Authors:
George Tzagkarakis
1
;
Panagiotis Tsakalides
2
and
Jean-Luc Starck
3
Affiliations:
1
Foundation for Research & Technology-Hellas (FORTH) - Institute of Computer Science (ICS) and EONOS Investment Technologies, Greece
;
2
Foundation for Research & Technology-Hellas (FORTH) - Institute of Computer Science (ICS), Greece
;
3
Centre de Saclay, France
Keyword(s):
Compressive Video Sensing, Measurement Allocation, Remote Imaging, MPEGx
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Coding and Compression
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
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 ada
ptive measurement allocation mechanism is introduced, which is combined with the CVS approach achieving an improved performance when compared with the basic MPEG-2 standard.
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