An unequally protected Distributed Compressed Video Sensing algorithm

Distributed Compressed Video Sensing (DCVS) has developed as one of the efficient solutions that guarantee low complexity video compression. In this paper, a novel DCVS algorithm with unequal protection of the video signal´s elements is proposed. The new algorithm utilizes not only the sparsity and probability distribution of the video signal but also its particular unequal significance feature. Based on this feature, we design the structured irregular low-density sensing matrix to sample the signal. From the analysis and simulation results, it is confirmed that our method has higher recovery quality than the conventional Bayesian Compressed Sensing (CS) using Belief Propagation (BP). Moreover, the excellent noise-resilience of BP is preserved in our algorithm comparing to the DCVS schemes using optimization recovery.