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Belief propagation‐based compressed video sensing for mobile Internet
Author(s) -
Tian Fang,
Liu Haixiao,
Song Bin,
Qin Hao,
Ren Guangliang
Publication year - 2015
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3023
Subject(s) - computer science , compressed sensing , wireless , real time computing , the internet , markov chain , artificial intelligence , computer vision , telecommunications , machine learning , world wide web
Summary The very high data rate applications such as streaming high‐definition video have attracted much attention of researchers in 5G systems. However, the design of 5G communication terminals faces several challenges, including how to reduce the computational burden of signal acquisition at the terminals and how to improve both the reconstruction quality and robust performance in wireless channels. How simple and fast online video acquisition can be implemented and how the original images can be recovered effectively by an offline algorithm for mobile Internet are taken into consideration in this paper. Specially, we firstly propose a Markov chain model to characterize variations of the video sequences' temporal correlation, wherein we utilize the measurement residue that reflects the intensity changes to estimate the transition matrix. Then, a compressed video sensing framework for mobile Internet is presented with the belief propagation algorithm and the proposed video signal model. Numerical results show that our proposal could efficiently utilize the temporal correlation in video and then obtain both the improved reconstruction quality and robust performance in wireless communication systems. Copyright © 2015 John Wiley & Sons, Ltd.