Perceptually weighted compressed sensing for video acquisition
International Conference on Pervasive and Embedded Computing • 2015
Publication Information
Authors
S. Elsayed, M. Elsabrouty
Keywords
Not Available
Journal
International Conference on Pervasive and Embedded Computing
Publisher
IEEE
Volume
Not Available
Issue
Not Available
Pages
209-216
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
Efficient video acquisition and coding techniques have received increasing attention
due to the wide spread of multimedia telecommunication. Compressed Sensing (CS) is
an emerging technology, which enables acquiring video in a compressed manner. CS
proves to be very powerful for energy constrained devices that benefit from processing
at lower sampling rates. In this paper, a framework for compressed video sensing
(CVS) that relies on an efficient fixed perceptual weighting strategy is adopted for
acquisition and recovery. The proposed compressed sensing strategy focuses the
measurements on the most perceptually pronounced coefficients. Three weighting
schemes are developed and compared with standard CS. Simulation results
demonstrate that the proposed framework provides a significant improvement in its
three different setups over standard CS in terms of both standard and perceptual
objective quality assessment metrics.
due to the wide spread of multimedia telecommunication. Compressed Sensing (CS) is
an emerging technology, which enables acquiring video in a compressed manner. CS
proves to be very powerful for energy constrained devices that benefit from processing
at lower sampling rates. In this paper, a framework for compressed video sensing
(CVS) that relies on an efficient fixed perceptual weighting strategy is adopted for
acquisition and recovery. The proposed compressed sensing strategy focuses the
measurements on the most perceptually pronounced coefficients. Three weighting
schemes are developed and compared with standard CS. Simulation results
demonstrate that the proposed framework provides a significant improvement in its
three different setups over standard CS in terms of both standard and perceptual
objective quality assessment metrics.
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