Multiview Image Block Compressive Sensing with Joint Multiphase Decoding for Visual Sensor Network

Abstract

In this article, a multiview image compression framework, which involves the use of Block-based Compressive Sensing (BCS) and Joint Multiphase Decoding (JMD), is proposed for a Visual Sensor Network (VSN). In the proposed framework, one of the sensor nodes is configured to serve as the reference node, the others as nonreference nodes. The images are encoded independently using the BCS to produce two observed measurements that are transmitted to the host workstation. In this case, the nonreference nodes always encoded the images (I NR ) at a lower subrate when compared with the images from the reference nodes (I R ). The idea is to improve the reconstruction of I NR using I R . After the two observed measurements are received by the host workstation, they are first decoded independently, then image registration is applied to align I R onto the same plane of I NR . The aligned I R is then fused with I NR , using wavelets to produce the projected image I P . Subsequently, the difference between the measurements of the I P and I NR is calculated. The difference is then decoded and added to I P to produce the final reconstructed I NR . The simulation results show that the proposed framework is able to improve the quality of I NR on average by 2dB to 3dB at lower subrates when compared with other Compressive Sensing (CS)--based multiview image compression frameworks.