Special Section on Compressive Sensing for Imaging

Video compressed sensing using iterative self-similarity modeling and residual reconstruction

[+] Author Affiliations
Yookyung Kim

University of Arizona, Department of Electrical and Computer Engineering, 1230 E Speedway Boulevard, Tucson, Arizona

Han Oh

Samsung Electronics, 416 Maetan 3(sam)-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, Republic of Korea

Ali Bilgin

University of Arizona, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, and the BIO5 Institute, 1657 E. Helen Street, Tucson, Arizona

J. Electron. Imaging. 22(2), 021005 (Feb 04, 2013). doi:10.1117/1.JEI.22.2.021005
History: Received July 27, 2012; Revised November 20, 2012; Accepted January 2, 2013
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Abstract.  Compressed sensing (CS) has great potential for use in video data acquisition and storage because it makes it unnecessary to collect an enormous amount of data and to perform the computationally demanding compression process. We propose an effective CS algorithm for video that consists of two iterative stages. In the first stage, frames containing the dominant structure are estimated. These frames are obtained by thresholding the coefficients of similar blocks. In the second stage, refined residual frames are reconstructed from the original measurements and the measurements corresponding to the frames estimated in the first stage. These two stages are iterated until convergence. The proposed algorithm exhibits superior subjective image quality and significantly improves the peak-signal-to-noise ratio and the structural similarity index measure compared to other state-of-the-art CS algorithms.

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Citation

Yookyung Kim ; Han Oh and Ali Bilgin
"Video compressed sensing using iterative self-similarity modeling and residual reconstruction", J. Electron. Imaging. 22(2), 021005 (Feb 04, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.2.021005


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