Distributed Compressed Video Sensing in Camera Sensor Networks

Abstract

With the booming of video devices ranging from low-power visual sensors to mobile phones, the video sequences captured by these simple devices must be compressed easily and reconstructed by relatively more powerful servers. In such scenarios, distributed compressed video sensing (DCVS), combining distributed video coding (DVC) and compressed sensing (CS), is developed as a novel and powerful signal-sensing and compression algorithm for video signals. In DCVS, video frames can be compressed to a few measurements in a separate manner, while the interframe correlation is explored by the joint recovery algorithm. In this paper, a new DCVS joint recovery scheme using side-information-based belief propagation (SI-BP) is proposed to exploit both the intraframe and interframe correlations, which is particularly efficient over error-prone channels. The DCVS scheme using SI-BP is designed over two frame signal models, the mixture Gaussian (MG) model and the wavelet hidden Markov tree (WHMT) model. Simulation results evaluated on two video sequences illustrate that the SI-BP-based DCVS scheme is error resilient when the measurements are transmitted through the noisy wireless channels.