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Efficient Bit Rate Transcoding for High Efficiency Video Coding

机译：用于高效视频编码的高效比特率转码

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摘要

High efficiency video coding (HEVC) shows a significant advance in compression efficiency and is considered to be the successor of H.264/AVC. To incorporate the HEVC standard into real-life network applications and a diversity of other applications, efficient bit rate adaptation (transrating) algorithms are required. A current problem of transrating for HEVC is the high computational complexity associated with the encoder part of such a cascaded pixel domain transcoder. This paper focuses on deriving an optimal strategy for reducing the transcoding complexity with a complexity-scalable scheme. We propose different transcoding techniques which are able to reduce the transcoding complexity in both CU and PU optimization levels. At the CU level, CUs can be evaluated in top-to-bottom or bottom-to-top flows, in which the coding information of the input video stream is utilized to reduce the number of evaluations or to early terminate certain evaluations. At the PU level, the PU candidates are adaptively selected based on the probability of PU sizes and the co-located input PU partitioning. Moreover, with the use of different proposed methods, a complexity-scalable transrating scheme can be achieved. Furthermore, the transcoding complexity can be effectively controlled by the machine learning based approach. Simulations show that the proposed techniques provide a superior transcoding performance compared to the state-of-the-art related works. Additionally, the proposed methods can achieve a range of trade-offs between transrating complexity and coding performance. From the proposed schemes, the fastest approach is able to reduce the complexity by 82% while keeping the bitrate loss below 3%.
机译：高效视频编码（HEVC）在压缩效率方面显示出显着进步，被认为是H.264 / AVC的后继产品。为了将HEVC标准并入现实生活中的网络应用以及其他各种应用中，需要高效的比特率自适应（转换）算法。用于HEVC的当前转换问题是与这种级联像素域代码转换器的编码器部分相关联的高计算复杂性。本文着重于推导一种采用复杂度可扩展方案降低转码复杂度的最佳策略。我们提出了不同的代码转换技术，它们能够降低CU和PU优化级别的代码转换复杂性。在CU级别上，可以按从上到下或从下到上的流程对CU进行评估，其中，使用输入视频流的编码信息来减少评估次数或提前终止某些评估。在PU级别，基于PU大小的概率和位于同一位置的输入PU分区来自适应地选择PU候选对象。此外，通过使用不同的提议方法，可以实现复杂度可缩放的转换方案。此外，可以通过基于机器学习的方法来有效地控制代码转换的复杂性。仿真表明，与最新技术相关的工作相比，所提出的技术提供了卓越的代码转换性能。另外，所提出的方法可以在转换复杂度和编码性能之间取得一系列权衡。从提出的方案中，最快的方法能够将复杂度降低82％，同时将比特率损失保持在3％以下。

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