The Adaptive Distributed Source Coding of Multi-View Images in Camera Sensor Networks

We show that distributed source coding of multi-view images in camera sensor networks (CSNs) using adaptive modules can come close to the Slepian-Wolf bound. In a systematic scenario with limited node abilities, work by Slepian and Wolf suggest that it is possible to encode statistically dependent signals in a distributed manner to the same rate as with a system where the signals are jointly encoded. We considered three nodes (PN, CN and CNs), which are statistically depended. Different distributed architecture solutions are proposed based on a parent node and child node framework. A PN sends the whole image whereas a CNs/CN only partially, using an adaptive coding based on adaptive module-operation at a rate close to theoretical bound H(CNs|PN)/H(CN|PN,CNs). CNs sends sub-sampled image and encodes the rest of image, however CN encodes all image. In other words, the proposed scheme allows independent encoding and jointly decoding of views. Experimental results show performance close to the information-theoretic limit. Furthermore, good performance of the proposed architecture with adaptive scheme shows significant improvement over previous work. key words: distributed source coding, camera sensor networks, multi-view images, Slepian-Wolf theory, parent node, child node