EXLPOITING MULTI VIEW CORRELATION FOR MAXIMIZING LIFE TIME OF WIRELESS SENSORS

Abstract

The wireless multimedia sensor networks is becoming popular and is used in many important applications like surveillance systems, health monitoring system, maintaining law and order systems monitoring and control of industrial processes. These applications require good quality of information to be transmitted and received by multimedia sensors. For wireless sensor networks multimedia applications are complex and require extra memory and processing power of the sensors. Since sensors are tiny devices, they have very limited memory and processing power. The lifetime of sensor depends on its remaining power, which is less likely to be recharged. Therefore multimedia applications, which need to be run on sensor networks, should consider the limitation of such networks. On wireless sensor networks, multi-view video processing is an important application. As sensors impose constraints like limited memory, storage and battery power, therefore, these limitations should be considered while designing multi view applications for such networks. The aim of this research is to study the existing correlation models in multi view videos and propose an approach which reduces battery consumption by minimizing the compression time of video frames based on the specific correlation model. A novel OPI model is discussed that is designed based on the decision of correlation value of sensor nodes. Algorithms for the extraction of overlapping part from the frame at the decoder and fusion of overlapping part with the modified frame at the decoder are discussed. Experimental results have been derived using OPI model and the results depict that by using this approach compression time of video frames is reduced as compared to originally encoded frames. Results show that there is remarkable decrease in compression time of processed frame using OPI model as compared to the compression time of original frame captured by the camera i.e. compression time of frames decreases from 10 to 20 percent and quality of image is also maintained as shown by PSNR values. PSNR values of reconstructed images range from 40 DBs to 45 DBs, which shows that quality of image is not degraded.