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Distributed compressed video sensing based on key frame secondary reconstruction

机译:基于关键帧二次重构的分布式压缩视频感知

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摘要

Distributed compressed video sensing scheme combines advantages of compressive sensing and distributed video coding to get better performance, in the meantime, adapts to the limited-resource wireless multimedia sensor network. However, in the conventional distributed compressed video sensing schemes, self-similarity and high sampling rate of the key frame have not been sufficiently utilized, and the overall computational complexity increases with the development of these schemes. To solve the aforementioned problems, we propose a novel distributed compressed video sensing scheme. A new key frame secondary reconstruction scheme is proposed, which further improves the quality of key frame and decreases computational complexity. The key frame’s initial reconstruction value is deeply exploited to assist the key frame secondary reconstruction. Then, a hypotheses set acquisition algorithm based on motion estimation is proposed to improve the quality of hypotheses set by optimizing the searching window under low complexity. Experimental results demonstrate that the overall performance of the proposed scheme outperforms that of the state-of-the-art methods.
机译:分布式压缩视频感知方案结合了压缩感知和分布式视频编码的优势,获得了较好的性能,同时适应资源有限的无线多媒体传感器网络。然而,在常规的分布式压缩视频感测方案中,关键帧的自相似性和高采样率尚未得到充分利用,并且随着这些方案的发展,整体计算复杂度增加。为了解决上述问题,我们提出了一种新颖的分布式压缩视频传感方案。提出了一种新的关键帧二次重构方案,该方案进一步提高了关键帧的质量并降低了计算复杂度。关键帧的初始重建值得到了深入的利用,以辅助关键帧的二次重建。然后,提出了一种基于运动估计的假设集获取算法,通过在低复杂度下优化搜索窗口来提高假设集的质量。实验结果表明,所提出方案的总体性能优于最新方法。

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