基于多假设运动补偿去噪的迭代边信息改进算法
doi: 10.3724/SP.J.1146.2011.00355
An Iterative Side Information Refinement Method Based on MHMCP Denoising in Distributed Video Coding
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摘要: 在分布式视频压缩系统中,边信息的质量对编码效率有至关重要的作用。利用已解码图像信息提高边信息质量是近年来研究的热点之一。根据边信息和已解码信息与原始信息之间的噪声关系,该文提出了一种基于多假设运动补偿去噪的迭代边信息改进算法。首先对原始边信息进行多假设运动补偿去噪,生成质量更好而不仅是相似的补偿图像,然后利用每个码平面的解码信息对边信息进行迭代改进。实验结果表明,该算法能减少码流,显著提高Wyner-Ziv (WZ)帧质量,从而有效的改善分布式视频压缩的率失真性能。Abstract: In the Distributed Video Coding (DVC), the quality of Side Information (SI) has a critical impact on the coding efficiency and Rate-Distortion (RD) performance. Improving SI quality by extracting motion information from decoded frames becomes a new hotspot research area in recent years. According to noise correlation between SI, partial decoded Wyner-Ziv (WZ) frame and source, a novel iterative SI refinement method based on Multi-Hypothesis Motion-Compensated Prediction (MHMCP) denoising is proposed. In this scheme, firstly the original SI is refined by MHMCP denoising and a better motion compensated frame is generated instead of a similar one. Then it is refined iteratively by refinement module after each bit-plane is decoded. Experimental results show that the proposed strategy can significantly improve quality of WZ frames and reduce bit rate, thereby improve effectively the RD performance of DVC.
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