基于HEVC标准的转码技术研究

发布时间：2018-08-28 17:41

【摘要】：新一代视频编码标准HEVC作为H.264/AVC的继承者,在视频压缩效率方面取得了巨大的提升,相比于H.264/AVC,HEVC在相似的视频感知质量下比特率减少了大约50%,因此正在逐渐成为业界视频压缩的主流标准。同时随着移动互联网革命的爆发,人们越来越频繁地使用移动端设备来观看视频,而在移动网络中视频的浏览受到网络阻塞的影响十分明显,暂时的网络阻塞会大大降低用户的观看体验。因此在视频服务器端,往往会保存视频的高比特率版本,并根据当前网络情况实时转码为不同码率的视频流提供给用户。这种情况就对高清HEVC视频的转码速度提出了新的要求。视频转码实际上是一个先解码再编码的过程,其中编码部分耗时占比达到90%。而HEVC标准因为其特殊性,编码时需要确定最优CU划分模式,这个过程需要遍历每一层CU划分,同时还需要进行复杂的率失真优化(RDO)计算,因此这是一个十分耗时的过程。针对这个问题,本文提出了两种快速确定编码单元CU划分模式的算法来降低HEVC标准视频的转码计算复杂度,从而在基本不影响视频质量的前提下,大大缩短视频转码时间。第一种方法利用了输入的高比特率视频流与输出的低比特率视频流在深度值上的关联性,简单快速地确定出深度值范围,从而减少CU划分模式的遍历范围。通过实验证明,相比于传统的全解全编转码模式,此方法仅仅增加了 0.84%的比特率,而转码时间缩短了 54%。第二种方法结合机器学习理论提出了在线训练在线分类的转码框架,利用原始码流CU划分信息以及时域前一帧的CU划分信息,通过朴素贝叶斯分类器预测出编码端的CU划分标志,从而确定了 CU划分模式。实验表明,通过这种方法视频实验帧仅仅增加了 2.74%的比特率,而转码时间缩短了 72%左右。
[Abstract]:As the successor of H.264/AVC, HEVC, a new video coding standard, has made great progress in video compression efficiency. Compared with H.264 / AVC HEVC, the bit rate is reduced by about 50% under the similar video perception quality, so it is becoming the mainstream standard of video compression in the industry. At the same time, with the outbreak of the revolution of the mobile Internet, people use mobile devices more and more frequently to watch video. However, in the mobile network, the browsing of video is obviously affected by the blocking of the network. Temporary network congestion can greatly reduce the user's viewing experience. Therefore, in the video server, the high bit-rate version of the video is often saved, and real-time transcoding is provided to the user for different bit-rate video streams according to the current network conditions. This situation puts forward new requirements for high-definition HEVC video transcoding speed. Video transcoding is actually a process of decoding and coding, in which the coding part takes up 90% of the time. Because of its particularity, HEVC standard needs to determine the optimal CU partitioning mode, which needs to traverse every layer of CU partition and perform complex rate-distortion optimization (RDO) computation, so it is a very time-consuming process. To solve this problem, this paper proposes two fast algorithms to determine the CU partition mode of the coding unit to reduce the computational complexity of the transcoding of the HEVC standard video, thus greatly reducing the transcoding time without affecting the video quality. The first method makes use of the correlation between the input high bit rate video stream and the output low bit rate video stream on the depth value, and determines the range of the depth value simply and quickly, thus reducing the traversal range of the CU partition mode. It is proved by experiments that compared with the traditional fully decomposed full-coding mode, the proposed method can only increase the bit rate by 0.84% and shorten the transcoding time by 54%. The second method combines machine learning theory and puts forward the transcoding framework of online training online classification, which uses the CU partition information of the original code stream and the CU partition information of the previous frame in the time domain. By using naive Bayesian classifier to predict the CU partition flag in the coding end, the CU partition mode is determined. The experimental results show that the proposed method can only increase the bit rate by 2.74% and shorten the transcoding time by about 72%.
【学位授予单位】：南京理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.81

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