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Performance Analysis of Inter-Layer Prediction in Scalable Video Coding Extension of H.264/AVC

机译:H.264 / AVC可伸缩视频编码扩展中层间预测的性能分析

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摘要

Scalable video coding (SVC) is a good approach for video services over heterogeneous networks. To achieve efficient video broadcasting systems, a good understanding on the performance of coding tools in SVC is necessary. In this paper, the efficiency of inter-layer prediction tools in SVC extension of H.264/AVC is thoroughly investigated by simulations. First, it is shown that inter-layer prediction is more efficient for fast/complex sequences than for slow/simple scenarios. Second, among the three inter-layer prediction methods, inter-layer residual prediction contributes most gains in simulations suggested by SVC standardization group. Nevertheless, all of them are comparatively less efficient for sequences with many details in the environment of spatial scalability. In addition, medium-grain quality scalability (MGS) outperforms coarse-grain quality scalability (CGS): MGS is able to provide up to 1 dB gain over CGS while keeping an even higher flexibility. However, MGS introduces a relatively large PSNR fluctuation which impacts visual quality.
机译:可伸缩视频编码(SVC)是异构网络上视频服务的一种好方法。为了实现有效的视频广播系统,有必要对SVC中的编码工具的性能有很好的了解。通过仿真,对H.264 / AVC的SVC扩展中层间预测工具的效率进行了深入研究。首先,表明对于快速/复杂的序列,层间预测比慢/简单的场景更有效。其次,在三种层间预测方法中,层间残差预测在SVC标准化小组建议的仿真中贡献最大。但是,在空间可伸缩性环境中,对于具有许多细节的序列,它们的效率都相对较低。此外,中等粒度质量可伸缩性(MGS)优于粗粒度质量可伸缩性(CGS):MGS能够提供比CGS高达1 dB的增益,同时保持更高的灵活性。但是,MGS引入了较大的PSNR波动,这会影响视觉质量。

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