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Journal of Electronic Imaging

Video compressed sensing using iterative self-similarity modeling and residual reconstruction
Author(s): Yookyung Kim; Han Oh; Ali Bilgin
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Paper 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.

Paper Details

Date Published: 4 February 2013
PDF: 14 pages
J. Electron. Imag. 22(2) 021005 doi: 10.1117/1.JEI.22.2.021005
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Yookyung Kim, The Univ. of Arizona (United States)
Han Oh, Samsung Digital City (Korea, Republic of)
Ali Bilgin, The Univ. of Arizona (United States)

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