CN118741139A - Data transmission system and method based on video coding real-time compression technology - Google Patents
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Abstract
The invention provides a data transmission system and a data transmission method based on video coding real-time compression technology, wherein the system comprises the following steps: digital high definition camera: acquiring video data and analyzing video content information of the video data; network switch: transmitting video data to the video light-weight device according to the video content information; video light-weight device: receiving video data transmitted by a network switch, and driving a convolutional neural network to compress the video data by adopting a nonlinear transformation method to obtain compressed video data; the compressed video data is sent to a network switch; and (3) a display platform: and receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data. So as to solve the technical problems of complex existing video data transmission system, tension of video transmission data bandwidth, high video transmission distortion rate and the like.
Description
Technical Field
The invention belongs to the technical field of power, and relates to a data transmission system and method based on a video coding real-time compression technology.
Background
The current new technologies of '5g+ultra-high definition+artificial intelligence' and the like bring about a new and significant original innovation, and video coding is being deeply transformed as one of the core research topics, and is moving toward intellisation (intellectualization), diversity (diversity) and customizable (configurable). The end-to-end video coding based on the neural network technology is used as the front-edge crossing key research field of video big data and artificial intelligence, breaks through the existing video coding research thought from multiple layers of algorithms, models, implementation and the like, and establishes a brand-new research paradigm.
The high-definition video compression technology is mainly applied to the following fields: video transmission, video monitoring, video storage optimization, and the like. These fields have high requirements for video storage and transmission efficiency, and therefore efficient high-definition video compression technology is required.
The background subtraction technique can achieve the purpose of filtering some useless information in the video and compressing the volume of video data. In practice, the background subtraction technology can be applied to various fields, for example, in the monitoring field, the background subtraction technology can filter static background in a monitoring picture, and the target to be monitored is presented in a dynamic form, so that the video transmission speed can be greatly improved, and the cost of data transmission can be reduced. In addition, in the post-processing process of the movie, the background subtraction technology can be used for image fusion, and a plurality of lenses respectively shot are fused into one image, so that the movie picture effect is more vivid, and the immersion of the audience is enhanced.
The video light weight aims at establishing a neural network structure targeting global rate distortion optimization, and training a highly nonlinear model realizes optimal compact representation under signal fidelity measure. In this respect, the method proposed in recent years is usually cut in from the angles of complex network model design, intra-frame inter-frame coding and the like, so that the model compression efficiency is greatly improved, and the rate distortion performance is superior to that of the traditional hybrid coding method based on rule design.
The existing video compression method is generally based on global consistency assumption, ignores local texture change of an image, lacks content self-adaption, and is insufficient in exploration of intelligent hardware coding methods and system research. Background subtraction techniques have different results in different fields, for example, in complex contexts, there may be erroneous judgment in the background subtraction techniques, which results in filtering out the target, and in complex scenes such as marine patrol, there may be a certain limitation in the background subtraction techniques due to rapid changes in the environment. In addition, when the background subtraction technique is used, there may be some erroneous judgment, for example, when a person enters the screen from behind the camera, it may be erroneously judged as a moving object. Some products are deployed in series in a deployment mode, the front end performs compression coding, the compression coding is transmitted back to the rear end for decoding, the normal network architecture can be influenced after the products fail, the network is paralyzed, and the original video cannot be watched normally. Some compression effects which cannot achieve the requirement can be subjected to lossy compression, and the lossy compression mode is realized by deleting some unimportant information, so that smaller storage space or transmission bandwidth is occupied. However, the data obtained in this way cannot be perfectly restored, frames can be lost, and the precision of the video can be lost.
Disclosure of Invention
The invention provides a data transmission system and a method based on a video coding real-time compression technology, which are used for solving the technical problems of complex existing video data transmission system, tension of video transmission data bandwidth, high video transmission distortion rate and the like.
The invention adopts the following technical scheme:
the invention provides a data transmission system based on video coding real-time compression technology, which comprises:
Digital high definition camera: acquiring video data and analyzing video content information of the video data, wherein the video content information comprises coding format, frame number, resolution, audio frequency and pixel information;
network switch: transmitting video data to video light-weight equipment according to the video content information at a first fixed transmission rate;
video light-weight device: receiving video data transmitted by a network switch, and driving a convolutional neural network to compress the video data by adopting a nonlinear transformation method to obtain compressed video data; transmitting the compressed video data to a network switch according to a second fixed transmission rate, wherein the second fixed transmission rate is smaller than the first fixed transmission rate;
And (3) a display platform: and receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data.
Optionally, the system further comprises:
video storage structure: the method is used for extracting the compressed video data transmitted by the network switch and storing the compressed video data.
Optionally, the video light-weight device comprises:
An image preprocessing module: carrying out gray scale processing on each frame of image of the video data, and converting the color image into a gray scale image; carrying out Gaussian blur on the gray level image to obtain a smooth image; calculating an image gradient based on the smooth image to obtain an edge amplitude value and an angle of the image; according to the amplitude and the angle of the image edge, adopting non-maximum signal pressing processing to obtain an image with thinned edges; binarizing the image with the thinned edge, and outputting a binarized gray level image; converting the binarized data into a color image and outputting the color image;
and a data compression module: the color image output by the image preprocessing module is used for driving the convolutional neural network to predict inter-frame data in a non-linear transformation method; weighting the predicted inter-frame data to synthesize a predicted block; and (3) carrying out target tracking on the prediction block by adopting an optical flow method, and reserving a target area.
Optionally, the image preprocessing module includes: and Gao Silv wave submodule for carrying out weighted average again on each pixel point in the gray image to eliminate noise.
Optionally, the data compression module: and the network switch is also used for receiving the compressed video data transmitted by the network switch, and superposing the compressed video data and the audio data to obtain superposed video data.
Optionally, the data compression module: and the method is also used for integrating and outputting the superimposed video data according to H.264/H.265 codes.
Optionally, the video light-weight device further comprises a prediction distortion rate statistics sub-module for counting intra-frame inter-frame prediction distortion rates, the prediction distortion rates determining whether to adaptively select intra-frame inter-frame combined predictions.
Optionally, the video light-weight device is linked as a bypass between the network switch and the display platform.
The invention also provides a data transmission method based on the video coding real-time compression technology, which comprises the following steps:
Acquiring video data and analyzing video content information of the video data, wherein the video content information comprises coding format, frame number, resolution, audio frequency and pixel information;
transmitting video data to video light-weight equipment according to the video content information at a first fixed transmission rate;
Receiving video data transmitted by a network switch, and driving a convolutional neural network to compress the video data by adopting a nonlinear transformation method to obtain compressed video data; transmitting the compressed video data to a network switch according to a second fixed transmission rate, wherein the second fixed transmission rate is smaller than the first fixed transmission rate;
And receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the data transmission method based on the video coding real-time compression technology according to the method embodiment when being executed by a processor.
The beneficial effects of the invention are as follows:
The invention mainly adopts video light equipment as a bypass between a network switch and a display platform, receives video data transmitted by the network switch, and adopts a nonlinear transformation method to drive a convolutional neural network to realize video data compression, so as to obtain compressed video data; and transmitting the compressed video data to a network switch according to a second fixed transmission rate, introducing a visual artificial intelligence deep learning algorithm in the coding prediction and decoding recovery stage based on the coding of H.264/H.265, and reducing the coded code stream. On the basis of not changing the original network link, resolution, frame number and coding format, the video is lightened to 30% -50% of the original storage.
In the video coding process, firstly, converting video data into multi-frame image data, and carrying out image segmentation and edge detection on each image data to improve the accuracy of target area detection;
in addition, the picture segmentation and the video coding are seamlessly integrated, so that the prediction accuracy is improved, and the code rate is reduced;
The data transmission system based on the video coding real-time compression technology can realize an end-to-end optimized image compression algorithm.
Drawings
Fig. 1 is a schematic diagram of a data transmission system based on a video coding real-time compression technique according to the present invention;
Fig. 2 is a schematic diagram of steps of a data transmission method based on a video coding real-time compression technique according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
The invention provides a data transmission system based on video coding real-time compression technology, as shown in figure 1, comprising:
Digital high definition camera: acquiring video data and analyzing video content information of the video data, wherein the video content information comprises coding format, frame number, resolution, audio frequency and pixel information;
in one embodiment, the digital high-definition camera collects information of surrounding rings, such as video data of road traffic pedestrians, and the video collection scene is not limited. Analyzing the collected video data to obtain video content information: encoding format, frame number, resolution, audio, pixel information, etc., and transmitting the video data to the network switch at a 4M rate transmission rate. The audio adopts a transparent transmission mode, and is directly combined with the video in the restoration stage without processing.
Network switch: transmitting video data to video light-weight equipment according to the video content information at a first fixed transmission rate;
In one embodiment, the network switch receives video data and selects an appropriate transmission standard based on the parsed video content information to transmit the video data to the video light-weight device at a first fixed transmission rate, such as a 4M rate, which may be determined based on the actual transmission rate of the video data, without limitation.
Video light-weight device: receiving video data transmitted by a network switch, and driving a convolutional neural network to compress the video data by adopting a nonlinear transformation method to obtain compressed video data; transmitting the compressed video data to the network switch according to a second fixed transmission rate, wherein the second fixed transmission rate is smaller than the first fixed transmission rate, and the second fixed transmission rate is 0.8Mbps as shown in fig. 1;
In one embodiment, the present embodiment converts the conventional video compression into converting the video data into the image data by adopting the deep learning technology, and designs a new image compression method, the data compression essentially uses the minimum information entropy to integrally describe the discrete data, the design compression algorithm depends on the analysis of the data probability structure, the various existing video compression schemes are still further optimized based on the JPEG/HEVC framework, and the image video codec of the present embodiment establishes a new video codec framework, and the image coding standard thereof is:
After converting video data into image data, carrying out reference pixel learning on each frame of image data to obtain optimal reference pixels;
The target area in the image, the unchanged area, the area formed by the boundary of the target area such as a person or a vehicle moving in the image, is divided by using blocks with any shape, the area division breaks through the traditional rectangular division, and the boundary of the target can be accurately divided;
The new nonlinear transformation method is designed to drive the convolutional neural network to realize video data compression, and the compressed data is output, wherein the distribution of the compressed data is closer to Gaussian distribution, and an image compression algorithm capable of performing end-to-end optimization is designed by combining a quantization technology, a self-coding network and a countermeasure generation network.
Optionally, in the process of compressing the video data, the video light-weight device is mainly implemented by an image preprocessing module and a data compression module, and specifically:
An image preprocessing module: carrying out gray scale processing on each frame of image of the video data, and converting the color image into a gray scale image; carrying out Gaussian blur on the gray level image to obtain a smooth image; calculating an image gradient based on the smooth image to obtain an edge amplitude value and an angle of the image; according to the amplitude and the angle of the image edge, adopting non-maximum signal pressing processing to obtain an image with thinned edges; binarizing the image with the thinned edge, and outputting a binarized gray level image; converting the binarized data into a color image and outputting the color image;
Optionally, the video light-weight device further includes a prediction distortion rate statistics sub-module, wherein the color image output after the image preprocessing is subjected to intra-frame inter-prediction according to the AI model, different weight ratios corresponding to the intra-frame prediction and the inter-frame prediction are output, the information of the frames is integrated in a best mode, the intra-frame inter-prediction distortion rate after the integration is counted, and whether the next step of self-adaptive selection of intra-frame inter-frame combined prediction is determined according to the prediction distortion rate. In a specific embodiment, when the obtained predicted distortion rate is smaller than the predicted distortion rate threshold, intra-frame inter-frame combined prediction is selected, and otherwise, the original video frame is adopted for processing.
Optionally, the image preprocessing module includes: and Gao Silv wave submodule for carrying out weighted average again on each pixel point in the gray image to eliminate noise.
In one embodiment, the Gaussian filter is a linear smoothing filter, which is suitable for eliminating Gaussian noise and is widely applied to noise reduction processes of image processing. The gaussian filtering is a process of performing weighted average on the whole image, and the value of each pixel point is obtained by performing weighted average on the pixel point and other pixel values in the neighborhood. The specific operations of gaussian filtering are: each pixel in the image is scanned with a template (or convolution, mask), and the value of the center pixel point of the template is replaced with the weighted average gray value of the pixels in the neighborhood determined by the template.
And a data compression module: the color image output by the image preprocessing module is used for driving the convolutional neural network to predict inter-frame data in a non-linear transformation method; weighting the predicted inter-frame data to synthesize a predicted block; and (3) carrying out target tracking on the prediction block by adopting an optical flow method, and reserving a target area.
In one embodiment, an optical flow method is adopted to complete inter-frame tracking, a motion vector and a residual error are separated, traditional translational motion is broken through, rotation, scaling and nonlinear prediction are added in the process, and a regional division fine degree intra-frame prediction algorithm is combined, so that details of a target region are reserved, and the target detection rate is improved.
Optionally, the data compression module: and the network switch is also used for receiving the compressed video data transmitted by the network switch, and superposing the compressed video data and the audio data to obtain superposed video data.
Optionally, the data compression module: and the method is also used for integrating and outputting the superimposed video data according to H.264/H.265 codes.
And (3) a display platform: and receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data.
Optionally, the system further comprises: video storage structure: the method is used for extracting the compressed video data transmitted by the network switch and storing the compressed video data.
Optionally, the video light-weight device is linked as a bypass between the network switch and the display platform, as also shown in fig. 1.
In one embodiment, the video light-weight device is connected with the network switch, then the original video is accessed into the video light-weight device, and then the light-weight video is accessed into the existing video platform for display through the switch.
Implementing and selecting a digital high-definition camera which needs to be light-weighted, wherein the resolution is 1080P, the code rate is 4M, and the coding format is H264; the video code stream after light weight is about 0.8Mbps, the smoothness of video transmission after light weight is unchanged, video information is unchanged, storage and bandwidth are optimized, and the video after light weight is stored.
And accessing the light video stream into a sea-health video recorder through onvif protocol, and performing display storage and video viewing. Accessing camera parameters: resolution 1080P, 4M bandwidth, coding format H264, video protocol ONVIF.
The invention also provides a data transmission method based on the video coding real-time compression technology, as shown in fig. 2, comprising the following steps:
Step S201, obtaining video data and analyzing video content information of the video data, wherein the video content information comprises coding format, frame number, resolution, audio frequency and pixel information;
step S202, video data is sent to video light-weight equipment according to video content information at a first fixed transmission rate;
Step 203, receiving video data transmitted by a network switch, and adopting a nonlinear transformation method to drive a convolutional neural network to compress the video data so as to obtain compressed video data; transmitting the compressed video data to a network switch according to a second fixed transmission rate, wherein the second fixed transmission rate is smaller than the first fixed transmission rate;
step S204, receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data.
It should be noted that, the steps of the data transmission method based on the video coding real-time compression technology, which can be implemented by the embodiment of the present invention, are consistent with those of the data transmission method based on the video coding real-time compression technology, which can be implemented by the data transmission system based on the video coding real-time compression technology, and are not described herein again.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the data transmission method based on the video coding real-time compression technology according to the method embodiment when being executed by a processor.
In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the data transmission method of the above embodiments, which are based on video encoding real-time compression techniques.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A data transmission system based on video coding real-time compression technology, comprising:
Digital high definition camera: acquiring video data and analyzing video content information of the video data, wherein the video content information comprises coding format, frame number, resolution, audio frequency and pixel information;
Network switch: transmitting the video data to video light-weight equipment according to the video content information at a first fixed transmission rate;
Video light-weight device: receiving video data transmitted by the network switch, and adopting a nonlinear transformation method to drive a convolutional neural network to compress the video data so as to obtain compressed video data; transmitting the compressed video data to the network switch according to a second fixed transmission rate, wherein the second fixed transmission rate is smaller than the first fixed transmission rate;
and (3) a display platform: and receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data.
2. The data transmission system based on video coding real-time compression technique according to claim 1, further comprising:
video storage structure: and the compressed video data is used for extracting the compressed video data transmitted by the network switch and storing the compressed video data.
3. A data transmission system based on video coding real-time compression technique according to claim 1, wherein the video lightening device comprises:
An image preprocessing module: carrying out gray scale processing on each frame of image of the video data, and converting a color image into a gray scale image; carrying out Gaussian blur on the gray level image to obtain a smooth image; calculating the image gradient based on the smooth image to obtain the edge amplitude and angle of the image; according to the image edge amplitude and angle, adopting non-maximum signal suppression processing to obtain an image with thinned edges; binarizing the image of the thinned edge, and outputting a binarized gray level image; converting the binarized data into a color image and outputting the color image;
and a data compression module: the color image output by the image preprocessing module is used for driving a convolutional neural network to predict inter-frame data in a non-linear transformation method; weighting the predicted inter-frame data to synthesize a predicted block; and carrying out target tracking on the prediction block by adopting an optical flow method, and reserving a target area.
4. A data transmission system based on video coding real-time compression technique as claimed in claim 3, wherein the image preprocessing module comprises: and the Gao Silv wave submodule is used for carrying out weighted average on each pixel point in the gray image again and eliminating noise.
5. A data transmission system based on video coding real-time compression technology as claimed in claim 3, wherein the data compression module: and the network switch is also used for receiving the compressed video data transmitted by the network switch, and superposing the compressed video data and the audio data to obtain superposed video data.
6. The data transmission system based on video coding real-time compression technology as claimed in claim 5, wherein the data compression module: and the video data after superposition is integrated and output according to H.264/H.265 codes.
7. The data transmission system based on the video coding real-time compression technique according to claim 1, wherein the video lightweight device further comprises a prediction distortion rate statistics sub-module for counting intra-frame inter-frame prediction distortion rates, and the prediction distortion rates determine whether to adaptively select inter-frame combined prediction.
8. The data transmission system based on video coding real-time compression technique according to claim 1, wherein the video lightweight device is connected as a bypass link between the network switch and the display platform.
9. A data transmission method based on video coding real-time compression technology, comprising:
Acquiring video data and analyzing video content information of the video data, wherein the video content information comprises coding format, frame number, resolution, audio frequency and pixel information;
transmitting the video data to video light-weight equipment according to the video content information at a first fixed transmission rate;
Receiving video data transmitted by the network switch, and adopting a nonlinear transformation method to drive a convolutional neural network to compress the video data so as to obtain compressed video data; transmitting the compressed video data to the network switch according to a second fixed transmission rate, wherein the second fixed transmission rate is smaller than the first fixed transmission rate;
and receiving the compressed video data transmitted by the network switch, superposing the compressed video data and the audio data, and displaying the superposed video data.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of a method for data transmission based on video encoding real-time compression techniques as claimed in claim 9.
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