Multi-view image compression for Visual Sensor Network based on Block Compressive Sensing and multi-phase joint decoding

摘要

In this paper, a multi-view image compression framework for the Visual Sensor Network (VSN) is proposed that involve the use of Block-based Compressive Sensing (BCS) and multi-phase joint decoding. In the proposed framework, one of the sensor nodes (encoder) is configured to serve as the reference node, whereas the others as non-reference nodes. The images captured by the reference and non-reference nodes are referred as I and I respectively. They are encoded independently using the BCS to produce two measurements that will be transmitted to the host workstation (decoder). In this case, I is always encoded at a lower bitrate when compared to I, because the idea is to improve the reconstruction of I with the help of IR. After the host workstation receives the two measurements, independent decoding is performed first, and then image registration is applied to project I onto the same plane of I. The projected I is then fused with I using wavelets. Subsequently, the difference between the measurement of the fused image and the measurement of I is calculated. The difference is then decoded and added to If to produce the final improved version of I. The simulation results show that the proposed framework is able to improve the quality of I by 1dB to ~3dB at lower bitrates, when compared to the conventional BCS.