A dictionary generation scheme for block-based compressed video sensing
A dictionary generation scheme for block-based compressed video sensing
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DOI：
10.1109/icspcc.2011.6061675
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Liu Haixiao;Song Bin;Q. Hao;Qiu Zhiliang
中科院分区：
文献类型：
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作者：
Liu Haixiao;Song Bin;Q. Hao;Qiu Zhiliang
压缩感知是一种利用变换域中信号的稀疏性来进行奈奎斯特率以下采样的新技术，其低复杂度在视频编码应用中具有很大的潜力。然而，传统的标准正交基不能为压缩视频感知提供足够稀疏的表示。因此，如何在视频中利用时间/空间冗余是主要的挑战。在本文中，我们提出了一种基于块的压缩视频感知字典生成方案。通过测量域的运动估计，将从参照系中提取的块作为原子初始化字典。然后得到当前帧的估计，并利用该估计更新字典。该算法以迭代的方式为视频的稀疏表示提供了更精确的字典。实验结果表明，该方法与现有方法性能相当，平均信噪比提高0.8dB ~ 2dB。
Compressed sensing is a novel technology that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate, and has great potential in video coding applications for its low-complexity. However, the traditional orthonormal basis cannot be adopted to provide a sparse enough representation for compressed video sensing. Therefore, how to use the temporal/spatial redundancy in video is the main challenge. In this paper, we propose a dictionary generation scheme for block-based compressed video sensing. By means of motion estimation in measurement domain, the dictionary is initialized using blocks extracted from the reference frame as the atoms. Then an estimation of the current frame can be obtained, which is in turn employed to update the dictionary. The proposed algorithm provides a more accurate dictionary for the sparse representation of video in an iterative fashion. And the experimental results show that our proposal offers comparable performance to other existing methods, with a 0.8dB to 2dB improvement in the average PSNR.