Video compression method based on sparse samples

Abstract

The invention relates to a video compression method based on sparse samples, and belongs to the technical field of video compression. The method comprises the steps: S1, data preprocessing; S2, firstly, learning each frame of a video in a data set by utilizing a variation auto-encoder through a video generation method combining the variation auto-encoder and a generative adversarial network, constructing a hidden space with good continuity, and enabling each point in the hidden space to correspond to one frame in the video; inputting the noise and the text into a generator of the generative adversarial network, enabling the generator to generate a plurality of associated points in the latent variable space, and finally generating continuous images are generated through a decoder of a variational auto-encoder; S3, inputting the generated continuous images into a video compression model, screening background frames through a CNN network, and then identifying a target in each frame of image by using a YOLO neural network. According to the invention, the video compression efficiency can be improved, and the network transmission delay and the consumption of local resources are reduced.

Description

technical field [0001] The invention belongs to the technical field of video compression, and relates to a video compression method based on sparse samples. Background technique [0002] Video compression technology is often used in the transmission and storage of video data, and is often used in daily life. Video surveillance has become more and more popular, which brings massive video storage, so video compression has become a technology with very strong demand, and video compression has also become a research hotspot in the field of video surveillance. With the rapid development of artificial intelligence technology, especially deep learning has been successfully applied to many fields, such as image recognition, speech recognition, NLP and target detection and other fields. Therefore, it can be considered to use deep learning to video compression to achieve more efficient video compression. However, a robust video compression neural network usually requires a large amo...

AI Technical Summary

Problems solved by technology

However, a robust video compression neural network usually requires a large amount of training data for long-term training before it can be formed. The collection and production of training data requires a long-term and large amount of personnel, equipment, and time investment, which increases the R&D cost of the project and makes the calculation of the model complex. very high

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