联合多特征的未来视频快速编码
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摘要
与前一代高效视频编码标准相比,未来视频编码标准增加了新的二叉树加四叉树编码结构。针对该结构虽然显著提升了超高清视频编码效率,但是大幅增加了编码复杂度的问题,提出了联合多特征快速编码算法。该算法联合多个编码特征的后验概率信息,估计当前编码单元的划分方式,通过提前终止若干编码单元的划分来节省编码时间。此外,针对单个特征贝叶斯分类不准确的问题,算法在简化联合概率模型的同时提升了分类算法的准确度。实验结果表明:在随机访问配置、低延迟P配置和低延迟B配置下,算法可以平均减少35.7%,25.6%和26.7%的编码复杂度,而BDBR只分别增长了4.3%,3.1%和2.89%。算法在保证视频主观质量的前提下,节省了编码时间。
Future video coding considerably improves compression efficiency over that of the previous high-efficiency video coding standard.The quadtree plus binary tree structure of future video coding is more consistent with the texture of high-definition video but at the cost of tremendous computational complexity.In this study,ajoint multi-feature classification algorithm was proposed to reduce the complexity of the quadtree plus binary tree block structure in future video coding.The partition mode of the current coding unit was estimated using the proposed algorithm,and the partition of the coding unit was then terminated.Consequently,coding time was conserved by the proposed algorithm.In general,classification with a single feature was inaccurate.Thus,ajoint multi-feature classification model was used in the proposed algorithm.The proposed algorithm not only improveed the accuracy of the classification,but also simplified the joint probability model.Experimental results show that our algorithm can reduce the coding complexity on average by 35.7%,25.6%,and 26.7% with only a 4.3%,3.1%,and 2.89% Bjontegaard delta bit rate increment with the random access main,low delay P,and low delay B configurations,respectively.The encoding time is conserved by the proposed algorithm based on subjective video quality.
Future video coding considerably improves compression efficiency over that of the previous high-efficiency video coding standard.The quadtree plus binary tree structure of future video coding is more consistent with the texture of high-definition video but at the cost of tremendous computational complexity.In this study,ajoint multi-feature classification algorithm was proposed to reduce the complexity of the quadtree plus binary tree block structure in future video coding.The partition mode of the current coding unit was estimated using the proposed algorithm,and the partition of the coding unit was then terminated.Consequently,coding time was conserved by the proposed algorithm.In general,classification with a single feature was inaccurate.Thus,ajoint multi-feature classification model was used in the proposed algorithm.The proposed algorithm not only improveed the accuracy of the classification,but also simplified the joint probability model.Experimental results show that our algorithm can reduce the coding complexity on average by 35.7%,25.6%,and 26.7% with only a 4.3%,3.1%,and 2.89% Bjontegaard delta bit rate increment with the random access main,low delay P,and low delay B configurations,respectively.The encoding time is conserved by the proposed algorithm based on subjective video quality.
引文
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[19]张强,邵喜斌,杨虹.大尺寸8K显示行业关键技术分析[J].液晶与显示,2018,33(5):389-396.ZHANG Q,SHAO X B,YANG H.Key technology analysis of large size 8Kdisplay[J].Chinese Journal of Liquid Crystals and Displays,2018,33(5):389-396.(in Chinese)
[20]蒋刚毅,杨小祥,彭宗举,等.高效视频编码的快速编码单元深度遍历选择和早期编码单元裁剪[J].光学精密工程,2014,22(5):1322-1330.JIANG G Y,YANG X X,PENG Z J,et al..Fast CU depth range selection and early CU pruning for HEVC[J].Opt.Precision Eng.,2014,22(5):1322-1330.(in Chinese)
[2]WANG Z,WANG S,ZHANG J,et al..Local-constrained quadtree plus binary tree block partition structure for enhanced video coding[J].2016 Visual Communications and Image Processing(VCIP),2016:1-4.
[3]YAMAMOTO Y,IKAI T.AHG5:Fast QTBTencoding configuration[C].4th Meeting Joint Video Exploration Team(JVET),Document JVET-D0095,Chengdu,2016:1-6.
[4]LIN P H,LIN C L,CHANG Y Z,et al..AHG5:Enhanced fast algorithm of JVET-E0078[C].6th Meeting Joint Video Exploration Team(JVET),Document JVET-F0063,Hobart,2017:1-2.
[5]PARK S H,DONG T,JANG E S.Low complexity reference frame selection in QTBT structure for JVET future video coding[J].2018 International Workshop on Advanced Image Technology(IWAIT),2018:1-4.
[6]WANG Z,WANG S,ZHANG J,et al..Effective quadtree plus binary tree block partition decision for future video coding[J].2017 Data Compression Conference(DCC),2017:23-32.
[7]WANG Z,WANG S,ZHANG J,et al..Probabilistic decision based block partitioning for future video coding[J].IEEE Transactions on Image Processing,2018,27(3):1475-1486.
[8]HU Q,SHI Z,ZHANG X,et al..Early SKIPmode decision based on Bayesian model for HEVC[J].2015 Visual Communications and Image Processing(VCIP),2015:1-4.
[9]CHEN J,YU L.Effective HEVC intra coding unit size decision based on online progressive Bayesian classification[J].2016 IEEE International Conference on Multimedia and Expo(ICME),2016:1-6.
[10]KIM H S,PARK R H.Fast CU partitioning algorithm for HEVC using an online-learning-based bayesian decision rule[J].IEEE Transactions on Circuits and Systems for Video Technology,2016,26(1):130-138.
[11]LI Y,LIU X,YU T,et al..Fast online-learning parameters decision algorithm based on bayesian decision rule for 3D-HEVC[J].2016 IEEE International Conference on Internet of Things(iThings)and IEEE Green Computing and Communications(GreenCom)and IEEE Cyber,Physical and Social Computing(CPSCom)and IEEESmart Data(SmartData),2016:894-898.
[12]ZHANG J,KWONG S,WANG X.Two-stage fast inter CU decision for HEVC based on bayesian method and conditional random fields[J].IEEE Transactions on Circuits and Systems for Video Technology,2018,28(11):3223-3235.
[13]AL-BASHABSHEH A,MAO Y,VONTOBEL PO.Normal factor graphs:A diagrammatic approach to linear algebra[J].2011 IEEE International Symposium on Information Theory Proceedings,2011:2178-2182.
[14]LOELIGER H A,VONTOBEL P O.A factorgraph representation of probabilities in quantum mechanics[J].2012 IEEE International Symposium on Information Theory Proceedings,2012:656-660.
[15]TSAI T H,SU S S,LEE T Y.Fast mode decision method based on edge feature for HEVC interprediction[J].IET Image Processing,2018,12(5):644-651.
[16]李大维,孙海江,刘伟宁,等.基于统计信息的改进滑动平均目标检测算法[J].液晶与显示,2018,33(6):497-503.LI D W,SUN H J,LIU W N,et al..Improved sliding average target detection algorithm based on statistical information[J].Chinese Journal of Liquid Crystals and Displays,2018,33(6):497-503.(in Chinese)
[17]陆牧,朱明,高扬,等.基于元胞自动机的动态背景运动目标检测[J].光学精密工程,2017,25(7):1934-1940.LU M,ZHU M,GAO Y,et al..Moving target detection based on dynamic background of cellular automaton[J].Opt.Precision Eng.,2017,25(7):1934-1940.(in Chinese)
[18]周同雪,朱明.视频图像中的运动目标检测[J].液晶与显示,2017,32(1):40-47ZHOU T X,ZHU M.Moving target detection of the video images[J].Chinese Journal of Liquid Crystals and Displays,2017,32(1):40-47.(in Chinese)
[19]张强,邵喜斌,杨虹.大尺寸8K显示行业关键技术分析[J].液晶与显示,2018,33(5):389-396.ZHANG Q,SHAO X B,YANG H.Key technology analysis of large size 8Kdisplay[J].Chinese Journal of Liquid Crystals and Displays,2018,33(5):389-396.(in Chinese)
[20]蒋刚毅,杨小祥,彭宗举,等.高效视频编码的快速编码单元深度遍历选择和早期编码单元裁剪[J].光学精密工程,2014,22(5):1322-1330.JIANG G Y,YANG X X,PENG Z J,et al..Fast CU depth range selection and early CU pruning for HEVC[J].Opt.Precision Eng.,2014,22(5):1322-1330.(in Chinese)