A dictionary generation scheme for block-based compressed video sensing

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.