Joint optimization of sampling and reconstruction for distributed compressive video sensing

Distributed compressive video sensing (DCVS) is a paradigm for low-complexity video compression. To improve the rate-distortion (RD) performance of DCVS, the existing works generally separately improve either sampling efficiency or reconstruction performance. We propose an RD efficient DCVS codec that takes advantage of the side information (SI) at the decoder side to jointly improve both sampling efficiency and reconstruction performance. Specifically, on the one hand, by making use of the statistics of the SI, a general content-dependent sampling (CDS) scheme is developed to enhance the sampling efficiency. The proposed CDS scheme can make the important frequency components of each video frame more efficiently sampled to facilitate their accurate reconstruction. On the other hand, by exploiting the residual between each nonkey frame and its SI, a joint residual sparsity and Tikhonov regularization approach is proposed to regularize the multihypothesis (MH) prediction optimization problem to obtain a more accurate MH prediction of the nonkey frame. Meanwhile, an efficient algorithm based on the split Bregman iteration technique is designed to solve the joint regularized optimization problem. The experimental results show that our DCVS codec significantly outperforms the existing DCVS codecs in terms of the RD performance while being computationally efficient.