Transcoding of Next Generation Distributed Video Codec for Mobile Video

V. K. Kodavalla
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引用次数: 2

Abstract

Presently most popular and widely adopted video coding standards include MPEGx, H.26x, DivX, AVSx and VPx. These standards are based on predictive video coding method, where encoder is designed to be more complex (about 10x) than the decoder. These predictive video coding standards are suitable for existing applications, including broadcast and video-ondemand. However, there are other evolving applications, including wireless surveillance, multi-view, one-time-use video cameras and mobile video, which demands encoder complexity being lower. This is in contrast to the existing video coding standards. To cater to these evolving applications, “Distributed Video Coding” (DVC) has progressed as an inventive solution. The uniqueness of distributed video coding solution is its deployment of channel coding methods into the source-coding problem, in reducing encoder complexity, even at the expense of increase in complexity of the decoder. Never the less, for a sub-set of evolving applications (for e.g., mobile video), both encoder and decoder need to be of lower complexity. For this video conference application, even the DVC is also not suitable. This paper demonstrates a method for achieving low complexity encoder and decoder from the end-user point of view, by using transcoding of DVC and H.264 Advanced Video Codec (AVC) in the network. The elaborate research and proof-of-concept models developed, over a period of several years, have become the basis for this paper.
面向移动视频的下一代分布式视频编解码器的转码
目前最流行和广泛采用的视频编码标准包括MPEGx、H.26x、DivX、AVSx和VPx。这些标准基于预测视频编码方法,其中编码器被设计为比解码器更复杂(约10倍)。这些预测性视频编码标准适用于现有的应用,包括广播和视频点播。然而,还有其他不断发展的应用,包括无线监控、多视图、一次性视频摄像机和移动视频,这些应用要求编码器的复杂性更低。这与现有的视频编码标准形成了对比。为了迎合这些不断发展的应用,“分布式视频编码”(DVC)作为一种创造性的解决方案已经取得了进展。分布式视频编码方案的独特之处在于它将信道编码方法部署到源编码问题中,在降低编码器复杂度的同时,不惜以增加解码器复杂度为代价。然而,对于不断发展的应用程序的子集(例如,移动视频),编码器和解码器都需要降低复杂性。对于这种视频会议应用,即使是DVC也不适合。本文从终端用户的角度阐述了一种利用网络中DVC和H.264高级视频编解码器(AVC)的转码实现低复杂度编码器和解码器的方法。经过几年的研究和开发的概念验证模型已成为本文的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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