Video compression method based on deep neural network

Abstract

The invention discloses a video compression method based on the deep neural network. The method includes the following steps that a video image data set is collected and organized, and a neural network training set, a test set and a cross validation set are constructed; the multi-layer deep neural network is set up; for inter-frame prediction, a motion estimation algorithm is used for searching for the optimal matching block, and residuals and the mean square error of inter-frame prediction are calculated; the residuals obtained after prediction are used as new training data to train a residual code network, and a residual network model comprises an intra-frame residual and an inter-frame residual; compression data serving as fixed-length code streams together with output data, obtained after quantization and lossless entropy coding, of the residual neural network is predicted; a decoding terminal restores the compression data through a neutral network symmetric with a coding terminal, and a compressed image is obtained through reestablishment and recovery. Compared with a traditional H.264 video coding method in equality comparison of plenty of test video sequences, the video compression method can save about 26% code rate on the premise of equal quality.

Description

technical field [0001] The invention relates to the field of video coding, in particular to a video compression method based on a deep neural network. Background technique [0002] In recent years, artificial neural networks have developed to the stage of deep learning. Deep learning attempts to use a series of algorithms that contain complex structures or multiple processing layers composed of multiple nonlinear transformations to perform high-level abstraction on data. Its powerful expressive ability enables it to achieve the best results in various machine learning tasks. The performance on video and image processing also currently exceeds other methods. [0003] Deep learning uses the idea of ​​hierarchical abstraction, and high-level concepts are learned through low-level concepts. This hierarchical structure is usually constructed using a greedy layer-by-layer training algorithm, and effective features that are helpful for machine learning are selected from it. Many ...

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Problems solved by technology

For those original video data in YUV420 format for each frame, no wi

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