NDLI: Noise-resilient distributed compressed video sensing using side-information-based belief propagation

Content Provider	IEEE Xplore Digital Library
Author	Yu Liu Xuqi Zhu Lin Zhang
Copyright Year	2012
Description	Author affiliation: Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China (Yu Liu; Xuqi Zhu; Lin Zhang)
Abstract	With the booming of energy-limited video devices ranging from visual sensors to camera cell phones, distributed compressed video sensing (DCVS) has developed as one of the efficient solutions that guarantee low complexity video encoding as well as acceptable recovery quality. In this paper, a noise-resilient DCVS scheme is proposed using side-information-based belief propagation (SI-BP). The video frames are encoded independently to a small number of measurements and recovered jointly based on the distributed video coding structure. The major contribution is the joint recovery algorithm SI-BP where the side information from the recovered frames is fully exploited to improve the original CSBP algorithm in both initialization and stopping criteria. As the estimated probability distribution of the DCT coefficients is introduced in the soft decision SI-BP iterations, our scheme is noise-resilient especially with low SNR. This advantage is verified by the simulation results on several video sequences.
Starting Page	350
Ending Page	390
File Size	1446054
Page Count	41
File Format	PDF
ISBN	9781467322010
e-ISBN	9781467322041
DOI	10.1109/ICNIDC.2012.6418780
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis PSNR Rate-distortion Sensors Complexity theory Side information Noise measurement Joints Compressed sensing Belief propagation Distributed video coding
Content Type	Text
Resource Type	Article

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