Perceptually-aware distributed compressive video sensing

By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring the best ways to utilize the spatial/temporal redundancy of video data to achieve efficient sparse representation and reconstruction at the decoder. Unlike the existing DCVS schemes, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel perceptually-aware DCVS codec. Based on online estimation of the correlation noise between a non-key frame and its side information (SI) considering the effect of human visual system (HVS), we design an efficient perceptually-aware block compressive sensing scheme for a non-key frame in our DCVS codec, in order to more accurately reconstruct the salient regions in the video frames. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.