A novel distributed compressive video sensing based on hybrid sparse basis

Abstract

Distributed compressive video sensing (DCVS) is a new emerging video codec that incorporates advantages of distributed video coding (DVC) and compressive sensing (CS). However, due to the absence of a good sparse basis, the DCVS does not achieve ideal compressing efficiency compared with the traditional video codec, such as MPEG-4, H.264, etc. This paper proposes a new hybrid sparse basis, which combines the image-block prediction and DCT basis. Adaptive block-based prediction is employed to learn block-prediction basis by exploiting temporal correlation among successive frames. Based on linear DCT basis and predicted basis, the hybrid sparse basis can achieve sparser representation with lower complexity. The experiment results indicate that the proposal outperforms the state-of-the-art DCVS schemes on both visual quality and average PSNR. In addition, an iterative fashion proposed in the decoder can enhance the sparsity of the hybrid sparse basis and improve the rate-distortion performance significantly.