4 February 2013 Video compressed sensing using iterative self-similarity modeling and residual reconstruction
Author Affiliations +
J. of Electronic Imaging, 22(2), 021005 (2013). doi:10.1117/1.JEI.22.2.021005
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.
© 2013 SPIE and IS&T
Yookyung Kim, Han Oh, Ali Bilgin, "Video compressed sensing using iterative self-similarity modeling and residual reconstruction," Journal of Electronic Imaging 22(2), 021005 (4 February 2013). https://doi.org/10.1117/1.JEI.22.2.021005


Reconstruction algorithms

Video compression

Virtual colonoscopy

Compressed sensing

Signal to noise ratio

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