CN109302614B - Video compression method based on third-order tensor self-coding network - Google Patents

Video compression method based on third-order tensor self-coding network Download PDF

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CN109302614B
CN109302614B CN201811168316.6A CN201811168316A CN109302614B CN 109302614 B CN109302614 B CN 109302614B CN 201811168316 A CN201811168316 A CN 201811168316A CN 109302614 B CN109302614 B CN 109302614B
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刘光灿
李阳
陈胜勇
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Jiangsu Xinshiyun Science And Technology Co ltd
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Abstract

The invention provides a video compression method based on a third-order tensor self-coding network. The self-coding network is widely used in image compression, but a large number of parameters need to be stored and a large number of pictures need to be used for training the network, so that a third-order tensor is used for replacing full connection parameters between layers in the self-coding network, a self-coding mechanism and a back propagation method are used for carrying out iterative solution on the parameters in the network to achieve convergence, a convergence result is coded, and finally a compressed video is obtained.

Description

Video compression method based on third-order tensor self-coding network
Technical Field
The invention relates to a video compression method based on a third-order tensor self-coding network, and belongs to the technical field of video compression.
Background
In recent years, video technology has gained widespread use and growth. By 2020, 80% of internet traffic is video traffic. Uncompressed video, however, occupies a large amount of storage space. At present, there are mainly h.264 and h.265 video stream coding methods and neural network based methods. Based on H.264 and H.265 video stream coding methods, the compression rate is high, but the decompression speed is low; the neural network-based method has relatively high compression efficiency, but needs a large number of images for training, has more network parameters, and needs to occupy more memory space of terminal equipment.
Disclosure of Invention
The invention provides a video compression method based on a third-order tensor self-coding network, which aims to solve the problems in the prior art and has high compression rate and high decompression speed.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a video compression method based on a third-order tensor self-coding network comprises the following steps:
firstly, preprocessing a target video;
setting full connection parameters between layers in a target video self-coding network as third-order tensors and setting iteration ending conditions;
step three, forward transmission of a video network is carried out;
step four, judging whether iteration is terminated, if so, skipping to step six, and outputting a core tensor and decoding network parameters; otherwise, continuing the step five;
fifthly, performing back propagation of the video network;
sixthly, coding and compressing the core tensor and the decoding network;
and step seven, outputting the compressed video.
The technical scheme is further designed as follows: and in the second step, parameters of the network and iteration ending conditions are set according to the required compression ratio and the peak signal-to-noise ratio.
The third step comprises the following specific steps: performing modulo-1 modulo-2 modulo-3 multiplication by using three factor matrixes between a first layer network (input video) and a next layer network, and mapping the result by using a sigmoid function to obtain a new third-order tensor (the next layer network); and respectively carrying out matrix multiplication and tensor multiplication for 5 times in sequence to obtain the final third-order tensor (output video).
The sigmoid function formula is as follows:
Figure GDA0003394228220000011
and the iteration ending condition is that the error or the iteration number reaches an upper limit. When the error value is smaller than the set error value, the iteration is ended; or when the iteration times are more than the set iteration times, ending the iteration.
The back propagation step in the step five is as follows:
step 5.1, solving the gradient from the output layer to the hidden layer;
according to the chain rule we obtain:
Figure GDA0003394228220000021
wherein:
Figure GDA0003394228220000022
Etotalis a tensor self-encoding network loss function, YrealFor input of video, YoutIn order to output the video, the video is output,
Figure GDA0003394228220000023
x is more than or equal to 1 and less than or equal to m, y is more than or equal to 1 and less than or equal to n, z is more than or equal to 1 and less than or equal to p, i is more than or equal to 1 and less than or equal to r, j is more than or equal to 1 and less than or equal to s, k is more than or equal to 1 and less than or equal to t, A, B and C are three factor matrixes; m, r represents the size of matrix A, n, s represents the size of matrix B, p, t represents the size of matrix C, x represents an integer between 1 and m, y represents an integer between 1 and n, z represents an integer between 1 and p, i represents an integer between 1 and r, j represents an integer between 1 and s, k represents an integer between 1 and t, m, n, p, r, s, t belong to positive integers,
Figure GDA0003394228220000024
represents a real number and a real number,
Figure GDA0003394228220000025
represents a derivation symbol;
Figure GDA0003394228220000026
Figure GDA0003394228220000027
Figure GDA0003394228220000028
the same can be obtained:
Figure GDA0003394228220000029
Figure GDA00033942282200000210
step 5.2, solving the gradient from the hidden layer to the hidden layer:
according to the chain rule we obtain:
Figure GDA00033942282200000211
wherein the content of the first and second substances,
Figure GDA00033942282200000212
a is more than or equal to 1 and less than or equal to u, b is more than or equal to 1 and less than or equal to v, and c is more than or equal to 1 and less than or equal to w; u, v, W represent the size of the tensor W, u, v, W are positive integers, a represents an integer between 1 and u, b represents an integer between 1 and v, and c represents an integer between 1 and W.
Solving for the hidden layer to hidden layer gradient is similar to the output layer to hidden layer gradient, except that
Figure GDA0003394228220000031
The solving formula is as follows:
Figure GDA0003394228220000032
wherein the content of the first and second substances,
Figure GDA0003394228220000033
other solving steps are the same as the steps in 1);
Figure GDA0003394228220000034
Figure GDA0003394228220000035
the following can be obtained by the same method:
Figure GDA0003394228220000036
Figure GDA0003394228220000037
step 5.3, the gradient from all hidden layers to the hidden layer is obtained by using the method in the step 5.2
Step 5.4, the tensor self-encoding network parameters are updated using the ADAM (adaptive moment estimation) method and the gradients found in step 5.1, step 5.2 and step 5.3.
The concrete steps of coding and compressing the core tensor and the compression network in the sixth step are as follows:
step 6.1, extracting and separating the integer part and the decimal part of the obtained nuclear tensor and compressed network parameter;
6.2, coding and compressing the integer part by using Huffman coding;
step 6.3, multiplying the separated fractional part by a scalar alpha and rounding the result;
step 6.4, carrying out quantization compression on the integer obtained in the step 6.3 by using a beta bit binary system;
and 6.5, storing the compression results of the integer and the decimal part.
The alpha is 2043, and the beta is 11.
The decompression step in the step 7 is as follows:
step 7.1, inputting the compression result obtained in the step six;
7.2, performing inverse coding on the core tensor and the compression network;
performing inverse Huffman transformation on the integer part to obtain a core tensor and an integer part of a network;
taking out binary compression representation of the decimal part, and dividing the binary compression representation by alpha to obtain a core tensor and a network decimal part;
combining the core tensor and the decimal and integer part of the network to obtain the parameters of the core tensor and the network;
step 7.3, carrying out decompression by using tensor modular multiplication;
and 7.4, outputting the decompressed video.
The invention has the beneficial effects that:
the invention carries out video compression through a tensor self-coding network, and the tensor has the capacity of expressing multi-dimensional complex data, so that the invention has higher compression rate and higher decompression speed than the prior method under the same condition, and can be an algorithm for monitoring video compression.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a back propagation flow chart.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
As shown in fig. 1, the video compression method based on the third-order tensor self-coding network of the present invention specifically includes the following steps:
inputting a target video (three-dimensional) and carrying out normalization and graying processing;
replacing full-connection parameters of the self-coding network with the three factor matrixes and setting iteration conditions;
setting parameters of the network and iteration ending conditions according to the required compression ratio and the peak signal-to-noise ratio; the upper limit of iteration times is set to 10^5, the error value of each pixel value is set to 0.0012, the size of the middle five hidden layers is set according to the compression rate and the peak signal-to-noise ratio, fewer hidden layer nodes are set if a larger compression rate needs to be obtained, and more hidden layer nodes are set if a higher PSNR value is obtained. The number of nodes of the third hidden layer is the minimum, the number of nodes of the first hidden layer is less than that of nodes of the input layer and greater than that of nodes of the second hidden layer, the number of nodes of the first hidden layer is equal to that of nodes of the fifth hidden layer, the number of nodes of the second hidden layer is the same as that of nodes of the fourth hidden layer, and the number of nodes of the input layer is the same as that of nodes of the output layer.
Step three, forward propagation is carried out;
performing modulo-1 modulo-2 modulo-3 multiplication by using three factor matrixes between a first layer network (input video) and a next layer network, and mapping the result by using a sigmoid function to obtain a new third-order tensor (the next layer network); and respectively carrying out matrix multiplication and tensor multiplication for 5 times in sequence to obtain the final third-order tensor (output video). sigmoid formula is as follows:
Figure GDA0003394228220000041
step four, whether an iteration end condition is reached or not is judged, and when the error value is smaller than the set error value, the iteration is ended; or when the iteration times are more than the set iteration times, the iteration is ended; if so, jumping to the sixth step to obtain a coding network and a compression network; otherwise, continuing the step five;
step five, performing reverse propagation, and then returning to the step three; the back propagation step is shown in figure 2,
step 5.1, solving the gradient from the output layer to the hidden layer: the output layer is the last third-order tensor, and except the third-order tensors of the input layer and the output layer, other third-order orderings are all hidden layers.
According to the chain rule we obtain:
Figure GDA0003394228220000051
wherein:
Figure GDA0003394228220000052
Etotalis a tensor self-encoding network loss function, YrealFor input of video, YoutIn order to output the video, the video is output,
Figure GDA0003394228220000053
x is more than or equal to 1 and less than or equal to m, y is more than or equal to 1 and less than or equal to n, z is more than or equal to 1 and less than or equal to p, i is more than or equal to 1 and less than or equal to r, j is more than or equal to 1 and less than or equal to s, k is more than or equal to 1 and less than or equal to t, A, B and C are three factor matrixes, m and r represent the size of the matrix A, n and s represent the size of the matrix B, p and t represent the size of the matrix C, x represents an integer between 1 and m, y represents an integer between 1 and n, and z represents 1 to pI represents an integer from 1 to r, j represents an integer from 1 to s, k represents an integer from 1 to t, and m, n, p, r, s, t are positive integers.
Figure GDA00033942282200000511
Represents a real number and a real number,
Figure GDA00033942282200000512
represents a derivation symbol;
Figure GDA0003394228220000054
Figure GDA0003394228220000055
Figure GDA0003394228220000056
the same can be obtained:
Figure GDA0003394228220000057
Figure GDA0003394228220000058
step 5.2, solving the gradient from the hidden layer to the hidden layer:
Figure GDA0003394228220000059
wherein the content of the first and second substances,
Figure GDA00033942282200000510
a is more than or equal to 1 and less than or equal to u, b is more than or equal to 1 and less than or equal to v, c is more than or equal to 1 and less than or equal to W, u, v and W represent the size of tensor W, u, v and W are positive integers, and a represents an integer between 1 and uB represents an integer between 1 and v, and c represents an integer between 1 and w.
Solving for the hidden layer to hidden layer gradient is similar to the output layer to hidden layer gradient, except that
Figure GDA0003394228220000061
Is solved by the formula
Figure GDA0003394228220000062
Wherein the content of the first and second substances,
Figure GDA0003394228220000063
other solving steps are the same as the steps in 1);
Figure GDA0003394228220000064
Figure GDA0003394228220000065
the following can be obtained by the same method:
Figure GDA0003394228220000066
Figure GDA0003394228220000067
step 5.3, the gradient from all hidden layers to the hidden layer is obtained by using the method in the step 5.2
Step 5.4, updating network parameters:
the tensor self-encoding network parameters are updated using the ADAM method and the gradients found in steps 5.1 to 5.3.
Sixthly, coding and compressing the core tensor and the compression network;
step 6.1, extracting and separating the integer part and the decimal part of the nuclear tensor and compressed network parameters obtained by separation
Step 6.2, using Huffman coding to code and compress the integer part
Step 6.3, multiply the fractional part obtained by separation by scalar alpha and round the result, alpha is 2043
Step 6.4, the integers obtained in the step 3 are quantized and compressed by using a beta bit binary system, wherein beta is 11
And 6.5, storing the compression results of the integer and the decimal part.
Step seven, outputting a compression result;
the decompression step is as follows:
step 7.1, inputting the compression result obtained in the step six;
7.2, performing inverse coding on the core tensor and the compression network;
performing inverse Huffman transform on the integer part in the result to obtain the integer part of the core tensor and the network
Taking out binary compression representation of the fractional part, and dividing the binary compression representation by alpha to obtain a core tensor and a fractional part of the network
Combining the nuclear tensor and the decimal and integer parts of the network to obtain the parameters of the nuclear tensor and the network
Step 7.3, carrying out decompression by using tensor modular multiplication;
and 7.4, outputting the decompressed video.
Table 1 shows some evaluation criteria of the compression result of the embodiment of the method of the present invention, Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and compression Ratio are image quality evaluation criteria, where PSNR and SSIM range from 0 to 1, and the larger the values of the three are, the better the image quality is.
Video name miss-america bridge-close bridge-far claire grandma
PSNR 33.05 37.68 45.48 33.39 33.47
SSIM 0.93 0.97 0.98 0.93 0.90
Compression ratio 112.35 79.35 88.73 183.64 134.59
Table 1 video compression results quantitative analysis table.
The technical solutions of the present invention are not limited to the above embodiments, and all technical solutions obtained by using equivalent substitution modes fall within the scope of the present invention.

Claims (6)

1. A video compression method based on a third-order tensor self-coding network is characterized by comprising the following steps:
firstly, preprocessing graying and normalization of a target three-dimensional video;
setting full connection parameters between layers in a target video self-coding network as third-order tensors and setting iteration ending conditions;
step three, forward transmission of a video network is carried out;
performing modulo-1 modulo-2 modulo-3 multiplication by using three factor matrixes between a first layer network and a next layer network, and mapping the result by using a sigmoid function to obtain a new third-order tensor; respectively and sequentially carrying out matrix multiplication 5 times and tensor multiplication to obtain a final third-order tensor; the input video is a first-layer network, the obtained new third-order tensor is a next-layer network, and the final third-order tensor is the output video;
step four, judging whether iteration is terminated, if so, skipping to step six, and outputting a core tensor and decoding network parameters; otherwise, continuing the step five;
fifthly, performing back propagation of the video network;
sixthly, coding and compressing the core tensor and the decoding network;
step 6.1, extracting and separating the integer part and the decimal part of the obtained nuclear tensor and compressed network parameter;
6.2, coding and compressing the integer part by using Huffman coding;
step 6.3, multiplying the separated fractional part by a scalar alpha, and rounding the result, wherein alpha is 2043;
step 6.4, carrying out quantization compression on the integer obtained in the step 6.3 by using a beta bit binary system, wherein beta is 11;
6.5, storing the compression results of the integer and the decimal part;
and step seven, outputting the compressed video.
2. The video compression method based on the third order tensor self-coding network as recited in claim 1, wherein: and in the second step, parameters of the network and iteration ending conditions are set according to the required compression ratio and the peak signal-to-noise ratio.
3. The video compression method based on the third order tensor self-coding network as recited in claim 1, wherein: the sigmoid function formula is as follows:
Figure FDA0003394228210000011
4. the video compression method based on the third order tensor self-coding network as recited in claim 2, wherein: the end condition of the iteration is as follows: when the error value is smaller than the set error value, the iteration is ended; or when the iteration times are more than the set iteration times, ending the iteration.
5. The video compression method based on the third order tensor self-coding network as recited in claim 1, wherein:
the back propagation step in the step five is as follows:
step 5.1, solving the gradient from the output layer to the hidden layer;
according to the chain rule we obtain:
Figure FDA0003394228210000021
wherein:
Figure FDA0003394228210000022
Etotalis a tensor self-encoding network loss function, YrealFor input of video, YoutIn order to output the video, the video is output,
Figure FDA0003394228210000023
1≤x≤m，1≤y≤n，1≤z≤p，1≤r is more than or equal to i, j is more than or equal to 1 and less than or equal to s, k is more than or equal to 1 and less than or equal to t, A, B and C are three factor matrixes; m, r represents the size of matrix A, n, s represents the size of matrix B, p, t represents the size of matrix C, x represents an integer between 1 and m, y represents an integer between 1 and n, z represents an integer between 1 and p, i represents an integer between 1 and r, j represents an integer between 1 and s, k represents an integer between 1 and t, m, n, p, r, s, t belong to positive integers,
Figure FDA0003394228210000024
represents a real number and a real number,
Figure FDA0003394228210000025
represents a derivation symbol;
Figure FDA0003394228210000026
Figure FDA0003394228210000027
Figure FDA0003394228210000028
the same can be obtained:
Figure FDA0003394228210000029
Figure FDA00033942282100000210
step 5.2, solving the gradient from the hidden layer to the hidden layer:
according to the chain rule we obtain:
Figure FDA00033942282100000211
wherein the content of the first and second substances,
Figure FDA00033942282100000212
a is more than or equal to 1 and less than or equal to u, b is more than or equal to 1 and less than or equal to v, and c is more than or equal to 1 and less than or equal to w; u, v, W represent the size of the tensor W, u, v, W are positive integers, a represents an integer between 1 and u, b represents an integer between 1 and v, and c represents an integer between 1 and W;
Figure FDA00033942282100000213
wherein the content of the first and second substances,
Figure FDA0003394228210000031
Figure FDA0003394228210000032
Figure FDA0003394228210000033
the same can be obtained:
Figure FDA0003394228210000034
Figure FDA0003394228210000035
step 5.3, the gradient from all hidden layers to the hidden layer is obtained by using the method in the step 5.2
Step 5.4, the tensor self-encoding network parameters are updated using the ADAM (adaptive moment estimation) method and the gradients found in step 5.1, step 5.2 and step 5.3.
6. The video compression method based on the third order tensor self-coding network as recited in claim 1, wherein: the decompression step in the step 7 is as follows:
step 7.1, inputting the compression result obtained in the step six;
7.2, performing inverse coding on the core tensor and the compression network;
performing inverse Huffman transformation on the integer part to obtain a core tensor and an integer part of a network;
taking out binary compression representation of the decimal part, and dividing the binary compression representation by alpha to obtain a core tensor and a network decimal part;
combining the core tensor and the decimal and integer part of the network to obtain the parameters of the core tensor and the network;
step 7.3, carrying out decompression by using tensor modular multiplication;
and 7.4, outputting the decompressed video.
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