Compressive Video Sensing with Adaptive Measurement Allocation for Improving MPEGx Performance

George Tzagkarakis, Panagiotis Tsakalides, Jean-Luc Starck

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.

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Paper Citation


in Harvard Style

Tzagkarakis G., Tsakalides P. and Starck J. (2015). Compressive Video Sensing with Adaptive Measurement Allocation for Improving MPEGx Performance . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 254-259. DOI: 10.5220/0005360602540259


in Bibtex Style

@conference{visapp15,
author={George Tzagkarakis and Panagiotis Tsakalides and Jean-Luc Starck},
title={Compressive Video Sensing with Adaptive Measurement Allocation for Improving MPEGx Performance},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={254-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005360602540259},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Compressive Video Sensing with Adaptive Measurement Allocation for Improving MPEGx Performance
SN - 978-989-758-089-5
AU - Tzagkarakis G.
AU - Tsakalides P.
AU - Starck J.
PY - 2015
SP - 254
EP - 259
DO - 10.5220/0005360602540259