基于深度学习的视频转码快速算法

doi:10.11896/j.issn.1002-137X.2019.03.016

计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 113-118.doi: 10.11896/j.issn.1002-137X.2019.03.016

• 2018 中国多媒体大会 • 上一篇下一篇

基于深度学习的视频转码快速算法

徐婧瑶,王祖林,徐迈

(北京航空航天大学电子信息工程学院北京 100191)

收稿日期:2018-07-11 修回日期:2018-09-15 出版日期:2019-03-15 发布日期:2019-03-22
通讯作者: 徐迈(1981-),男,博士,教授,CCF会员,主要研究方向为视频通信、图像处理、计算机视觉,E-mail:maixu@buaa.edu.cn(通信作者)。
作者简介:徐婧瑶(1994-),女,硕士生,主要研究方向为视频转码和深度学习;王祖林(1965-),男,博士,教授,主要研究方向为图像处理、通信信号处理等
基金资助:
国家自然科学基金(61573037)资助

Deep Learning Based Fast VideoTranscoding Algorithm

XU Jing-yao, WANG Zu-lin, XU Mai

(College of Electronics and Information Engineering,Beihang University,Beijing 100191,China)

Received:2018-07-11 Revised:2018-09-15 Online:2019-03-15 Published:2019-03-22

摘要/Abstract

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。

关键词: HEVC, H.264, 视频转码, 深度学习

Abstract: Due to the good rate-distortion performance,as the latest video compression standard,high efficiency video coding (HEVC) has been adopted by more and more terminals.However,there are still a large number of H.264 streams in the field of video compression.Therefore,H.264 to HEVC video transcoding is a meaningful research issue.The simplest way to achieve H.264 to HEVC transcoding is to directly cascade the H.264 decoder and the HEVC encoder.Due to high complexity of the HEVC coding process,this transcoding method is time-consuming.Therefore,this paper proposed a fast H.264 to HEVC transcoding method based on deep learning to predict the CTU(Coding Tree Unit) partition of HEVC,avoiding the brute-force search of CTU partition for rate-distortion optimization(RDO).First,a large-scale database of H.264 to HEVC transcoding is built for ensuring the training of deep learning model.Second,the correlation between HEVC CTU partition and H.264 domain features is analyzed,and the similarity of CTU partition across frames is found out.Then,a three-level classifier based on LSTM (Long Short-Term Memory) is designed to predict the CTU partition.The experimental results show that the H.264 to HEVC fast transcoding algorithm proposed in this paper achieves 60% reduction in complexity compared to the original transcoder,while the peak signal-to-noise ratio is only reduced by 0.039kdB,so the proposed method outperforms the state-of-the-art transcoding methods.

Key words: HEVC, H.264, Video transcoding, Deep learning

中图分类号:

TN919.81

徐婧瑶, 王祖林, 徐迈. 基于深度学习的视频转码快速算法[J]. 计算机科学, 2019, 46(3): 113-118.

XU Jing-yao, WANG Zu-lin, XU Mai. Deep Learning Based Fast VideoTranscoding Algorithm[J]. Computer Science, 2019, 46(3): 113-118.

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基于深度学习的视频转码快速算法

Deep Learning Based Fast VideoTranscoding Algorithm

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