计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 113-118.doi: 10.11896/j.issn.1002-137X.2019.03.016
徐婧瑶,王祖林,徐迈
XU Jing-yao, WANG Zu-lin, XU Mai
摘要： 由于良好的率失真表现,新一代视频压缩标准HEVC(High Efficiency Video Coding)得到了越来越多终端设备的支持。然而目前仍有大量的H.264码流存在,因此H.264到HEVC的高效视频转码具有重要的实际意义。实现H.264到HEVC转码最简单的方法,是将H.264解码端和HEVC编码端直接级联起来。由于HEVC编码过程的复杂度较高,这种方法的转码时间较长。针对H.264到HEVC转码耗时的问题,文中提出一种基于深度学习的方法来预测HEVC的CTU(Coding Tree Unit)块划分结果,从而避开HEVC对CTU所有块划分情况循环遍历以寻找率失真最优划分结构的过程,实现H.264到HEVC的快速转码。首先建立了一个H.264到HEVC转码的大型数据库,为训练深度学习模型提供数据保障;随后对H.264压缩域特征和HEVC的CTU块划分模式进行相关性分析,并发掘了CTU块划分模式在时序上的相似性,进而提出基于时间递归神经网络LSTM(Long Short-Term Memory)的三级分类器来预测HEVC的CTU划分。实验结果表明,与直接级联转码器相比,文中提出的H.264到HEVC快速转码算法实现了60%的时间节省,同时峰值信噪比仅下降了0.039kdB,其性能胜过近年来的转码算法的性能。
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| [1]LIU Z,YU X,GAO Y,et al.CU Partition Mode Decision for HEVC Hardwired Intra Encoder Using Convolution Neural Network[J].IEEE Transactions on Image Processing,2016,25(11):5088-5103. [2]SHEN L,LIU Z,ZHANG X,et al.An Effective CU Size Decision Method for HEVC Encoders[J].IEEE Transactions on Multimedia,2013,15(2):465-470. [3]ZHANG D,TONG J,ZAND D.Fast CU partition for H.264/AVC to HEVC transcoding based on fisher discriminant analysis[C]∥Visual Communications and Image Processing.IEEE,2017:1-4. [4]PEIXOTO E,IZQUIERDO E.A complexity-scalable transcoder from H.264/AVC to the new HEVC codec[C]∥IEEE International Conference on Image Processing.IEEE,2012:737-740. [5]NAGARAGHATTA A,ZHAO Y,MAXWELL G,et al.Fast H.264/AVC to HEVC transcoding using mode merging and mode mapping[C]∥IEEE International Conference on ConsumerElectronics.Berlin:IEEE,2016:165-169. [6]FRANCHE J F,COULOMBE S.Fast H.264 to HEVC transcoder based on post-order traversal of quadtree structure[C]∥IEEE International Conference on Image Processing.IEEE,2015:477-481. [7]PEIXOTO E,MACCHIAVELLO B,HUNG E M,et al.An H.264/AVC to HEVC video transcoder based on mode mapping[C]∥IEEE International Conference on Image Processing.IEEE,2014:1972-1976. [8]PEIXOTOE,SHANABLEH T,IZQUIERDOE.H.264/AVC to HEVC Video Transcoder Based on Dynamic Thresholding and Content Modeling[J].IEEE Transactions on Circuits & Systems for Video Technology,2014,24(1):99-112. [9]PEIXOTO E,MACCHIAVELLO B,QUEIROZ R L D,et al.Fast H.264/AVC to HEVC transcoding based on machine learning[C]∥Telecommunications Symposium.IEEE,2014:1-4. [10]JIANG W,CHEN Y,TIAN X.Fast transcoding from H.264 to HEVC based on region feature analysis[J].Multimedia Tools & Applications,2014,73(3):2179-2200. [11]DAZ-HONRUBIA A J,MARTNEZ J L,PUERTA J M,et al.Fast quadtree level decision algorithm for H.264/HEVC transcoder[C]∥IEEE International Conference on Image Processing.IEEE,2015:2497-2501. [12]DAZ-HONRUBIA A J,MARTNEZ J L,CUENCA P,et al. Adaptive Fast Quadtree Level Decision Algorithm for H.264 to HEVC Video Transcoding[J].IEEE Transactions on Circuits & Systems for Video Technology,2016,26(1):154-168. [13]CORREA G,AGOSTINI L,CRUZ L A D S.Fast H.264/AVC to HEVC transcoder based on data mining and decision trees[C]∥IEEE International Symposium on Circuits and Systems.IEEE,2016:2539-2542. [14]ZHU L,ZHANG Y,LI N,et al.Machine learning based fast H. 264/AVC to HEVC transcoding exploiting block partition similarity[J].Journal of Visual Communication & Image Representation,2016,38(C):824-837. [15]Xiph.org.Xiph.org video test media[OL].https://media.xiph.org/video/derf/. [16]XU M,DENG X,LI S,et al.Region-of-Interest Based Conversational HEVC Coding with Hierarchical Perception Model of Face[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(3):475-489. [17]OHM J R,SULLIVAN G J,TAN T K,et al.Comparison of the Coding Efficiency of Video Coding Standards—Including High Efficiency Video Coding (HEVC)[J].IEEE Transactions on Circuits & Systems for Video Technology,2012,22(12):1669-1684. [18]INGMA D P,BA J.Adam:A Method for Stochastic Optimization[J].arXiv preprint arXiv:141206980,2014. [19]CORREA G,ASSUNCAO P A,AGOSTINI L V,et al.Fast HEVC Encoding Decisions Using Data Mining[J].IEEE Tran-sactions on Circuits & Systems for Video Technology,2015,25(4):660-673. |
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