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Optimizing HEVC CABAC decoding with a context model cache and application-specific prefetching

机译:使用上下文模型缓存和特定于应用程序的预取来优化HEVC CABAC解码

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摘要

Context-based Adaptive Binary Arithmetic Coding is the entropy coding module in the most recent JCT-VC video coding standard HEVC/H.265. As in the predecessor H.264/AVC, CABAC is a well-known throughput bottleneck due to its strong data dependencies. Beside other optimizations, the replacement of the context model memory by a smaller cache has been proposed, resulting in an improved clock frequency. However, the effect of potential cache misses has not been properly evaluated. Our work fills this gap and performs an extensive evaluation of different cache configurations. Furthermore, it is demonstrated that application-specific context model prefetching can effectively reduce the miss rate and make it negligible. Best overall performance results were achieved with caches of two and four lines, where each cache line consists of four context models. Four cache lines allow a speed-up of 10% to 12% for all video configurations while two cache lines improve the throughput by 9% to 15% for high bitrate videos and by 1% to 4% for low bitrate videos.
机译:基于上下文的自适应二进制算术编码是最新的JCT-VC视频编码标准HEVC / H.265中的熵编码模块。与以前的H.264 / AVC一样,CABAC由于其强大的数据依赖性而成为众所周知的吞吐量瓶颈。除了其他优化之外,还提出了使用较小的缓存替换上下文模型存储器的方法,从而提高了时钟频率。但是,尚未正确评估潜在的高速缓存未命中的影响。我们的工作填补了这一空白,并对不同的缓存配置进行了广泛的评估。此外,证明了特定于应用程序的上下文模型预取可以有效地降低未命中率并使它可以忽略。使用两行和四行的缓存可以获得最佳的总体性能结果,其中每条缓存行由四个上下文模型组成。四个高速缓存行可将所有视频配置的速度提高10%至12%,而两个高速缓存行可将高比特率视频的吞吐量提高9%至15%,将低比特率视频的吞吐量提高1%至4%。

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