Exploration of Video Encoding and Decoding Technology under the Concept of Deep Learning
In the process of video encoding and decoding,the introduction of quantization operations often leads to lossy compression of the video.In subsequent playback,there are more compression residues,which are more pronounced in low bit encoding.Therefore,in order to optimize this problem,a new spatiotemporal neural network model based on optical flow technology(FGTSN)is proposed on the basis of deep learning theory,aiming to perform accurate post-processing on encoded videos.Experimental results have shown that the FGTSN method can significantly improve the quality of HEVC compressed videos,and its effectiveness far exceeds other video quality enhancement techniques.This method can effectively solve the problems of occlusion and large-scale motion scenes,and improve the reconstruction efficiency of compressed video frames,proving its high value in practical applications.
deep learning conceptvideo encoding and decoding technologyFGTSN method
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