Hierarchical frame based spatial–temporal recovery for video compressive sensing coding

Abstract

In this paper, the divide-and-conquer based hierarchical video compressive sensing (CS) coding framework is proposed, in which the whole video is independently divided into non-overlapped blocks of the hierarchical frames. The proposed framework outperforms the traditional framework through the better exploitation of frames correlation with reference frames, the unequal sample subrates setting among frames in different layers and the reduction of the error propagation. At the encoder, compared with the video/frame based CS, the proposed hierarchical block based CS matrix can be easily implemented and stored in hardware. Each measurement of the block in a different hierarchical frame is obtained with the different sample subrate. At the decoder, by considering the spatial and temporal correlations of the video sequence, a spatial–temporal sparse representation based recovery is proposed, in which the similar blocks in the current frame and these recovered reference frames are organized as a spatial–temporal group unit to be represented sparsely. Finally, the recovery problem of video compressive sensing coding can be solved by adopting the split Bregman iteration. Experimental results show that the proposed method achieves better performance against many state-of-the-art still-image CS and video CS recovery algorithms.