Adaptive Distributed Compressed Video Sensing

Compressed sensing is a state-of-the-art technology which can significantly reduce the number of sampled data in sparse signal acquisition. This paper studies the distributed compressed sensing (DISCOS) of video signals. To this end, we propose adaptive adjustments to the block-based (local) measurement rate, the frame-based (global) measurement rate, and the sparse dictionary size, thus forming an adaptive DISCOS scheme (aDISCOS). Two adjustments on measurement rates are based on the spatial and temporal sparsity that is obtained through an analysis on the block-type and the inter-frame motion, while the sparse dictionary size is adjusted according to the motion information. All analyses are implemented at the decoder side and the analysis results are sent back to the encoder via a feedback channel, yielding a low-complexity encoding (to meet the equirement of a distributed coding scheme). Simulation results show that the proposed aDISCOS achieves a superior rate-distortion performance as well as better visual quality, when compared with the original DISCOS scheme.