高技术通讯2024，Vol.34Issue(7)：726-733,8.DOI:10.3772/j.issn.1002-0470.2024.07.006
基于三维卷积时空融合网络的压缩视频质量增强算法
Compressed video quality enhancement algorithm based on 3D convolutional spatio-temporal fusion network
摘要
Abstract
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.关键词
3D卷积/视频质量增强/多帧信息/深度学习Key words
3-dimensional convolution/video quality enhancement/multi-frame information/deep learning引用本文复制引用
黄威威,贾克斌..基于三维卷积时空融合网络的压缩视频质量增强算法[J].高技术通讯,2024,34(7):726-733,8.基金项目
北京市自然科学基金(4212001)资助项目. （4212001）
