Frame Level Quantization Control with Temporal Distortion Propagation Model for Video Coding

Haibing Yin, Dong Li, Yu Lu, Yang Zhou
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引用次数: 1

Abstract

In video coder, inter-frame prediction causes distortion propagation among temporally adjacent frames, which complicates frame level bit allocation and quantization control. Quantization parameter cascading (QPC) is generally employed to determine a sequence of quantization parameter for dependent rate distortion optimization (RDO). This paper proposes a general framework for temporal dependency analysis by lever-aging a distortion propagation model. The amount of distortion propagated from the temporally adjacent frames is measured by tree-style dependent analysis. Then, a trellis comprised of frame level quantization parameters of one GOP is constructed to achieve global optimization via branch-prune based dynamic programming. The simulation results verify that the frame level QPC algorithm with the proposed distortion model achieves up to 1.2dB—1.5dB PSNR improvement on average, with smaller temporal distortion fluctuation contributed by efficient bit allocation.
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基于时间失真传播模型的帧级量化控制视频编码
在视频编码器中,帧间预测会导致失真在时间相邻帧之间传播,使帧级比特分配和量化控制变得复杂。在相关率失真优化(RDO)中,通常采用量化参数级联(QPC)来确定量化参数序列。本文提出了一种利用失真传播模型进行时间相关性分析的通用框架。从时间上相邻的帧传播的失真量通过树式相关分析来测量。然后,通过基于分支剪枝的动态规划,构建了由单个GOP的帧级量化参数组成的网格,实现全局优化。仿真结果表明,采用该失真模型的帧级QPC算法平均可提高1.2 db ~ 1.5 db的PSNR,且有效的比特分配有助于减小时间失真波动。
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