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Multi-threaded syntax element partitioning for parallel entropy decoding

机译:多线程语法元素划分,用于并行熵解码

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摘要

Strong demand for high resolution video services leads to active studies on high speed video processing. Especially, widespread deployment of multi-core systems accelerates researches on high resolution video processing based on parallelization of multimedia software. Even if parallelization of other decoding steps on a multi-core platform may improve performance, entropy decoding often becomes a performance bottleneck since it should be processed sequentially. To resolve this concern, parallel entropy coding algorithms have been proposed. Syntax element partitioning is an algorithm for parallelization of Context Adaptive Binary Arithmetic Coding (CABAC). In this paper, we propose Multi-Threaded Syntax Element Partitioning (MT-SEP) for parallel entropy decoding. One major advantage of software parallel video decoding over hardware implementations will be that versatile video codecs can be implemented flexibly. We parallelized the KTA 2.7 decoder with the proposed technique on an Intel Quad-Core platform. We achieved up to 56% performance improvement using the proposed version of syntax element partitioning.
机译:对高分辨率视频服务的强烈需求导致对高速视频处理的积极研究。特别是,多核系统的广泛部署加速了基于多媒体软件并行化的高分辨率视频处理的研究。即使多核平台上其他解码步骤的并行化可以提高性能,但熵解码通常会成为性能瓶颈,因为它应按顺序进行处理。为了解决这个问题,已经提出了并行熵编码算法。语法元素划分是一种用于上下文自适应二进制算术编码(CABAC)并行化的算法。在本文中,我们提出了用于并行熵解码的多线程语法元素划分(MT-SEP)。与硬件实现相比,软件并行视频解码的一个主要优点是可以灵活地实现通用视频编解码器。我们在英特尔四核平台上将KTA 2.7解码器与提出的技术并行化。使用建议的语法元素分区版本,我们将性能提高了56%。

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