Distributed Compressed Video Sensing in Camera Sensor Networks

Yu Liu,Sung Ho Cho,Lin Zhang,Xuqi Zhu

doi:10.1155/2012/352167

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.

Highlights

Current video coding paradigms, such as MPEG and the ITU-T H.26x, are traditionally designed for the applications followed the so-called “broadcast” model, as shown in the left part of Figure 1
We select the commonly used YUV 4 : 2 : 0 video sequences “Coastguard” and “Foreman” to test the performance of the distributed compressed video sensing (DCVS) algorithm, where the first one is in QCIF format and the last one is in CIF format
The peak signal to noise ratios (PSNRs) performances of the side-information-based belief propagation (SI-belief propagation (BP)) scheme based on wavelet hidden Markov tree (WHMT) and mixture Gaussian (MG) models and the gradient projection for sparse reconstruction (GPSR) scheme are compared at the average compression ratio Ravg = 0.4 with the changing standard deviation of the channel noise

Summary

Introduction

Current video coding paradigms, such as MPEG and the ITU-T H.26x, are traditionally designed for the applications followed the so-called “broadcast” model, as shown in the left part of Figure 1. A classical framework called power-efficient, robust, high-compression, syndrome-based multimedia coding (PRISM) is proposed in [5] They encoded the P frames following the same procedures as that of I frames but at lower rates, while the motion search is used at the decoder to estimate the side information from the neighboring recovered I frames. The layered Wyner-Ziv video coding system [8] achieved robust video transmission by adding Wyner-Ziv bitstream layers as the enhanced layers Another recent direction to achieve light encoder is to exploit the intraframe signal’s sparsity property, known as compressed sensing (CS) [9,10,11,12,13,14].

Frame Signal Models

Implementation of DCVS Using SI-BP

Simulation Results and Analysis

Conclusions and Future Works