# Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Authors: Yixin Yang; Zhiqang Xiang; Jianbo Li
Journal: Security and Communication Networks
 (2021)

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## Abstract

When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.


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## Body

## 1. Introduction

With the development of Internet technology, image storage technology and image transmission technology have become the core content of multimedia [1]. With the rapid development of aerospace imaging technology, portable devices, and mobile terminals, low bit rate images are more and more widely used. In the power grid operation site, it is necessary to transmit the video and image of the field operation in the channel with limited bandwidth at low bit rate, but the transmission bandwidth and storage capacity of the video image are difficult to meet the requirements when the definition and resolution of the video image are continuously enhanced. Therefore, it is necessary to compress the low frame rate video animation video [2].In [3], the combination of frame rate conversion technology and new video compression coding standard HEVC is conducive to improve the video compression efficiency. Aiming at the problem that the effect of frame rate up conversion directly using the motion vector of low frame rate video in HEVC bitstream information is not ideal, a video compression algorithm based on the combination of frame rate up conversion and HEVC based on motion vector refinement is proposed in this paper. Firstly, the original video is extracted at the encoding end to reduce the video frame rate. Secondly, the low frame rate video is encoded and decoded by HEVC. Then, combined with the motion vector extracted from the H EVC code stream at the decoding end, the forward and backward joint motion estimation is used to further refine it, so that the refined motion vector is closer to the real motion of the object. Finally, the frame rate up-conversion technology based on motion compensation is used to restore the video sequence to the original frame rate. The experimental results show that compared with HEVC standard, the proposed algorithm can save a certain bit rate under the same video quality. At the same time, compared with other algorithms, under the same bit rate, the average PSNR value of the reconstructed video can be increased by 0.5 dB. However, the proposed algorithm does not compensate the frame rate of the video image, resulting in low definition of the compressed video image. In [4], in order to better encode the depth map in 3D video, this paper introduces the depth modeling mode (DMMs) into the 3D-HEVC standard. The new mode not only improves the coding quality, but also improves the complexity of the original algorithm. When designing the DMM-1 encoder circuit, the coding cycle of the traditional architecture circuit is long; it can only meet the requirements of real-time video coding with low resolution and frame rate. In order to further improve the performance of DMM-1 encoder in 3D-HEVC, this paper studies the DMM-1 algorithm architecture. According to the characteristics that there is no data correlation in wedge block evaluation, a hardware circuit of DMM-1 encoder with 5-stage pipeline architecture is proposed in order to reduce the coding cycle required for a depth block coding. Experiments show that the architecture can reduce the coding cycle by at least 52.3% at the cost of increasing the number of circuit gates by about 1568. However, the algorithm is prone to artifacts in the process of video image compression, resulting in poor quality of compressed video image. In [5], aiming at the problems of high bit error rate and large space occupied in the compression results obtained by using the traditional compression method to compress the video in the playing state, an embedded online video interframe signal adaptive compression method is proposed. The h.264/avc video coding standard is set as the operation constraint of the compression method, and the online video key frames in the playing state are extracted. According to the interframe correlation, the interframe classification is carried out, and the estimation results of online video motion are obtained combined with the classification results of interframe signals. The adaptive compression of online video interframe signals can be realized by installing adaptive compression coding equipment, setting adaptive threshold, and inputting the motion estimation results and extracted video key frames into the encoder. After simulation test and verification, the conclusion is drawn, Compared with the traditional compression methods, the bit error rate of the designed adaptive compression method is reduced, and the space occupied by the video compression result is significantly reduced. However, the compression time of the algorithm under different compression ratios is long, and the compression efficiency is low.Although researchers are still trying to improve its efficiency by fine-tuning its composition and parameters, its basic structure has not changed in the past 20 years. Recently developed deep learning techniques may provide a new direction for building high compression image/video coding systems. Recent results, particularly from CVPR’s Learning Image Compression Challenges (CLIC), suggest that this novel approach, which typically trains end-to-end, may have good potential to further improve compression efficiency [6]. Kulupana et al. [7] propose a probabilistic model that accurately predicts the overall distortion of the decoded video at the encoder followed by an accurate QP-λ relationship, which can be used in the rate-distortion optimization (RDO) process. Kulupana et al. [7] propose a novel RC method which considers distortion characteristics for model parameters updating for quality enhancement, including a low-complexity-based I-frame QP decision strategy of low-delay applications.In order to solve the problems in the above algorithms, a low frame rate video compression algorithm based on linear function model is proposed.The research contributions of this paper include the following aspects:(1)
In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm.(2)
In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation videos.(3)
The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.


## 2. Development of Animation Video in the Context of New Media

In the short decades of the development of new media, the new media platform has become more and more perfect, and new media works have been applied in various fields of people’s life. However, the development of characteristic contents in these works is relatively backward, and the research and practice in this field are still relatively insufficient. Before the arrival of the new media era, two-dimensional animation has its own production process and communication mode. Although the technical support under the background of new media has brought a new revolution to two-dimensional animation, it is difficult for two-dimensional animation works to make a new breakthrough in the traditional form. With the popularization of three-dimensional technology, two-dimensional animation has encountered a bottleneck in its development. The combination of real shooting and two-dimensional animation is a good exploration direction. Elements such as real shooting roles and scenes are highly consistent with two-dimensional animation. The combination of the two breaks the single form of two-dimensional animation and brings more possibilities in artistic form, creative mode, and audience group.The development of science and technology has had a crucial impact on the performance of design. The combination of real images and artistic two-dimensional animation has produced a collision of beauty, giving people a real and illusory visual feeling. With the progress of computer technology, the terminal equipment and media platform used to transmit information are updated and iterated at a very fast speed. Compared with the traditional media such as newspaper, radio, television and film, new media forms such as the Internet, mobile phone, and portable computer are more favored by people. As a unique form of artistic expression, two-dimensional animation has rich and flexible lens language. The infinitely changing visual space and unique artistic conception atmosphere have brought leaps and bounds to the animation industry. The combination of real shooting and two-dimensional animation does not overturn the traditional two-dimensional animation, but inherits the artistic essence of the traditional two-dimensional animation with novelty and creativity. The combination of the two absorbs the advantages brought by new scientific and technological forces and makes full use of the convenience of communication between a variety of new media devices so that two-dimensional animation is widely used in various fields and plays a more and more important role in the new media platform that people are exposed to every day [8].Animation is an artistic style comprehensively displayed by means of modeling, action, language, painting, color, and sound. Its unique artistic form and expression bring people a dreamlike feeling. At present, animation has gradually penetrated into every link of human life with its fascinating magic [9]. New media is a new media form under network technology, computer technology, and multimedia technology, such as digital magazine, mobile TV, virtual university, online blog, virtual studio, and so on. In addition to appearing as an independent art form, animation, as an integral part of new media art, complements with network interaction, video, environment, and media to form a richer new art form.There are various standards for animation classification, which can be divided into cinema animation, TV animation, and network animation. In terms of form and subject matter, animation can be divided into artistic animation, scientific animation, entertainment animation, and so on. Taking the modeling design of animation as the standard, it can be divided into realistic class, freehand class, and abstract class. In order to achieve the above various forms of animation, computer animation under new media art can be divided into two-dimensional animation and three-dimensional animation. Two-dimensional animation is a plane moving picture drawn in two-dimensional space. Compared with traditional animation, computer two-dimensional animation saves labor cost and improves production efficiency and picture quality. According to the complexity and flow of the production process, two-dimensional animation can be subdivided into GIF animation, interactive animation, and ordinary two-dimensional digital animation. Three-dimensional animation is the animation of three-dimensional image, and its motion is created in three-dimensional space. Due to the advantages of three-dimensional computer graphics and animation in performance materials, lighting, space, and motion, three-dimensional animation is widely used in advertising, film and television production, art design, network communication, games, virtual reality, and other fields.


## 3. Frame Rate Compensation

As we all know, the amount of information obtained by human vision accounts for about 70% of the total amount of information, and video information has a series of advantages, such as intuition, reliability, and so on. Therefore, the key technology of video technology is video technology. However, both images and videos contain a large amount of data. For example, the number of bits occupied by CIF format video of segment-length second is 360 × two hundred and eighty-eight × twenty-four × 30 = 74.65 Mb. That is, each frame of image has nearly 2.5 Mb of data.General color TV signals also have a huge amount of data. For the n-standard color TV signal, in the yi-q chromaticity space representing light intensity, color and color saturation, the bandwidth of each component is, respectively, 4.2 MHz, 1.5 MHz, and 0.5 MHz. If both components are digitized to 8 bits, the data amount of color signal in one second is (4.2 + 1.5 + 0.5) × two × 8 =  = 99.2 Mb. With such a huge amount of data, it is unrealistic to simply rely on expanding the memory capacity and increasing the capacity of the communication trunk line. Data compression technology is an effective solution [10]. Although a large amount of data is needed to represent image and video information, these data are often highly correlated, which will cause information redundancy. Therefore, the compression of image or video data can be realized by removing redundant information. One of the main objectives of still image compression is to remove the spatial redundancy of the image itself on the premise of maintaining the acceptable quality of the reconstructed image. The compression of video signal can remove both spatial redundancy and temporal redundancy to achieve high compression ratio. In addition to spatial redundancy and temporal redundancy, there are other redundant information in general image and video data. Therefore, the video image quality is optimized by frame rate compensation. The description of the main compliance in this article is shown in Table 1.Table 1
Symbol description.
WWeight matrixIFrame of a pictureΔOffsetF (,)Mapping functionK (,)Kernel functionThe low frame rate video compression algorithm based on the linear function model uses the adaptive separable convolution network to fuse the offset from thei frame image to the i+1 frame and i+2 frame images and obtains the prediction value corresponding to the i+2 frame image according to the fusion result. The above process can be described by the following formula:(1)Ic2′=W1∗Ic1+Δi,i+1+W2∗Ic3+Δi+2,i+1,where Ic2′ represents the prediction frame corresponding to the i+1th frame image; ∗ describes the convolution operation; W1, W2 represent weight; Ic1 is the ith frame image; Ic3 is the i+2th frame image [11, 12]; Δi,i+1 represents the offset between the ith frame image and the i+1th frame image; and Δi+2,i+1 represents the offset between the i+2 frame image and the i+1th frame image.According to the above analysis, in order to obtain the prediction value corresponding to thei+1th frame image, the offset from the ith frame image to the i+1th frame image is calculated:(2)Ic2′=fIc1,Ic2=Ic1+Δi,i+1,where f represents the mapping function.The low frame rate video animation video compression algorithm based on linear function model realizes the frame rate compensation processing of video image through convolutional neural network. The processing process is divided into the following two parts. Frame rate compensation framework is shown in Figure1.Figure 1
Frame rate compensation framework. (a) Frame rate compensation module. (b) Decompression artifact module.
(a)(b)According to Figure1, the encoding module and decoding module are, respectively, composed of pooling layer, convolution layer, sampling layer, and convolution layer. The feature map is fused at different scales through jump connection operation. The final result obtained by the decoding module is the input content of the separable convolution module.The encoding and decoding modules in the encoding module are composed of an average pooling layer and three 3∗3 convolution layers [13]. In order to obtain more features of low frame rate video animation video, the convolution block of the module is composed of three convolution layers to improve the receptive field. The feature map is reduced to half of the original image in the average pool layer. The bilinear upsampling layer and three 3∗3 convolution layers are arranged in turn to obtain the decoding module. In order to avoid the chessboard effect in the image processing process, the low frame rate video animation video compression algorithm based on the linear function model uses the bilinear difference method to complete the upsampling operation on the video image. In the encoding module, the convolution block is operated by jumping connection to obtain the characteristic information of the video image.In the separable convolution module, one-dimensional convolution kernelsKhx,y and Kvx,y are predicted through the subnetwork, where Khx,y represents the horizontal vector and Kvx,y represents the vertical vector. The relationship between the two can be described by the following formula:(3)Kx,y=Khx,y∗Kvx,y.The convolution operation processes the input image frameIc2 to obtain the result Ic2′ after frame rate compensation:(4)Ic2′=Khx,y∗Kvx,y∗Ic2x,y.


## 4. Low Frame Rate Video Animation Video Compression Algorithm

The low frame rate video animation compression algorithm based on the linear function model performs block classification processing on the video image after frame rate compensation processing, performs CS measurement according to the block characteristics, selects the best linear estimation based on the linear function model, and compresses the video image to complete the compression processing of low frame rate video animation video.


### 4.1. Image Block Classification

There are local features such as texture, edge, and flatness in the live video image [14]. The image block is identified by the above features. Let di represent the variance corresponding to the image block, and its calculation formula is as follows:(5)di=1B×B∑j=1B×Bpij−p¯i2,p¯i=1B×B∑j=1B×Bpij,where B×B represents the size corresponding to the video image block; p¯i represents the gray value; and pij represents the gray value corresponding to the ith block and the jth pixel.Letd¯i represent the average variance of the image block, and its calculation formula is as follows:(6)d¯i=di−dmindmax−dmin,where dmin=mind1,d2,…,dn represents the minimum variance and dmax=maxd1,d2,…,dn represents the maximum variance [15, 16].Set the image block classification criterionxi according to the mean value of variance:(7)xi∈q1,d¯i≤T1,q2,T1<d¯i≤T2,q3,d¯i>T2,where T1 and T2 represent the classification threshold, and q1,q2,andq3 represent smooth block, edge block, and texture block, respectively.


### 4.2. Adaptive CS measurement

LetN represent the number of low frame rate video animation video blocks; r1,r2,andr3, respectively, represent the measurement rates corresponding to smooth block, edge block, and texture block. At this time, there is the following formula:(8)N×B2×r=N1B2r1+N2B2r2+N3B2r3,where N1,N2,andN3, respectively, represent the number of flat blocks, edge blocks, and texture blocks. Set r1=R, r2=3R, and r3=5R to simplify the above formula to the following formula:(9)R=NrN1+3N2+5N3.The value of parameterR is obtained by the above formula, and the corresponding measurement rates r1, r2, and r3 can be obtained.Due to the influence of weight, the measurement rate corresponding to edge block is moderate [17], and the measurement rate corresponding to texture block and flat block is high and low, respectively.The corresponding measurement matrix is constructed according to the measurement rate calculated above. The orthogonal Gaussian matrix is selected as the measurement matrix, and the specific process is as follows:(1)
The random matrixΘ of B2×B2 is constructed from the elements satisfying the standard Gaussian distribution.(2)
Schmidt orthogonalization is performed on random vectorΘ to make its column vectors orthogonal to each other [18, 19].(3)
The measurement timesM1, M2, and M3 of smooth block, edge block, and texture block can be calculated by corresponding measurement rates.(4)
Randomly obtain rowsM1, M2, and M3 in random vector Θ, and construct corresponding measurement matrices ΦB1, ΦB2, and ΦB3.Set the matrixΘΩΩ=m, which is composed of the row vector existing in the random vector Θ. Under the condition of m≥CSlgB44, the probability of uncorrelation between the matrix ΘΩ and the orthogonal reference matrix Ψ is high, where C is a constant, and S represents the sparsity corresponding to the image block coefficient under Ψ; B4 represents the number of pixels in the image block.


### 4.3. Best Linear Estimation

The linear function modelΦB is used to process the image block data x to obtain the CS measurement data y. Linear function model ΦB usually has limited isometry [20].The CS measurement datay is linearly transformed to obtain the estimation error e and the linear estimation x^:(10)x^=Hy,e=x−x^=x−Hy,where H represents a linear operator [21, 22].Set the criterionHopt for selecting linear operator H on the basis of minimum error mean square value:(11)Hopt=argminHRcc=EeeT=Ex−Hyx−HyT,where E⋅ represents the function matrix; Rcc represents the projection data. Let the value of Hopt be zero to obtain the linear operator Hopt corresponding to the best linear estimation:(12)Hopt=RxxΦBTΦBRxxΦBT,where Rxx represents the autocorrelation function matrix [23] existing between low frame rate video animation video blocks, and its expression is as follows:(13)Rxx=Ex1,x1Ex1,x2⋯Ex1,xB2Ex2,x1Ex2,x2⋯Ex2,xB2⋯⋯⋯⋯ExB2,x1ExB2,x2⋯ExB2,xB2.The autocorrelation function matrix is used to compress the image block to complete the compression of low frame rate video and animation video.


## 4.1. Image Block Classification

There are local features such as texture, edge, and flatness in the live video image [14]. The image block is identified by the above features. Let di represent the variance corresponding to the image block, and its calculation formula is as follows:(5)di=1B×B∑j=1B×Bpij−p¯i2,p¯i=1B×B∑j=1B×Bpij,where B×B represents the size corresponding to the video image block; p¯i represents the gray value; and pij represents the gray value corresponding to the ith block and the jth pixel.Letd¯i represent the average variance of the image block, and its calculation formula is as follows:(6)d¯i=di−dmindmax−dmin,where dmin=mind1,d2,…,dn represents the minimum variance and dmax=maxd1,d2,…,dn represents the maximum variance [15, 16].Set the image block classification criterionxi according to the mean value of variance:(7)xi∈q1,d¯i≤T1,q2,T1<d¯i≤T2,q3,d¯i>T2,where T1 and T2 represent the classification threshold, and q1,q2,andq3 represent smooth block, edge block, and texture block, respectively.


## 4.2. Adaptive CS measurement

LetN represent the number of low frame rate video animation video blocks; r1,r2,andr3, respectively, represent the measurement rates corresponding to smooth block, edge block, and texture block. At this time, there is the following formula:(8)N×B2×r=N1B2r1+N2B2r2+N3B2r3,where N1,N2,andN3, respectively, represent the number of flat blocks, edge blocks, and texture blocks. Set r1=R, r2=3R, and r3=5R to simplify the above formula to the following formula:(9)R=NrN1+3N2+5N3.The value of parameterR is obtained by the above formula, and the corresponding measurement rates r1, r2, and r3 can be obtained.Due to the influence of weight, the measurement rate corresponding to edge block is moderate [17], and the measurement rate corresponding to texture block and flat block is high and low, respectively.The corresponding measurement matrix is constructed according to the measurement rate calculated above. The orthogonal Gaussian matrix is selected as the measurement matrix, and the specific process is as follows:(1)
The random matrixΘ of B2×B2 is constructed from the elements satisfying the standard Gaussian distribution.(2)
Schmidt orthogonalization is performed on random vectorΘ to make its column vectors orthogonal to each other [18, 19].(3)
The measurement timesM1, M2, and M3 of smooth block, edge block, and texture block can be calculated by corresponding measurement rates.(4)
Randomly obtain rowsM1, M2, and M3 in random vector Θ, and construct corresponding measurement matrices ΦB1, ΦB2, and ΦB3.Set the matrixΘΩΩ=m, which is composed of the row vector existing in the random vector Θ. Under the condition of m≥CSlgB44, the probability of uncorrelation between the matrix ΘΩ and the orthogonal reference matrix Ψ is high, where C is a constant, and S represents the sparsity corresponding to the image block coefficient under Ψ; B4 represents the number of pixels in the image block.


## 4.3. Best Linear Estimation

The linear function modelΦB is used to process the image block data x to obtain the CS measurement data y. Linear function model ΦB usually has limited isometry [20].The CS measurement datay is linearly transformed to obtain the estimation error e and the linear estimation x^:(10)x^=Hy,e=x−x^=x−Hy,where H represents a linear operator [21, 22].Set the criterionHopt for selecting linear operator H on the basis of minimum error mean square value:(11)Hopt=argminHRcc=EeeT=Ex−Hyx−HyT,where E⋅ represents the function matrix; Rcc represents the projection data. Let the value of Hopt be zero to obtain the linear operator Hopt corresponding to the best linear estimation:(12)Hopt=RxxΦBTΦBRxxΦBT,where Rxx represents the autocorrelation function matrix [23] existing between low frame rate video animation video blocks, and its expression is as follows:(13)Rxx=Ex1,x1Ex1,x2⋯Ex1,xB2Ex2,x1Ex2,x2⋯Ex2,xB2⋯⋯⋯⋯ExB2,x1ExB2,x2⋯ExB2,xB2.The autocorrelation function matrix is used to compress the image block to complete the compression of low frame rate video and animation video.


## 5. Experiments and Results

In order to verify the overall effectiveness of the low frame rate video compression algorithm based on linear function model, it is necessary to test the low frame rate video compression algorithm based on linear function model. Image adaptive optimization is independent of the specific neural compression algorithm used. To prove this, we used two existing architectures: a (simple) model using a single latent space and decomposition probability model, and a more complex model (complete) with a hierarchical latent representation, where super prior and context used model potential probabilities. Both models are trained on images from the COCO segmentation dataset. After training, the model is tested on the Tecnick dataset. The low frame rate video animation video compression algorithm (algorithm 1, our method), reference [3] algorithm (algorithm 2, called HEVC), and reference [4] algorithm (algorithm 3, called Model-1) based on the linear function model are used to compress the field video image. The compressed image is shown in Figure 2.Figure 2
Image compression results of different algorithms.It can be seen from the analysis of Figure2 that after the power grid operation site image is compressed by algorithm 1, the image obtained is relatively clear. After the power grid site image is compressed by algorithm 2 and algorithm 3, the images obtained are blurred and deformed to varying degrees, indicating that the above two algorithms cannot realize the lossless compression of low frame rate video animation video, Algorithm 1 compensates the frame rate of the video image before compressing the low frame rate video animation video, eliminates the artifacts generated in the compression process, and improves the definition of the image after compression.Algorithm 1, algorithm 2, and algorithm 3 are used to compress the low frame rate video animation video. The video image compression quality of the above algorithm under different compression multiples is tested through the peak signal-to-noise ratio PSNR. The test results are shown in Figure3.Figure 3
Peak signal-to-noise ratio under different compression ratios. (a) Compression factor 32. (b) Compression factor 64. (c) Compression factor 128.
(a)(b)(c)By analyzing Figure3, it can be seen that under different compression ratios, the peak signal-to-noise ratio of algorithm 1 fluctuates around 30 dB, and the fluctuation under the change of frame number is small and basically stable, while the fluctuation range of peak signal-to-noise ratio of algorithm 2 and algorithm 3 is large when the compression ratios are 32, 64, and 128, and the peak signal-to-noise ratio is much lower than that of algorithm 1. The higher the peak signal-to-noise ratio, the better the compression quality of the video image. According to the above analysis, the proposed algorithm can complete the compression processing of low frame rate video animation video with high quality under different compression multiples.Take the running time as the test index to verify the compression efficiency of algorithm 1, algorithm 2, and algorithm 3 under different compression ratios. The test results are shown in Figure4.Figure 4
Running time under different compression ratios. (a) Compression ratio 40. (b) Compression ratio 80. (c) Compression ratio 160.
(a)(b)(c)According to the data in Figure4, with the increase of compression ratio, the running time of algorithm 1, algorithm 2, and algorithm 3 continues to increase, indicating that there is a linear relationship between compression ratio and operation. Under different compression ratios, the running time of algorithm 1 for low frame rate video animation video compression is lower than that of algorithm 2 and algorithm 3 for power grid operation site video compression. Because before compressing the video image, algorithm 1 performs frame rate compensation on the video image, eliminates the interference of artifacts on the video compression, shortens the running time of video image compression, and improves the compression efficiency of algorithm 1.


## 6. Conclusion

With the wide application of bandwidth networks such as asynchronous transmission mode and integrated digital service network, people put forward higher requirements for the transmission quality of video images. The amount of low frame rate video animation video is huge, so it needs to be compressed before transmission. At present, there are some problems in the image compression algorithm of power grid operation site, such as low compression definition, poor video image quality, and low compression efficiency. A low frame rate video compression algorithm based on linear function model is proposed. The adaptive separable convolution network is used to compensate the frame rate of low frame rate video animation video. On this basis, the linear function model is used to complete the compression of low frame rate video animation video, which solves the problems existing in traditional algorithms. The compressed low frame rate video animation video has high definition, good quality, and high compression efficiency. Although the method we proposed after the compression of the video is of high definition and good quality, however, abundant features need to be constructed in the process of modeling, and a lot of time needs to be spent in the process of model training. Therefore, in the future work, we need to reduce the complexity of the model, in order to improve the efficiency of video compression. [23].



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