A method, device, equipment and medium for lossless transmission of light field video

Abstract

The present invention relates to a lossless transmission method, device, equipment and medium of a light field video, and belongs to the field of video transmission. The method comprises acquiring a light field video through a light field camera, compressing the light field video to acquire a compressed light field image; transmitting the compressed light field image to a receiving end and acquiring a reconstructed light field image through reconstruction; processing the reconstructed light field image through an improved deblurring method and acquiring a lossless light field video. The present invention realizes fast, lossless and efficient transmission of light field videos.

Description

Technical Field

[0001] The present invention belongs to the technical field of video transmission, and in particular relates to a lossless transmission method, device, equipment and medium for light field video. Background Art

[0002] In recent years, the concept of the Metaverse has rapidly emerged, integrating multiple new technologies and forming a new Internet application and social form that integrates the virtual and the real. This technological integration provides an opportunity to build independent social relationships and financial systems, opening up a new future prospect. The development of the Metaverse has greatly expanded the human sensory dimension, and users can get a fully immersive experience.

[0003] As one of the important elements in building the metaverse world, the reconstruction and transmission of light field videos still face many challenges. The amount of data in light field videos is huge, requiring a lot of storage and processing resources. The simultaneous transmission and display of light field videos requires a high-bandwidth and low-latency network, which requires the support of rapidly developing 5G technology and high-speed Internet infrastructure. In order for users to have a fully immersive experience, it is necessary to solve the high-precision reconstruction of light field videos. The reconstructed light field videos may still have low resolution, and effective means are needed to deblur them. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a lossless transmission method, device, equipment and medium for light field video.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for lossless transmission of light field video, the implementation of the lossless transmission method comprising the following steps:

[0007] S1: Acquire a light field video through a light field camera, and compress the light field video to acquire a compressed light field image;

[0008] S2: Transmitting the compressed light field image to a receiving end and obtaining a reconstructed light field image through reconstruction;

[0009] S3: Processing the reconstructed light field image by an improved deblurring method to obtain a lossless light field video.

[0010] Preferably, the step S1 specifically includes:

[0011] S101: Dividing the light field video into four-dimensional light field images by frame;

[0012] S102: Acquire a central sub-aperture image of the four-dimensional light field image, wherein the central sub-aperture image contains a reference cluster center set;

[0013] S103: Acquire pixel points of the four-dimensional light field image;

[0014] S104: Obtaining a pixel weight distance of the four-dimensional light field image;

[0015] S105: Compress the four-dimensional light field image according to the pixel weight distance to obtain the compressed light field image.

[0016] Preferably, the step S104 specifically includes:

[0017] S104-1: converting the four-dimensional light field image into a CIELAB color space, and calculating a color distance according to a color channel;

[0018] S104-2: Calculating the position distance according to the reference cluster center set;

[0019] S104-3: Calculating the disparity distance according to the reference cluster center set;

[0020] S104-4: Calculate the pixel weight distance according to the color distance, the position distance, and the parallax distance.

[0021] Preferably, the step S2 specifically includes:

[0022] S201: Obtain a light field depth map of a compressed light field image and obtain depth values ​​of pixels in the light field depth map;

[0023] S202: Acquire reconstructed pixel point coordinates according to the light field depth map, where the reconstructed pixel point coordinates include a first reconstructed coordinate component, a second reconstructed coordinate component, and a third reconstructed coordinate component;

[0024] S203: Reconstruct each pixel point of the compressed light field image according to the reconstructed pixel point coordinates and obtain the reconstructed light field image.

[0025] Preferably, the step S202 specifically includes:

[0026] S202-1: The calculation formula of the first reconstruction coordinate component is: , where h1 is the first reconstruction coordinate component, b is the baseline, f is the focal length, I is the central sub-aperture image area, g is the focusing distance, M is the depth value of the pixel point in the light field depth map, and s is the sensor size;

[0027] S202-2: The calculation formula of the second reconstruction coordinate component is: , where h2 is the second reconstruction coordinate component, I' is the light field depth map area, I is the central sub-aperture image area, s is the sensor size, h1 is the first reconstruction coordinate component, and f is the focal length;

[0028] S202-3: The calculation formula of the third reconstruction coordinate component is: , where h3 is the second reconstruction coordinate component, I'' is the compressed light field image area, I is the central sub-aperture image area, s is the sensor size, h1 is the first reconstruction coordinate component, and f is the focal length.

[0029] Preferably, the step S3 specifically includes:

[0030] S301: Acquire the two-dimensional pixel coordinates of the reconstructed light field image;

[0031] S302: Obtain a reflected image of the reconstructed light field image based on a surround function, wherein the expression of the reflected image is: , where T is the reflected image and U is the number of surround functions. is the u-th surround function weight, I is the reconstructed light field image, g is the surround function, (o, s) is the two-dimensional pixel coordinate;

[0032] S303: Optimizing the reconstructed light field image according to the reflected image to obtain a first optimized image, where the expression of the first optimized image is: , where I' is the first optimized image, I is the reconstructed light field image, and T is the reflected image;

[0033] S304: Acquire a lossless light field image based on the first optimized image;

[0034] S305: Collecting the lossless light field images to obtain the reconstructed light field video.

[0035] Preferably, the step S304 specifically includes:

[0036] S304-1: Obtain the coordinates of the central pixel point of the first optimized image and calculate the Euclidean distance coefficient and the difference coefficient. The calculation formula of the Euclidean distance coefficient is: , where θ is the Euclidean distance coefficient, (o, s) is the two-dimensional pixel coordinate, (v, z) is the center pixel coordinate, is the standard deviation of the surround function;

[0037] The calculation formula of the difference coefficient is: ,in, is the difference coefficient, I' is the first optimized image, (o, s) is the two-dimensional pixel coordinate, (v, z) is the center pixel coordinate, is the standard deviation of the surround function, ||·|| is the Euclidean distance;

[0038] S304-2: Calculate the lossless image coefficient according to the Euclidean distance coefficient and the difference coefficient, and the calculation formula is: ,in, is the lossless image coefficient, θ is the Euclidean distance coefficient, is the difference coefficient, (o,s) is the two-dimensional pixel coordinate;

[0039] S304-3: Calculate lossless pixel values ​​according to the lossless image coefficients. The calculation formula of the lossless pixel values ​​is: , where E is the lossless pixel value, E(q,r) is the pixel value of the two-dimensional pixel point (q,r), is the lossless image coefficient, (o, s) is the two-dimensional pixel coordinate, (q, r) is located in the (2&+1)×(2&+1) area centered at (o, s), and & is the filter window parameter;

[0040] S304-4: Optimize each pixel point of the first optimized image according to the lossless pixel value to obtain the lossless light field image.

[0041] A lossless transmission device for light field video, used to execute the above-mentioned lossless transmission method, characterized by comprising a video compression module, an image reconstruction module, and a lossless processing module;

[0042] The video compression module is used to obtain a light field video through a light field camera, and compress the light field video to obtain a compressed light field image;

[0043] The image reconstruction module is used to transmit the compressed light field image to the receiving end and obtain a reconstructed light field image through reconstruction;

[0044] The lossless processing module is used to process the reconstructed light field image by an improved deblurring method and obtain a lossless light field video.

[0045] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned lossless transmission method when executing the program.

[0046] A storage medium containing computer executable instructions, wherein the computer executable instructions are used to perform the above-mentioned lossless transmission method when executed by a computer processor.

[0047] The beneficial effects of the present invention are:

[0048] (1) By compressing light field videos, the storage space, processing resources, and bandwidth required for transmission and display of light field videos are reduced, which is conducive to the development of the metaverse world.

[0049] (2) Reconstructing light field video to restore the loss during compression and improve user experience;

[0050] (3) Obtain lossless light field video by improving the deblurring method, solve the problem that the reconstructed light field video may still have low resolution, and improve the accuracy of the light field video;

[0051] (4) The light field video is divided into four-dimensional light field images by frame, and the pixel weight distance is obtained through the pixel points of the four-dimensional light field image and the reference cluster center, and the light field video is compressed. The compression process is more streamlined, more efficient, and has better compression effect;

[0052] (5) By converting the four-dimensional light field image into the CIELAB color space and calculating the color distance according to the color channel, the position distance and the parallax distance are calculated at the same time, and the pixel weight distance is obtained through the above distances, which makes the calculation simpler and more efficient;

[0053] (6) Reconstruct each pixel of the compressed light field image by reconstructing the pixel coordinates and obtaining the reconstructed light field image, thereby reducing the workload of the reconstruction process and improving work efficiency;

[0054] (7) A reflection image of the reconstructed light field image is obtained through a surround function, and the reconstructed light field image is optimized according to the reflection image to obtain a first optimized image and a lossless light field image. The lossless light field images are combined to obtain a reconstructed light field video, thereby achieving deblurring of the light field video and effectively increasing the resolution of the light field video. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0056] Figure 1 The present invention is a flowchart of the steps of a lossless transmission method of light field video. DETAILED DESCRIPTION

[0057] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0058] The working principle and use process of the present invention:

[0059] See also Figure 1 , a lossless transmission method of light field video, comprising:

[0060] S1: Acquire a light field video through a light field camera, and compress the light field video to acquire a compressed light field image;

[0061] S2: Transmitting the compressed light field image to a receiving end and obtaining a reconstructed light field image through reconstruction;

[0062] S3: Processing the reconstructed light field image by an improved deblurring method to obtain a lossless light field video.

[0063] In this embodiment, a light field video is acquired by a light field camera, and the light field video is compressed to acquire a compressed light field image, which can be specifically implemented by the following steps:

[0064] S101: Dividing the light field video into four-dimensional light field images by frame;

[0065] S102: Acquire a central sub-aperture image of the four-dimensional light field image, wherein the central sub-aperture image contains a reference cluster center set, wherein the reference cluster center set includes a first reference cluster center, a second reference cluster center, ..., an nth reference cluster center;

[0066] S103: Acquire pixel points of the four-dimensional light field image, where the pixel points include a first pixel point, a second pixel point, ..., an Nth pixel point;

[0067] S104: Acquire a pixel weight distance of the four-dimensional light field image, where the pixel weight distance is a weight distance from the pixel point to the nearest reference cluster center;

[0068] S105: Compressing the four-dimensional light field image according to the pixel weight distance to obtain the compressed light field image. The compression process is more streamlined, more efficient, and has a better compression effect.

[0069] In this embodiment, the pixel weight distance of the four-dimensional light field image is obtained, and the pixel weight distance is the weight distance from the pixel point to the nearest reference cluster center, which can be specifically implemented by the following steps:

[0070] S104-1: Convert the four-dimensional light field image into a CIELAB color space, and calculate the color distance according to the color channels, wherein the color channels include a lightness channel, a first color channel, and a second color channel. The first color channel is a color change from red to dark green, and the second color channel is a color change from blue to yellow. The calculation formula of the color distance is:

[0071] ,

[0072] Among them, D1 is the color distance, L i is the brightness channel value of the i-th pixel, L j is the brightness channel value of the j-th reference cluster center, A i is the first color channel value of the i-th pixel, A j is the first color channel value of the jth reference cluster center, B i is the second color channel value of the i-th pixel, L j is the second color channel value of the jth reference cluster center, N is the number of pixels, and n is the number of reference cluster centers;

[0073] S104-2: Calculate the position distance, the calculation formula is:

[0074] ,

[0075] Where D2 is the position distance, E ix is the horizontal coordinate of the i-th pixel, E iy is the ordinate of the i-th pixel, F jx is the horizontal coordinate of the jth reference cluster center, F jy is the ordinate of the jth reference cluster center, S is the area of ​​the four-dimensional light field image, N is the number of pixels, and n is the number of reference cluster centers;

[0076] S104-3: Calculate the parallax distance, the calculation formula is:

[0077] ,

[0078] Among them, D3 is the parallax distance, is the average horizontal disparity between the pixel and the reference cluster center, is the average vertical disparity between the pixel and the reference cluster center;

[0079] S104-4: Calculating the pixel weight distance according to the color distance, the position distance, and the parallax distance. The calculation is simpler and more efficient. The calculation formula is:

[0080] ,

[0081] Where D is the pixel weight distance, is the color distance weight, is the location distance weight, is the parallax distance weight, D1 is the color distance, D2 is the position distance, and D3 is the parallax distance;

[0082] In this embodiment, the compressed light field image is transmitted to the receiving end and a reconstructed light field image is obtained by reconstruction, which can be specifically implemented by the following steps:

[0083] S201: Obtain a light field depth map of a compressed light field image and obtain depth values ​​of pixels in the light field depth map;

[0084] S202: Acquire reconstructed pixel point coordinates according to the light field depth map, where the reconstructed pixel point coordinates include a first reconstructed coordinate component, a second reconstructed coordinate component, and a third reconstructed coordinate component;

[0085] S203: Reconstruct each pixel point of the compressed light field image according to the reconstructed pixel point coordinates and obtain the reconstructed light field image, thereby reducing the workload of the reconstruction process and improving work efficiency.

[0086] In this embodiment, the reconstructed pixel point coordinates are obtained according to the light field depth map, and the reconstructed pixel point coordinates include a first reconstructed coordinate component, a second reconstructed coordinate component, and a third reconstructed coordinate component, which can be specifically implemented by the following steps:

[0087] S202-1: The calculation formula of the first reconstructed coordinate component is:

[0088] ,

[0089] Wherein, h1 is the first reconstruction coordinate component, b is the baseline, f is the focal length, I is the central sub-aperture image area, g is the focusing distance, M is the depth value of the pixel point in the light field depth map, and s is the sensor size;

[0090] S202-2: The calculation formula of the second reconstructed coordinate component is:

[0091] ,

[0092] Wherein, h2 is the second reconstruction coordinate component, I' is the area of ​​the light field depth map, I is the area of ​​the central sub-aperture image, s is the sensor size, h1 is the first reconstruction coordinate component, and f is the focal length;

[0093] S202-3: The calculation formula of the third reconstruction coordinate component is:

[0094] ,

[0095] Wherein, h3 is the second reconstruction coordinate component, I'' is the compressed light field image area, I is the central sub-aperture image area, s is the sensor size, h1 is the first reconstruction coordinate component, and f is the focal length;

[0096] In this embodiment, the reconstructed light field image is processed by an improved deblurring method to obtain a lossless light field video, which can be specifically implemented by the following steps:

[0097] S301: Acquire the two-dimensional pixel coordinates of the reconstructed light field image;

[0098] S302: Obtaining a reflected image of the reconstructed light field image based on a surround function, wherein the surround function includes a first surround function, a second surround function, ..., a Uth surround function, and an expression of the reflected image is:

[0099] ,

[0100] Where T is the reflected image, U is the number of surround functions, is the u-th surround function weight, I is the reconstructed light field image, g is the surround function, (o, s) is the two-dimensional pixel coordinate;

[0101] S303: Optimizing the reconstructed light field image according to the reflected image to obtain a first optimized image, where the expression of the first optimized image is:

[0102] ,

[0103] Wherein, I' is the first optimized image, I is the reconstructed light field image, and T is the reflected image;

[0104] S304: Acquire a lossless light field image based on the first optimized image;

[0105] S305: The lossless light field images are collected to obtain the reconstructed light field video, thereby deblurring the light field video and effectively increasing the resolution of the light field video.

[0106] In this embodiment, acquiring a lossless light field image based on the first optimized image can be specifically implemented by the following steps:

[0107] S304-1: Obtain the coordinates of the central pixel point of the first optimized image and calculate the Euclidean distance coefficient and the difference coefficient. The calculation formula of the Euclidean distance coefficient is:

[0108] ,

[0109] Among them, θ is the Euclidean distance coefficient, (o, s) is the two-dimensional pixel coordinate, (v, z) is the center pixel coordinate, is the standard deviation of the surround function;

[0110] The calculation formula of the difference coefficient is:

[0111] ,

[0112] in, is the difference coefficient, I' is the first optimized image, (o, s) is the two-dimensional pixel coordinate, (v, z) is the center pixel coordinate, is the standard deviation of the surround function;

[0113] S304-2: Calculate the lossless image coefficient according to the Euclidean distance coefficient and the difference coefficient, and the calculation formula is:

[0114] ,

[0115] in, is the lossless image coefficient, θ is the Euclidean distance coefficient, is the difference coefficient, (o,s) is the two-dimensional pixel coordinate;

[0116] S304-3: Calculate a lossless pixel value according to the lossless image coefficient. The lossless pixel value is the pixel value of the two-dimensional pixel point (o, s) after being processed by the improved deblurring method. The calculation formula of the lossless pixel value is:

[0117] ,

[0118] Among them, E is the lossless pixel value, E(q,r) is the pixel value of the two-dimensional pixel point (q,r), is the lossless image coefficient, (o, s) is the two-dimensional pixel coordinate, (q, r) is located in the (2&+1)×(2&+1) area centered at (o, s), and & is the filter window parameter;

[0119] S304-4: Optimize each pixel point of the first optimized image according to the lossless pixel value to obtain the lossless light field image.

[0120] A lossless transmission device for light field video, comprising a video compression module, an image reconstruction module, and a lossless processing module;

[0121] The video compression module is used to obtain a light field video through a light field camera, and compress the light field video to obtain a compressed light field image;

[0122] The image reconstruction module is used to transmit the compressed light field image to the receiving end and obtain a reconstructed light field image through reconstruction;

[0123] The lossless processing module is used to process the reconstructed light field image by an improved deblurring method and obtain a lossless light field video.

[0124] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0125] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0126] The program code contained on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0127] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A lossless transmission method for light field video, characterized in that: The implementation of the lossless transmission method comprises the following steps: S1: Acquire a light field video through a light field camera, and compress the light field video to acquire a compressed light field image; S2: Transmitting the compressed light field image to a receiving end and obtaining a reconstructed light field image through reconstruction; S3: Processing the reconstructed light field image by an improved deblurring method to obtain a lossless light field video; S301: Acquire the two-dimensional pixel coordinates of the reconstructed light field image; S302: Obtain a reflected image of the reconstructed light field image based on a surround function, wherein the expression of the reflected image is: , where T is the reflected image and U is the number of surround functions. is the u-th surround function weight, I is the reconstructed light field image, g is the surround function, (o, s) is the two-dimensional pixel coordinate; S303: Optimizing the reconstructed light field image according to the reflected image to obtain a first optimized image, where the expression of the first optimized image is: , where I' is the first optimized image, I is the reconstructed light field image, and T is the reflected image; S304: Acquire a lossless light field image based on the first optimized image; S304-1: Obtain the coordinates of the central pixel point of the first optimized image and calculate the Euclidean distance coefficient and the difference coefficient. The calculation formula of the Euclidean distance coefficient is: , where θ is the Euclidean distance coefficient, (o, s) is the two-dimensional pixel coordinate, (v, z) is the center pixel coordinate, is the standard deviation of the surround function; The calculation formula of the difference coefficient is: ,in, is the difference coefficient, I' is the first optimized image, (o, s) is the two-dimensional pixel coordinate, (v, z) is the center pixel coordinate, is the standard deviation of the surround function, ||·|| is the Euclidean distance; S304-2: Calculate the lossless image coefficient according to the Euclidean distance coefficient and the difference coefficient, and the calculation formula is: ,in, is the lossless image coefficient, θ is the Euclidean distance coefficient, is the difference coefficient, (o,s) is the two-dimensional pixel coordinate; S304-3: Calculate lossless pixel values ​​according to the lossless image coefficients. The calculation formula of the lossless pixel values ​​is: , where E is the lossless pixel value, E(q,r) is the pixel value of the two-dimensional pixel point (q,r), is the lossless image coefficient, (o, s) is the two-dimensional pixel coordinate, (q, r) is located in the (2&+1)×(2&+1) area centered at (o, s), and & is the filter window parameter; S304-4: Optimize each pixel point of the first optimized image according to the lossless pixel value to obtain the lossless light field image; S305: Collect the lossless light field images to obtain a reconstructed light field video.

2. The lossless transmission method according to claim 1, characterized in that: The step S1 specifically includes: S101: Dividing the light field video into four-dimensional light field images by frame; S102: Acquire a central sub-aperture image of the four-dimensional light field image, wherein the central sub-aperture image contains a reference cluster center set; S103: Acquire pixel points of the four-dimensional light field image; S104: Obtaining a pixel weight distance of the four-dimensional light field image; S105: Compress the four-dimensional light field image according to the pixel weight distance to obtain the compressed light field image.

3. The lossless transmission method according to claim 2, characterized in that: The step S104 specifically includes: S104-1: converting the four-dimensional light field image into a CIELAB color space, and calculating a color distance according to a color channel; S104-2: Calculating the position distance according to the reference cluster center set; S104-3: Calculating the disparity distance according to the reference cluster center set; S104-4: Calculate the pixel weight distance according to the color distance, the position distance, and the parallax distance.

4. The lossless transmission method according to claim 1, characterized in that: The step S2 specifically includes: S201: Obtain a light field depth map of a compressed light field image and obtain depth values ​​of pixels in the light field depth map; S202: Acquire reconstructed pixel point coordinates according to the light field depth map, where the reconstructed pixel point coordinates include a first reconstructed coordinate component, a second reconstructed coordinate component, and a third reconstructed coordinate component; S203: Reconstruct each pixel point of the compressed light field image according to the reconstructed pixel point coordinates and obtain the reconstructed light field image.

5. The lossless transmission method according to claim 4, characterized in that: The step S202 specifically includes: S202-1: The calculation formula of the first reconstruction coordinate component is: , where h1 is the first reconstruction coordinate component, b is the baseline, f is the focal length, I is the central sub-aperture image area, g is the focusing distance, M is the depth value of the pixel point in the light field depth map, and s is the sensor size; S202-2: The calculation formula of the second reconstruction coordinate component is: , where h2 is the second reconstruction coordinate component, I' is the light field depth map area, I is the central sub-aperture image area, s is the sensor size, h1 is the first reconstruction coordinate component, and f is the focal length; S202-3: The calculation formula of the third reconstruction coordinate component is: Wherein, h3 is the second reconstruction coordinate component, I'' is the compressed light field image area, I is the central sub-aperture image area, s is the sensor size, h1 is the first reconstruction coordinate component, and f is the focal length.

6. A light field video lossless transmission device using the light field video lossless transmission method according to claim 1, characterized in that: Including video compression module, image reconstruction module and lossless processing module; The video compression module is used to obtain a light field video through a light field camera, and compress the light field video to obtain a compressed light field image; The image reconstruction module is used to transmit the compressed light field image to the receiving end and obtain a reconstructed light field image through reconstruction; The lossless processing module is used to process the reconstructed light field image by an improved deblurring method and obtain a lossless light field video.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the lossless transmission method as described in any one of claims 1-5 is implemented.

8. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the lossless transmission method as claimed in any one of claims 1 to 5 when executed by a computer processor.

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