Perceptually weighted compressed sensing for video acquisition
International Conference on Pervasive and Embedded Computing • 2015
معلومات البحث
المؤلفون
S. Elsayed, M. Elsabrouty
الكلمات المفتاحية
Not Available
المجلة العلمية
International Conference on Pervasive and Embedded Computing
الناشر
IEEE
المجلد
Not Available
العدد
Not Available
الصفحات
209-216
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
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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