基于三维卷积时空融合网络的压缩视频质量增强算法OA北大核心CSTPCD

Standard compression algorithms are typically used to compress video data for storage and transmission over networks.However,compressed video can have compression artifacts that degrade quality.To address this prob-lem,a post-processing method based on deep learning is proposed.Firstly,a novel 3-dimensional convolutional spatio-temporal fusion(3D-CSTF)network is designed,which extracts the temporal information between consecu-tive video frames through the filtering characteristics of the 3D convolution kernel in three dimensions,and utilizes the strong correlation of the information between video frames to enhance the video quality.Among it,a quality en-hanced network(Qe-Net)is designed for mapping and extracting video frame features.Secondly,seven consecu-tive video frames are sent to the network for end-to-end training and the current frame is enhanced by using the in-formation of the previous and last three frames.Finally,training and testing are carried out on the MFQEv2 data-set.Experimental results demonstrate that this method achieves excellent performance in terms of the video quality assessment standard PSNR.When the quantization parameter(QP)are equal to 37,32,27 and 22,the PSNR can be increased by 0.82 dB,0.83 dB,0.79 dB and 0.74 dB,respectively.