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
Spatial models joint external and internal aspects of human activity,mental schemas of thinking,and spatial structures of things.These models represent objects of knowledge,valuation,and transformation due to similari...Spatial models joint external and internal aspects of human activity,mental schemas of thinking,and spatial structures of things.These models represent objects of knowledge,valuation,and transformation due to similarity with them in various relations,and they participate in inter-subject communication using schemata common for many people.The spatial models can reproduce a modelled object or be productive regarding it.These models are created in cognitive modus of comprehension as images of objects known at various mental levels;in projective modus,they appear as projects of object’s transformation and planes of subject’s actions;in communicative modus,they are interpreted as spatial texts expressing certain senses.All of them interact in spatial thinking,which deals with the relationship of parts and the whole,unlike logical thinking operating with genus-species relations.Both practical and theoretical thinking use common spatial schemas as means of internal modelling,which are elaborated in collective and individual experience.Due to their simplicity and unification,these schemas can serve also as units of spatial codes mediating the objects representation and inter-subject communication through spatial texts created in the semiotized space.展开更多
多功能视频编码(versatile video coding,VVC)是最新的视频编码标准,与高效视频编码(high efficiency video coding,HEVC)相比进一步提高了压缩效率,但由于引入了包括二叉树和三叉树在内的多类树结构,同时帧内角度模式从35种增加到67种...多功能视频编码(versatile video coding,VVC)是最新的视频编码标准,与高效视频编码(high efficiency video coding,HEVC)相比进一步提高了压缩效率,但由于引入了包括二叉树和三叉树在内的多类树结构,同时帧内角度模式从35种增加到67种,导致编码复杂度剧增。为了降低计算复杂度,本文提出了一种基于快速编码单元(coding unit,CU)划分和角度模式决策的VVC帧内编码算法。首先根据自适应标准差阈值对CU纹理复杂度进行分类,初步缩减划分模式列表;然后采用Sobel梯度算子确定纹理方向,跳过非最优划分模式;最后根据统计结果筛选淘汰掉概率小于2%的角度模式。实验结果表明,与VTM-2.1相比,该算法能节省51.05%的编码时间,BDBR(Bjontegarrd delta bit rate)仅上升1.98%。展开更多
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.
文摘Spatial models joint external and internal aspects of human activity,mental schemas of thinking,and spatial structures of things.These models represent objects of knowledge,valuation,and transformation due to similarity with them in various relations,and they participate in inter-subject communication using schemata common for many people.The spatial models can reproduce a modelled object or be productive regarding it.These models are created in cognitive modus of comprehension as images of objects known at various mental levels;in projective modus,they appear as projects of object’s transformation and planes of subject’s actions;in communicative modus,they are interpreted as spatial texts expressing certain senses.All of them interact in spatial thinking,which deals with the relationship of parts and the whole,unlike logical thinking operating with genus-species relations.Both practical and theoretical thinking use common spatial schemas as means of internal modelling,which are elaborated in collective and individual experience.Due to their simplicity and unification,these schemas can serve also as units of spatial codes mediating the objects representation and inter-subject communication through spatial texts created in the semiotized space.
文摘多功能视频编码(versatile video coding,VVC)是最新的视频编码标准,与高效视频编码(high efficiency video coding,HEVC)相比进一步提高了压缩效率,但由于引入了包括二叉树和三叉树在内的多类树结构,同时帧内角度模式从35种增加到67种,导致编码复杂度剧增。为了降低计算复杂度,本文提出了一种基于快速编码单元(coding unit,CU)划分和角度模式决策的VVC帧内编码算法。首先根据自适应标准差阈值对CU纹理复杂度进行分类,初步缩减划分模式列表;然后采用Sobel梯度算子确定纹理方向,跳过非最优划分模式;最后根据统计结果筛选淘汰掉概率小于2%的角度模式。实验结果表明,与VTM-2.1相比,该算法能节省51.05%的编码时间,BDBR(Bjontegarrd delta bit rate)仅上升1.98%。
