Video lossless compression encoding method and system based on spatio-temporal information

Abstract

The present invention discloses a video lossless compression encoding method and system based on spatio-temporal information. The method includes: S1: For each frame image in the video, distinguish the foreground and the background to obtain foreground units; S2: Connect the foreground units in the image to obtain a foreground region; S3: Construct a foreground region offset matrix along the time sequence; S4: Based on the foreground region offset matrix, sample and select the images in the video along the time sequence; S5: In the images obtained by sampling and selecting in step S4, sample and select the pixel units in the foreground region, and retain the amplitudes of the pixel units that are not sampled and selected; S6: Use sparse coding to encode the images obtained in step S5. Based on the spatio-temporal information of video data, the present invention effectively realizes video lossless compression in multiple scenarios by using a sampling method; by utilizing the video context correlation, the encoding efficiency is improved, and a high compression ratio is obtained with a low computational complexity.

Description

Technical Field

[0001] The present invention relates to the technical field of video compression, and in particular, to a video lossless compression encoding method and system based on spatio-temporal information. Background Art

[0002] Video lossless compression requires compressing video images while ensuring video quality, reducing the storage space of video files. Lossless compression of video helps improve storage efficiency and retrieval efficiency, facilitating long-term and large-scale preservation of videos, as well as retrieval management. Video lossless compression technology ensures that the effective information in the video is not lost and can be widely applied in medical records, legal evidence collection, and road monitoring fields.

[0003] Existing video lossless compression technologies mainly start from removing redundant information in video data, and use encoding methods to encode and store the effective data in the video to ensure that the compressed video can be restored to its original state. Common encoding methods include predictive coding and transform coding. Among them, predictive coding reduces the amount of data by predicting pixel values in video frames. Due to the existence of prediction errors, the restoration of the video is affected. The transform coding method performs frequency domain transformation on video data, involving complex operations and predictions, and is limited in application. Summary of the Invention

[0004] In view of this, the present invention provides a video lossless compression encoding method and system based on spatio-temporal information, which integrates spatio-temporal information into the video lossless compression encoding method and utilizes the context correlation of video frames, which is beneficial to improving the encoding efficiency.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0006] A video lossless compression encoding method based on spatio-temporal information includes the following steps:

[0007] S1: For each frame image in the video, distinguish the foreground and the background to obtain foreground units;

[0008] S2: Connect the foreground units in the image to obtain a foreground region;

[0009] S3: Calculate the pixel unit index where the centroid of the foreground region is located; and based on the pixel unit index where the centroid is located, construct a spatio-temporal matrix of the foreground region, and then calculate the offset speed of the foreground region; then based on the offset speed of the foreground region, construct a foreground region offset matrix along the time series;

[0010] S4: Based on the foreground region offset matrix, sample and select the images in the video along the time series;

[0011] S5: In the image obtained by sampling and selecting in step S4, sample and select pixel units in the foreground region, and retain the amplitudes of the pixel units not sampled and selected.

[0012] S6: Encode the image obtained in step S5 using sparse coding.

[0013] Optionally, in step S1, for each frame image in the video, distinguish the foreground and the background to obtain foreground units, including:

[0014] S11: For each frame image in the video, construct a two-dimensional matrix, where the elements are represented as:

[0015] ;

[0016] Among them, represents the element with index in the two-dimensional matrix, represents the index of the pixel unit in the horizontal axis direction in the image, represents the index of the pixel unit in the vertical axis direction in the image, represents the amplitude of the pixel unit;

[0017] S12: Scan the two-dimensional matrix to find the peak, and the index of the pixel unit where the peak is located is ;

[0018] S13: Based on the two-dimensional matrix, estimate the image background noise:

[0019] ;

[0020] Among them, represents the estimated value of the image background noise, represents the number of pixel units in the image;

[0021] S14: Filter the peak obtained in step S12 to determine the foreground units:

[0022] ;

[0023] Among them, indicates that the pixel unit where the peak is located belongs to the foreground unit, indicates that the pixel unit where the peak is located belongs to the background unit, represents the detection scale factor.

[0024] Optionally, in step S2, connect the foreground units in the image to obtain the foreground region, including:

[0025] Starting from any foreground unit in the image, use the breadth-first algorithm to connect the foreground units to obtain the foreground region.

[0026] Optionally, in step S3, calculate the pixel unit index where the centroid of the foreground region is located; and based on the pixel unit index where the centroid is located, construct a spatio-temporal matrix of the foreground region, and then calculate the offset velocity of the foreground region; then, based on the offset velocity of the foreground region, construct a foreground region offset matrix along the time series, including:

[0027] S31: Calculate the pixel unit index where the centroid of the foreground region is located;

[0028] S32: Based on the pixel unit index where the centroid is located, construct a spatio-temporal matrix of the foreground region:

[0029] ;

[0030] where, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the -th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the -th frame image, represents the frame sequence of the image;

[0031] S33: Calculate the offset velocity of the foreground region:

[0032] ;

[0033] where, represents the offset velocity of the foreground region in the horizontal axis direction in the -th frame image, represents the offset velocity of the foreground region in the vertical axis direction in the -th frame image, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the -th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the -th frame image;

[0034] S34: Along the time series, construct a foreground region offset matrix:

[0035] .

[0036] Optionally, in step S31, calculating the pixel unit index where the centroid of the foreground region is located includes:

[0037] S311: Calculate the centroid position of the foreground region:

[0038] ;

[0039] where, represents the position of the centroid of the foreground region in the horizontal axis direction in the -th frame image, represents the position of the centroid of the foreground region in the vertical axis direction in the -th frame image, represents the number of units in the foreground region in the -th frame image, represents the index of each unit in the foreground region in the horizontal axis direction in the -th frame image, represents the index of each unit in the foreground region in the vertical axis direction in the -th frame image;

[0040] S312: The index of the pixel unit where the centroid is located is:

[0041] ;

[0042] wherein, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the -th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the -th frame image, represents rounding to the nearest integer.

[0043] Optionally, in step S4, sampling and selecting images in the video along the time series based on the foreground region offset matrix includes:

[0044] Determining the images to be sampled and selected:

[0045] ;

[0046] wherein, represents that the image is sampled and selected, represents that the image is not sampled and selected, represents the offset threshold for sampling and selection.

[0047] Optionally, in step S5, in the images obtained by sampling and selecting in step S4, sampling and selecting pixel units in the foreground region and retaining the amplitudes of the pixel units not sampled and selected includes:

[0048] S51: In the images obtained by sampling and selecting in step S4, determining the edge of the foreground region includes:

[0049] S511: In the images obtained by sampling and selecting in step S4, retaining the pixel amplitudes of the foreground units and setting the amplitudes of the remaining pixel units to 0;

[0050] S512: Traverse the image along the horizontal axis, find the minimum and maximum horizontal indices of the pixel units with non-zero amplitudes, and determine the horizontal edges of the target.

[0051] S513: Traverse the image along the vertical axis, find the minimum and maximum vertical indices of the pixel units with non-zero amplitudes, and determine the vertical edges of the target.

[0052] S52: Starting from the pixel units at the edge of the foreground region, continuously extract at intervals of the sampling factor to obtain the sampled pixel units, set the amplitudes of the sampled pixel units to 0, and retain the amplitudes of the non-sampled pixel units.

[0053] When sampling and selecting pixel units, the sampling factor is set to:

[0054] ;

[0055] where is the scale factor.

[0056] Optionally, in step S6, sparse coding is used to encode the image obtained in step S5, including:

[0057] Use sparse coding to encode the image obtained in step S5;

[0058] ;

[0059] where represents the sparse coding result, represents the index of the pixel units with non-zero amplitudes in the horizontal axis direction of the image, represents the index of the pixel units with non-zero amplitudes in the vertical axis direction of the image, represents the frame sequence of the image obtained in step S5, represents the amplitude of the pixel units with non-zero amplitudes in the image.

[0060] The present invention also provides a video lossless compression coding system based on spatio-temporal information, including:

[0061] Foreground unit recognition module: Construct a two-dimensional matrix of the image, find the peaks, estimate the background noise of the image, and determine the foreground units;

[0062] Foreground region determination module: Connect the foreground units to obtain the foreground region;

[0063] Foreground region offset matrix module: Calculate the centroid of the foreground region, construct the spatio-temporal matrix of the foreground region, calculate the offset speed of the foreground region, and construct the foreground region offset matrix;

[0064] Image sampling module: Determine the sampled image;

[0065] Foreground region sampling module: Determine the foreground region edge and sample and select the pixel units inside the foreground region;

[0066] Sparse coding module: Perform sparse coding on the image.

[0067] Beneficial effects:

[0068] Based on the spatio-temporal information of video data, the present invention effectively realizes lossless compression of videos in multiple scenarios by using a sampling method; by utilizing the context correlation of videos, the coding efficiency is improved, and a high compression ratio is obtained with a low computational complexity.

[0069] The method of constructing a matrix based on video images in the present invention is conducive to accurately representing and managing video data, provides convenience for subsequent compression coding, and simplifies the processing flow; differentiating the foreground and background of video images is conducive to removing redundant background information, allocating more storage resources to foreground information, and realizing effective compression of foreground information; adopting different sampling methods for the foreground region contour and the interior of the foreground region respectively is conducive to accurately coding the target; sampling the video image along the time axis is conducive to increasing the compression ratio.

[0070] On the basis of obtaining foreground units by using the method of combining peak detection and filtering in the present invention, through connectivity processing, it is conducive to obtaining the overall region of interest and avoiding repeated operation processing on the key area of concern; by calculating the centroid of the foreground region, it is conducive to sampling the video image according to the motion law of the foreground region, conducive to discriminating redundant information, and realizing lossless compression; the setting of the internal sampling factor of the foreground region is proportional to the size of the foreground region, which is conducive to increasing the sampling rate of small regions. Description of the drawings

[0071] Figure 1 It is a schematic flowchart of a method for lossless compression coding of videos based on spatio-temporal information provided by an embodiment of the present invention.

[0072] Figure 2 It is an image obtained by performing connectivity processing on the foreground units in the image in step S2 of an embodiment of the present invention.

[0073] Figure 3 It is an image obtained by performing sampling and selection processing on the foreground region in step S5 of an embodiment of the present invention. Specific implementation manners

[0074] The present invention will be further described below with reference to the drawings, but the present invention is not limited in any way. Any transformation or replacement made based on the teachings of the present invention falls within the protection scope of the present invention.

[0075] Embodiment 1:

[0076] A video lossless compression coding method based on spatio-temporal information, as Figure 1 shown, includes the following steps:

[0077] S1: For each frame image in the video, distinguish the foreground and the background to obtain foreground units, including:

[0078] S11: For each frame image in the video, construct a two-dimensional matrix, and the elements therein are represented as:

[0079] ;

[0080] Among them, represents the element with index in the two-dimensional matrix, represents the index of the pixel unit in the horizontal axis direction in the image, represents the index of the pixel unit in the vertical axis direction in the image, represents the amplitude of the pixel unit;

[0081] S12: Scan the two-dimensional matrix to find the peak, and the index of the pixel unit where the peak is located is ;

[0082] S13: Based on the two-dimensional matrix, estimate the image background noise:

[0083] ;

[0084] Among them, represents the estimated value of the image background noise, represents the number of pixel units in the image;

[0085] S14: Filter the peak obtained in step S12 to determine the foreground unit:

[0086] ;

[0087] Among them, indicates that the pixel unit where the peak is located belongs to the foreground unit, indicates that the pixel unit where the peak is located belongs to the background unit, represents the detection scale factor.

[0088] In the embodiments of the present invention, the foreground refers to valuable information, and the background refers to clutter or content that is not the key concern and can be ignored in video compression; for example, in vehicle tracking, the vehicle is the foreground, and the road and roadside environment are the background; in a pedestrian flow monitoring video, people are the foreground and the environment is the background.

[0089] S2: Connect the foreground units in the image to obtain a foreground region, including:

[0090] Starting from any foreground unit in the image, using the breadth-first algorithm to connect the foreground units to obtain the foreground region.

[0091] In the embodiment of the present invention, Figure 2 Shown is the image obtained by performing the connection process on the foreground units in the image in step S2 (due to privacy concerns regarding portrait rights, the face in the image is blurred); it can be seen that Figure 2 the person in the image is selected, and the overall contour is continuous, and the background region is represented by blurring.

[0092] S3: Calculate the pixel unit index where the centroid of the foreground region is located; and based on the pixel unit index where the centroid is located, construct the spatio-temporal matrix of the foreground region, and then calculate the offset speed of the foreground region; then, based on the offset speed of the foreground region, construct the foreground region offset matrix along the time series, including:

[0093] S31: Calculate the pixel unit index where the centroid of the foreground region is located, including:

[0094] S311: Calculate the centroid position of the foreground region:

[0095] ;

[0096] Wherein, represents the position of the centroid of the foreground region in the horizontal axis direction in the th frame image, represents the position of the centroid of the foreground region in the vertical axis direction in the th frame image, represents the number of units in the foreground region in the th frame image, represents the index of each unit in the foreground region in the horizontal axis direction in the th frame image, represents the index of each unit in the foreground region in the vertical axis direction in the th frame image;

[0097] S312: The pixel unit index where the centroid is located is:

[0098] ;

[0099] Wherein, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the th frame image, represents rounding to the nearest integer;

[0100] S32: Based on the pixel unit index where the centroid is located, construct the spatio-temporal matrix of the foreground region:

[0101] ;

[0102] Among them, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the th frame image, represents the frame sequence of the image;

[0103] S33: Calculate the offset velocity of the foreground region:

[0104] ;

[0105] Among them, represents the offset velocity of the foreground region in the horizontal axis direction in the th frame image, represents the offset velocity of the foreground region in the vertical axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the th frame image;

[0106] S34: Along the time series, construct the foreground region offset matrix:

[0107] .

[0108] S4: Based on the foreground region offset matrix, along the time series, sample and select the images in the video, including:

[0109] Determine the sampled and selected images:

[0110] ;

[0111] Among them, indicates that the image is sampled and selected, indicates that the image is not sampled and selected, indicates the offset threshold for sampling and selection.

[0112] S5: In the images obtained by sampling and selection in step S4, sample and select the pixel units of the foreground region, and retain the amplitudes of the pixel units that are not sampled and selected, including:

[0113] S51: In the images obtained by sampling and selection in step S4, determine the edge of the foreground region, including:

[0114] S511: In the image obtained by sampling and selection in step S4, retain the pixel amplitudes of the foreground units, and set the amplitudes of the remaining pixel units to 0;

[0115] S512: Traverse the image along the horizontal axis, find the minimum horizontal axis index and the maximum horizontal axis index where the amplitude of the pixel unit is not 0, and determine the horizontal axis edge of the target;

[0116] S513: Traverse the image along the vertical axis, find the minimum vertical axis index and the maximum vertical axis index where the amplitude of the pixel unit is not 0, and determine the vertical axis edge of the target;

[0117] S52: Use the pixel units at the edge of the foreground area as the starting units, and perform continuous extraction at intervals of the sampling factor to obtain the sampled and selected pixel units. Set the amplitudes of the sampled and selected pixel units to 0, and retain the amplitudes of the pixel units that are not sampled and selected;

[0118] When sampling and selecting pixel units, the sampling factor is set to:

[0119] ;

[0120] where is the scaling factor.

[0121] In the embodiment of the present invention, Figure 3 The figure shows the image obtained by sampling and processing the foreground area in step S5 (due to portrait rights, the face in the image is blurred); the amplitudes of the pixel units in the background area of the figure are 0. In the foreground area, the amplitudes of the sampled and selected pixel units are set to 0; it can be seen that Figure 3 the background area in

[0122] S6: Use sparse coding to encode the image obtained in step S5, including:

[0123] Use sparse coding to encode the image obtained in step S5;

[0124] ;

[0125] where represents the sparse coding result, represents the index of the pixel unit with a non-zero amplitude in the horizontal axis direction of the image, represents the index of the pixel unit with a non-zero amplitude in the vertical axis direction of the image, represents the frame sequence of the image obtained in step S5, represents the amplitude of the pixel unit with a non-zero amplitude in the image.

[0126] Embodiment 2: The present invention also provides a lossless video compression and encoding system based on spatio-temporal information, which includes the following six modules:

[0127] Foreground unit recognition module: Construct a two-dimensional image matrix, find the wave peaks, estimate the image background noise, and determine the foreground units;

[0128] Foreground area determination module: Connect the foreground units to obtain the foreground area;

[0129] Foreground area offset matrix module: Calculate the centroid of the foreground area, construct the spatio-temporal matrix of the foreground area, calculate the offset speed of the foreground area, and construct the foreground area offset matrix;

[0130] Image sampling module: Determine the images selected for sampling;

[0131] Foreground area sampling module: Determine the edges of the foreground area and sample the internal pixel units of the foreground area;

[0132] Sparse coding module: Perform sparse coding on the images.

[0133] It should be noted that the serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments. And the terms "including", "comprising" or any other variant thereof in this article are intended to cover a non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. Without further limitations, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article or method including that element.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0135] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A lossless video compression and encoding method based on spatio-temporal information, characterized in that, The method includes: S1: For each frame image in the video, distinguish the foreground and background to obtain foreground units; S2: Connect the foreground units in the image to obtain a foreground region; S3: Calculate the pixel unit index where the centroid of the foreground region is located; and based on the pixel unit index where the centroid is located, construct a spatio-temporal matrix of the foreground region, and then calculate the offset velocity of the foreground region; then based on the offset velocity of the foreground region, construct a foreground region offset matrix along the time series, including: S31: Calculate the pixel unit index where the centroid of the foreground region is located; S32: Based on the pixel unit index where the centroid is located, construct a spatio-temporal matrix of the foreground region: ； Among them, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the th frame image, represents the frame sequence of the image; S33: Calculate the offset velocity of the foreground region: ； Among them, represents the offset velocity of the foreground region in the horizontal axis direction in the th frame image, represents the offset velocity of the foreground region in the vertical axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the th frame image; S34: Along the time series, construct a foreground region offset matrix: ； S4: Based on the foreground region offset matrix, sample and select images in the video along the time series; S5: In the images obtained by sampling and selecting in step S4, sample and select the pixel units of the foreground region, and retain the amplitudes of the pixel units not sampled and selected; S6: Use sparse coding to encode the images obtained in step S5.

2. The video lossless compression encoding method based on spatio-temporal information according to claim 1, wherein, The step S1 includes: S11: For each frame image in the video, construct a two-dimensional matrix, and the elements therein are represented as: ； Among them, represents the element with index in the two-dimensional matrix, represents the index of the pixel unit in the horizontal axis direction of the image, represents the index of the pixel unit in the vertical axis direction of the image, represents the amplitude of the pixel unit; S12: Scan the two-dimensional matrix to find the peak, and the index of the pixel unit where the peak is located is ; S13: Based on the two-dimensional matrix, estimate the image background noise: ； Among them, represents the estimated value of the image background noise, represents the number of pixel units in the image; S14: Filter the peaks obtained in step S12 to determine the foreground units: ； Among them, indicates that the pixel unit where the wave peak is located belongs to the foreground unit, indicates that the pixel unit where the wave peak is located belongs to the background unit, indicates the detection scale factor.

3. The video lossless compression encoding method based on spatio-temporal information according to claim 2, wherein The step S2 includes: Starting from any foreground unit in the image, use the breadth-first algorithm to connect the foreground units to obtain a foreground region.

4. The video lossless compression and encoding method based on spatio-temporal information according to claim 3, wherein, The step S31 includes: S311: Calculate the centroid position of the foreground region: ； Among them, represents the position of the centroid of the foreground region in the horizontal axis direction in the th frame image, represents the position of the centroid of the foreground region in the vertical axis direction in the th frame image, represents the number of cells in the foreground region in the th frame image, represents the index of each cell in the foreground region in the horizontal axis direction in the th frame image, represents the index of each cell in the foreground region in the vertical axis direction in the th frame image; S312: The pixel unit index where the centroid is located is: ； Among them, represents the index of the pixel unit where the centroid is located in the horizontal axis direction in the th frame image, represents the index of the pixel unit where the centroid is located in the vertical axis direction in the th frame image, means rounding to the nearest integer.

5. The video lossless compression and encoding method based on spatio-temporal information according to claim 3, wherein The step S4 includes: Determine the images sampled and selected: ； Among them, indicates that the image is sampled and selected, indicates that the image is not sampled and selected, indicates the offset threshold for sampling and selection.

6. The video lossless compression encoding method based on spatio-temporal information according to claim 5, characterized in that, The step S5 includes: S51: In the images obtained by sampling and selecting in step S4, determine the edge of the foreground region, including: S511: In the images obtained by sampling and selecting in step S4, retain the pixel amplitudes of the foreground units, and set the amplitudes of the remaining pixel units to 0; S512: Traverse the image along the horizontal axis to find the minimum horizontal axis index and the maximum horizontal axis index where the amplitude of the pixel unit is not 0, and determine the horizontal axis edge of the target; S513: Traverse the image along the vertical axis to find the minimum vertical axis index and the maximum vertical axis index where the amplitude of the pixel unit is not 0, and determine the vertical axis edge of the target; S52: Starting from the pixel units at the edge of the foreground region, perform continuous sampling and selection at intervals of the sampling factor to obtain the sampled and selected pixel units, set the amplitudes of the sampled and selected pixel units to 0, and retain the amplitudes of the pixel units not sampled and selected; When sampling and selecting pixel units, the sampling factor is set to: ； wherein, is a scale factor.

7. The video lossless compression encoding method based on spatio-temporal information according to claim 6, characterized in that The step S6 includes: Use sparse coding to encode the images obtained in step S5; ； Among them, represents the sparse coding result, represents the index of the pixel unit with a non-zero amplitude in the horizontal axis direction in the image, represents the index of the pixel unit with a non-zero amplitude in the vertical axis direction in the image, represents the frame sequence of the image obtained in step S5, represents the amplitude of the pixel unit with a non-zero amplitude in the image.

8. A lossless video compression and encoding system based on spatio-temporal information, characterized in that, Including: Foreground unit recognition module: Construct an image two-dimensional matrix, find peaks, estimate image background noise, and determine foreground units; Foreground region determination module: Connect foreground units to obtain a foreground region; Foreground region offset matrix module: Calculate the centroid of the foreground region, construct a spatio-temporal matrix of the foreground region, calculate the offset velocity of the foreground region, and construct an offset matrix of the foreground region; Image sampling module: Determine the sampled image; Foreground region sampling module: Determine the foreground region edge and sample the internal pixel units of the foreground region; Sparse coding module: Perform sparse coding on the image; To implement a lossless video compression and coding method based on spatio-temporal information as described in any one of claims 1-7.

Images