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An error-resilient video coding framework with soft reset and end-to-end distortion optimization

机译：具有软复位和端到端失真优化功能的防错视频编码框架

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摘要

Temporal prediction plays a crucial role in most video coding applications. However, due to error propagation via the prediction loop, it also increases the vulnerability to channel loss. The standard counter measure to mitigate error propagation is the `intra refresh' mode, which in effect resets temporal prediction to block error propagation, but at a significant rate overhead. This on/off switch for temporal prediction is overly crude to optimize the compression-resilience tradeoff. In this paper, we propose a novel framework that significantly expands the options available to counter error propagation by introducing optimally controlled soft resets, wherein intra and inter predictions are combined with adjustable weights to control the dependency on previous frames while accounting for the overall rate and distortion. Since the optimal control of such soft resets can only be achieved if the encoder can effectively estimate its impact on the end-to-end distortion (EED), we propose to extend the well known recursive optimal per-pixel estimation (ROPE) approach to accurately account for the soft reset mode, then optimize encoder mode decisions to minimize the estimated EED for the given rate. Experimental results show that the proposed framework achieves significant performance gains for video streaming over unreliable networks.
机译：时间预测在大多数视频编码应用中起着至关重要的作用。但是，由于错误会通过预测循环传播，因此也增加了信道丢失的风险。减轻错误传播的标准对策是“内部刷新”模式，该模式实际上会重置时间预测以阻止错误传播，但开销要大得多。这种用于时间预测的通/断开关过于粗糙，无法优化压缩弹性的权衡。在本文中，我们提出了一种新颖的框架，该框架通过引入最优控制的软重置来显着扩展可用于对抗错误传播的选项，其中，帧内和帧间预测与可调整的权重相结合，以控制对先前帧的依赖性，同时考虑总速率和失真。由于只有在编码器可以有效地估计其对端到端失真（EED）的影响的情况下，才能实现对此类软重置的最佳控制，因此我们建议将众所周知的递归最佳每像素估计（ROPE）方法扩展为准确地说明软复位模式，然后优化编码器模式决策，以最小化给定速率下的估计EED。实验结果表明，所提出的框架在不可靠的网络上实现了视频流的显着性能提升。

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