Chaotic moving video quality enhancement based on deep in-loop filtering  

作　　者：Tong Tang Yi Yang Dapeng Wu Ruyan Wang Zhidu Li 

机构地区：[1]School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [2]Advanced Network and Inteligent Interconnection Technology Key Laboratory of Chongqing Education Commission of China,Chongqing,400065,China [3]Chongqing Key Laboratory of Ubiquitous Sensing and Networking,Chongqing,400065,China

出　　处：《Digital Communications and Networks》2024年第6期1708-1715,共8页数字通信与网络(英文版)

基　　金：supported by National Natural Science Foundation of China under grant U20A20157,61771082,62271096 and 61871062;the General Program of Chonqing Natural Science Foundation under grant cstc2021jcyj-msxm X0032;the Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxm X0024);the Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJQN202300632;the University Innovation Research Group of Chongqing(CXQT20017)。

摘　　要：The Joint Video Experts Team(JVET)has announced the latest generation of the Versatile Video Coding(VVC,H.266)standard.The in-loop filter in VVC inherits the De-Blocking Filter(DBF)and Sample Adaptive Offset(SAO)of High Efficiency Video Coding(HEVC,H.265),and adds the Adaptive Loop Filter(ALF)to minimize the error between the original sample and the decoded sample.However,for chaotic moving video encoding with low bitrates,serious blocking artifacts still remain after in-loop filtering due to the severe quantization distortion of texture details.To tackle this problem,this paper proposes a Convolutional Neural Network(CNN)based VVC in-loop filter for chaotic moving video encoding with low bitrates.First,a blur-aware attention network is designed to perceive the blurring effect and to restore texture details.Then,a deep in-loop filtering method is proposed based on the blur-aware network to replace the VVC in-loop filter.Finally,experimental results show that the proposed method could averagely save 8.3%of bit consumption at similar subjective quality.Meanwhile,under close bit rate consumption,the proposed method could reconstruct more texture information,thereby significantly reducing the blocking artifacts and improving the visual quality.

关 键 词：H.266 Versatile Video Coding Convolutional neural network In-loop filter 

分 类 号：R71[医药卫生—妇产科学]