Compressive Video Sensing with Adaptive Measurement Allocation for Improving MPEGx Performance

Abstract

Remote imaging systems, such as unmanned aerial vehicles (UAVs) and terrestrial-based visual sensor networks, have been increasingly used in surveillance and reconnaissance both at the civilian and battlegroup levels. Nevertheless, most existing solutions do not adequately accommodate efficient operation, since limited power, processing and bandwidth resources is a major barrier for abandoned visual sensors and for light UAVs, not well addressed by MPEGx compression standards. To cope with the growing compression ratios, required for all remote imaging applications to minimize the payloads, existing MPEGx compression profiles may result in poor image quality. In this paper, the inherent property of compressive sensing, acting simultaneously as a sensing and compression framework, is exploited to built a compressive video sensing (CVS) system by modifying the standard MPEGx structure, such as to cope with the limitations of a resource-restricted visual sensing system. Besides, an adaptive measurement allocation mechanism is introduced, which is combined with the CVS approach achieving an improved performance when compared with the basic MPEG-2 standard.