DISTRIBUTED IMAGE COMPRESSION FOR WIRELESS SENSOR NETWORKS

Abstract

Dense networks of wireless, battery-powered sensors are now gaining widespread attention due to their numerous applications in various fields, due to recent hardware advances, but key issues such as power consumption plague the widespread deployment of these sensor networks. However, in a dense sensor network, cross-sensor correlation can be exploited to reduce the communication power consumption. In this thesis, we examine a novel technique for distributed image compression in sensor networks. First, sensors are involved in registration of image from neighboring sensors, allowing sensors to identify a common region of overlap. The region is then compressed via spatial downsampling, and image super-resolution techniques are employed at the receiver to reconstruct an original-resolution estimate of the common area from the set of low-resolution sensor images. We evaluate the effectiveness of this technique using a set of sensor images gathered with an off-the-shelf digital camera.