained by scanning the data points in 3D space according to the main scanning direction, secondary scanning direction, and final scanning direction. The specific process is as follows: Using the center point of the coordinate axes in 3D space as the starting point (the origin of 3D space), the data points in 3D space are scanned sequentially along the main scanning direction. When the scan reaches the end of the sequence, the scanning position is shifted one unit length in the secondary scanning direction, and the process continues along the... The scanning traversal continues along the main direction to scan and traverse the data points in the three-dimensional space. The process of scanning and traversing along the main direction and translating along the secondary direction is repeated until the secondary direction is translated to the end and the main direction is scanned to the end. Then, the scanning position is translated one unit length towards the end of the scanning traversal, and the scanning traversal continues along the main direction to scan and traverse the data points in the three-dimensional space. The entire process is repeated until all data points in the three-dimensional space have been scanned and traversed. The data points in the three-dimensional space are then arranged according to the order of the scanning traversal, and the resulting sequence is recorded as the scanning sequence.

[0042] To illustrate the scanning traversal, if the video to be compressed contains 3 frames, each with 3 rows and 3 columns, and the main scanning direction, secondary scanning direction, and final scanning direction are the horizontal axis, vertical axis, and longitudinal axis respectively, and the data point corresponding to the origin in 3D space is mapped from the bottom left pixel of the first frame of the video to be compressed, then starting from the origin, the scanning traversal will first complete the first row of the first frame, the first row of the second frame, and the first row of the third frame of the video to be compressed in sequence. Then, the scanning traversal will complete the second row of the first frame, the second row of the second frame, and the second row of the third frame of the video to be compressed in sequence. Finally, the scanning traversal will complete the third row of the first frame, the third row of the second frame, and the third row of the third frame of the video to be compressed in sequence. Arranging the data points in 3D space according to the order in which they are scanned yields the scanning sequence.

[0043] After obtaining the scanning sequence, a grayscale value sequence to be differentiated is obtained based on the scanning sequence. The grayscale value sequence to be differentiated is a sequence composed of the grayscale values ​​corresponding to each data point in the scanning sequence. That is, the g-th grayscale value in the grayscale value sequence to be differentiated is the grayscale value corresponding to the g-th data point in the scanning sequence. Then, the grayscale value sequence to be differentiated is processed by difference to obtain the difference sequence of the grayscale value sequence to be differentiated. The result of subtracting the h+1-th data from the h-th data in the grayscale value sequence to be differentiated is the h-th data in the difference sequence. Then, run-length encoding is performed on the difference sequence of the grayscale value sequence to be differentiated, and the obtained encoded data is recorded as compressed data. In this embodiment, the scanning traversal primary, secondary and final directions determined by the above process can make the degree of continuous repetition or continuous similarity of the obtained scanning sequence as high as possible. Because there will be many similar but not identical continuous data in the grayscale value sequence to be differentiated, the above difference processing can convert more grayscale similar data into grayscale identical data, which can further improve the compression effect.

[0044] After obtaining the compressed data of the video to be compressed, this embodiment can store and manage the compressed data of the video to be compressed.

[0045] Thus, this embodiment completes the compression and storage management of the video to be compressed, and improves the compression effect, thereby achieving the purpose of saving storage space and reducing storage costs, that is, achieving the purpose of effective compression and storage management of video data.

[0046] In summary, this embodiment includes a video mapping module for acquiring the video to be compressed and mapping each frame of the video to a three-dimensional space to obtain data points in the three-dimensional space; a scanning traversal direction acquisition module for obtaining the metric distance between data points in the three-dimensional space based on the spatial distance and grayscale difference between the data points, and clustering the data points in the three-dimensional space according to the metric distance to obtain each cluster; obtaining the main scanning traversal direction, secondary scanning traversal direction, and final scanning traversal direction based on the number of neighboring data points of each cluster, the side length of the minimum bounding cube of each cluster, and the proportion of data points within the cluster in the corresponding minimum bounding cube; and a compression storage management module for scanning traversal encoding the data points in the three-dimensional space according to the main scanning traversal direction, secondary scanning traversal direction, and final scanning traversal direction to obtain compressed data, and storing and managing the compressed data. Furthermore, the main direction, secondary direction, and final direction of the scanning traversal determined by the cluster distribution characteristics in this embodiment can ensure that the continuous repetition or continuous similarity in the sequence obtained by the scanning traversal is relatively high, thereby improving the video compression storage management effect.

[0047] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A management platform with local audio and video storage management function, characterized in that, The management platform with local audio and video storage management functions includes: The video mapping module is used to acquire the video to be compressed and map each frame of the video to be compressed into a three-dimensional space to obtain data points in the three-dimensional space. The scanning traversal direction acquisition module is used to obtain the metric distance between data points in the three-dimensional space based on the spatial distance and grayscale difference between data points in the three-dimensional space, and to cluster the data points in the three-dimensional space according to the metric distance between data points in the three-dimensional space to obtain each cluster. Based on the number of neighboring data points of the data points in each cluster, the side length of the minimum bounding cube of each cluster, and the proportion of data points within the cluster in the corresponding minimum bounding cube, the main scanning traversal direction, the secondary scanning traversal direction, and the final scanning traversal direction are obtained. The compressed storage management module is used to scan and encode data points in the three-dimensional space according to the main scanning direction, secondary scanning direction, and final scanning direction to obtain compressed data, and to store and manage the compressed data.

2. The management platform with local audio and video storage management function as described in claim 1, characterized in that, The method for obtaining the metric distance between data points in the three-dimensional space includes: For data points a1 and a2 in the three-dimensional space, the normalized result of the Euclidean distance between data points a1 and a2 is recorded as the first distance value, the normalized result of the absolute value of the grayscale difference between data points a1 and a2 is recorded as the second distance value, and the sum of the first distance value and the second distance value is used as the metric distance between data points a1 and a2.

3. A management platform with local audio and video storage management function as described in claim 1, characterized in that, The method for obtaining the main scanning traversal direction, the secondary scanning traversal direction, and the final scanning traversal direction includes: Based on the number of neighboring data points of each data point in each cluster and the proportion of intra-cluster data points in the minimum bounding cube of each cluster, the directional confidence of each cluster is obtained. The length of the minimum bounding cube of each cluster is parallel to the horizontal axis of the three-dimensional space, the width of the minimum bounding cube is parallel to the vertical axis of the three-dimensional space, and the height of the minimum bounding cube is parallel to the vertical axis of the three-dimensional space. Based on the direction confidence corresponding to each cluster and the side length of the minimum circumscribed cube of each cluster, the main scan traversal direction, the secondary scan traversal direction, and the final scan traversal direction are obtained.

4. The management platform with local audio and video storage management function as described in claim 3, characterized in that, The method for obtaining the directional confidence corresponding to each cluster includes: Based on the ratio of the total number of data points in each cluster to the minimum bounding cube volume of the corresponding cluster, and the total number of data points belonging to the preset neighborhood of the data point and belonging to the same cluster as the corresponding data point, the filling degree characterization value and the intra-cluster distribution density characterization value of each cluster are obtained. The sum of the filling degree characterization value of each cluster and the intra-cluster distribution density characterization value of the corresponding cluster is recorded as the orientation confidence of the corresponding cluster.

5. A management platform with local audio and video storage management function as described in claim 4, characterized in that, The ratio of the total number of data points in each cluster to the volume of the smallest bounding cube of the corresponding cluster is the fill factor of the corresponding cluster.

6. The management platform with local audio and video storage management function as described in claim 4, characterized in that, The method for obtaining the cluster distribution density characterization value includes: The total number of data points within the preset neighborhood of the data point is recorded as the total neighborhood number value. The total number of data points belonging to the preset neighborhood of the data point and belonging to the same cluster as the corresponding data point is recorded as the intra-cluster neighborhood number value of the corresponding data point. The ratio of the intra-cluster neighborhood number value of the data point to the total neighborhood number value is recorded as the intra-cluster neighborhood proportion of the corresponding data point. The mean of the intra-cluster neighborhood proportions of all data points in the cluster is recorded as the intra-cluster distribution density characterization value of the corresponding cluster.

7. A management platform with local audio and video storage management function as described in claim 3, characterized in that, Based on the direction confidence corresponding to each cluster and the side length of the minimum bounding cube of each cluster, a method for scanning the main direction, the secondary direction, and the final direction is obtained, including: The sum of the weighted lengths of the minimum bounding cubes of each cluster is recorded as the representative value corresponding to the horizontal axis. The sum of the weighted widths of the minimum bounding cubes of each cluster is recorded as the representative value corresponding to the vertical axis. The sum of the weighted heights of the minimum bounding cubes of each cluster is recorded as the representative value corresponding to the vertical axis. The weighted length of the minimum bounding cube of any cluster is the result of multiplying the length of the minimum bounding cube of the corresponding cluster by the directional confidence of the corresponding cluster. The weighted width of the minimum bounding cube of any cluster is the result of multiplying the width of the minimum bounding cube of the corresponding cluster by the directional confidence of the corresponding cluster. The weighted height of the minimum bounding cube of any cluster is the result of multiplying the height of the minimum bounding cube of the corresponding cluster by the directional confidence of the corresponding cluster. The horizontal, vertical, and axial directions are sorted in descending order of their representation values ​​to obtain a direction sequence. The first direction in the direction sequence is taken as the main direction of the scan traversal, the second direction is taken as the secondary direction of the scan traversal, and the third direction is taken as the final direction of the scan traversal.

8. A management platform with local audio and video storage management function as described in claim 1, characterized in that, A method for obtaining compressed data by scanning and encoding data points in the three-dimensional space according to the main scanning direction, secondary scanning direction, and final scanning direction includes: The scanning sequence is obtained by scanning the data points in the three-dimensional space according to the main scanning direction, the secondary scanning direction, and the final scanning direction. The sequence of gray values ​​corresponding to each data point in the scanning sequence is denoted as the gray value sequence to be differentiated. Run-length encoding is performed on the differential sequence of the gray value sequence to be differentiated to obtain compressed data.

9. A management platform with local audio and video storage management function as described in claim 8, characterized in that, A method for obtaining a scan sequence by scanning data points in the three-dimensional space according to the main scan direction, secondary scan direction, and final scan direction includes: Using the origin in the three-dimensional space as the starting point of the scan traversal, the data points in the three-dimensional space are scanned sequentially along the main scan traversal direction. When the scan traversal reaches the end, the scan position is shifted one unit length in the secondary scan traversal direction, and the scan traversal of the data points in the three-dimensional space continues along the main scan traversal direction. This process of scanning traversing along the main scan traversal direction and shifting to the secondary scan traversal direction is repeated until the secondary scan traversal direction reaches the end. Then, the scan traversal position is shifted one unit length in the final scan traversal direction, and the scan traversal of the data points in the three-dimensional space continues along the main scan traversal direction. This process is repeated until all data points in the three-dimensional space have been scanned. The sequence obtained by arranging the data points in the three-dimensional space according to the order of the scan traversal is recorded as the scan sequence.

10. A management platform with local audio and video storage management function as described in claim 1, characterized in that, The horizontal axis of the three-dimensional space represents the horizontal pixel position within a single frame image, the vertical axis represents the vertical pixel position within a single frame image, and the vertical axis represents the sequence number of the image in the video to be compressed.

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