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Measurement Compression in Compressive Sampling Based Distributed Video Coding

机译:基于压缩采样的分布式视频编码中的测量压缩

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Compressive sampling (CS) theory and distributed video coding (DVC) are two techniques suitable to scenarios where a video codec with simple encoder and complex decoder is desired. The combination of CS theory and DVC is a new research trend in this field and several integrated schemes have now appeared. However, in these existing integrated schemes, the dependencies between measurements of successive image frames have not yet been exploited. Recently we proposed a Gaussian distribution model to describe the correlations of measurements between a CS frame and its side information in a previous paper. In this paper, we extend the Gaussian model to correlations of measurements between two successive key frames. Based on this model the measurements of key frame can be compressed using a channel coder, similar to that in DVC. Experiment results indicate that the measurement compression ratio of the proposed compression scheme achieves 48.96% -88.12% for key frames when measurement rate of key frame is 50%.
机译:压缩采样(CS)理论和分布式视频编码(DVC)是两种适用于需要具有简单编码器和复杂解码器的视频编解码器的场景的技术。 CS理论与DVC的结合是该领域的新研究趋势,现已出现了几种集成方案。然而,在这些现有的集成方案中,尚未利用连续图像帧的测量之间的依赖性。最近,我们提出了一种高斯分布模型来描述CS帧与其辅助信息之间的测量相关性。在本文中,我们将高斯模型扩展到两个连续关键帧之间的测量相关性。基于此模型,可以使用类似于DVC中的通道编码器来压缩关键帧的测量值。实验结果表明,当关键帧的测量率为50%时,所提出的压缩方案对关键帧的测量压缩率达到48.96%-88.12%。

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