A genetic approach for improving the side information in Wyner-Ziv video coding with long duration GOP

C. Yaacoub, J. Farah, Chadi Jabroun
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Abstract

This work tackles the problem of side information generation for the case of large-duration GOPs in distributed video coding. Based on a previously developed technique for side-information enhancement, we develop a genetic algorithm particularly designed for large GOPs, taking into account the GOP size, the additional bitrate incurred by encoding hash information, as well as the decoding complexity. The proposed algorithm makes use of different interpolation methods available in the literature in a fusion-based approach. A significant gain in the average PSNR that can reach 2 dB is observed with respect to the best performing interpolation technique, while the algorithm is run for no more than 18% of the total number of blocks in a given video sequence. On the other hand, while the encoding complexity is a main concern in distributed video coding, the proposed solution incurs no additional complexity at the encoder side in the case of hash-based Wyner-Ziv video coding.
一种改进长时间GOP的Wyner-Ziv视频编码侧信息的遗传方法
本文解决了分布式视频编码中大持续时间GOPs情况下的边信息生成问题。基于先前开发的侧信息增强技术,我们开发了一种专门为大型GOPs设计的遗传算法,考虑到GOP大小,编码哈希信息所产生的额外比特率以及解码复杂性。提出的算法在基于融合的方法中利用了文献中可用的不同插值方法。相对于性能最好的插值技术,可以观察到平均PSNR可以达到2 dB的显着增益,而该算法在给定视频序列中运行不超过18%的块总数。另一方面,虽然编码复杂性是分布式视频编码的主要问题,但在基于哈希的Wyner-Ziv视频编码的情况下,所提出的解决方案不会在编码器端产生额外的复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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