Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). In this paper, we propose a novel rate-distortion optimized DCVS codec, which takes advantage of a rate-distortion optimization (RDO) model based on the estimated correlation noise (CN) between a non-key frame and its side information (SI) to determine the optimal measurements allocation for the non-key frame. Because the actual CN can be more accurately recovered by our DCVS codec, it leads to more faithful reconstruction of the non-key frames by adding the recovered CN to the SI. The experimental results reveal that our DCVS codec significantly outperforms the legacy DCVS codecs in terms of both objective and subjective performance.

Because the perceptual compressive sensing framework can achieve a much better performance than the legacy compressive sensing framework, it is very promising for the compressive sensing based image compression system. In this paper, we propose an innovative adaptive perceptual block compressive sensing scheme. Firstly, a new block-based statistical metric which can more appropriately measure each block's sparsity and perceptual sensibility is devised. Then, the approximated theoretical minimum measurement number for each block is derived from the new block-based metric and used as weight for adaptive measurements allocation. The obtained experimental results show that our scheme can significantly enhance both objective and subjective performance of a perceptual compressive sensing framework.