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 Hi...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.展开更多
Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure i...Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure in High Efficiency Video Coding(H.265/HEVC).More complicated coding unit(CU)partitioning processes in H.266/VVC significantly improve video compression efficiency,but greatly increase the computational complexity compared.The ultra-high encoding complexity has obstructed its real-time applications.In order to solve this problem,a CU partition algorithm using convolutional neural network(CNN)is proposed in this paper to speed up the H.266/VVC CU partition process.Firstly,64×64 CU is divided into smooth texture CU,mildly complex texture CU and complex texture CU according to the CU texture characteristics.Second,CU texture complexity classification convolutional neural network(CUTCC-CNN)is proposed to classify CUs.Finally,according to the classification results,the encoder is guided to skip different RDO search process.And optimal CU partition results will be determined.Experimental results show that the proposed method reduces the average coding time by 32.2%with only 0.55%BD-BR loss compared with VTM 10.2.展开更多
基金supported by National Natural Science Foundation of China under grant U20A20157,61771082,62271096 and 61871062the General Program of Chonqing Natural Science Foundation under grant cstc2021jcyj-msxm X0032+2 种基金the Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxm X0024)the Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJQN202300632the University Innovation Research Group of Chongqing(CXQT20017)。
文摘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.
基金This paper is supported by the following funds:The National Key Research and Development Program of China(2018YFF01010100)Basic Research Program of Qinghai Province under Grants No.2021-ZJ-704,The Beijing Natural Science Foundation(4212001)Advanced information network Beijing laboratory(PXM2019_014204_500029).
文摘Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure in High Efficiency Video Coding(H.265/HEVC).More complicated coding unit(CU)partitioning processes in H.266/VVC significantly improve video compression efficiency,but greatly increase the computational complexity compared.The ultra-high encoding complexity has obstructed its real-time applications.In order to solve this problem,a CU partition algorithm using convolutional neural network(CNN)is proposed in this paper to speed up the H.266/VVC CU partition process.Firstly,64×64 CU is divided into smooth texture CU,mildly complex texture CU and complex texture CU according to the CU texture characteristics.Second,CU texture complexity classification convolutional neural network(CUTCC-CNN)is proposed to classify CUs.Finally,according to the classification results,the encoder is guided to skip different RDO search process.And optimal CU partition results will be determined.Experimental results show that the proposed method reduces the average coding time by 32.2%with only 0.55%BD-BR loss compared with VTM 10.2.
