17 February 2012 Viewer preferences for classes of noise removal algorithms for high definition content
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Perceived video quality studies were performed on a number of key classes of noise removal algorithms to determine viewer preference. The noise removal algorithm classes represent increase in complexity from linear filter to nonlinear filter to adaptive filter to spatio-temporal filter. The subjective results quantify the perceived quality improvements that can be obtained with increasing complexity. The specific algorithm classes tested include: linear spatial one channel filter, nonlinear spatial two-channel filter, adaptive nonlinear spatial filter, multi-frame spatio-temporal adaptive filter. All algorithms were applied on full HD (1080P) content. Our subjective results show that spatio-temporal (multi-frame) noise removal algorithm performs best amongst the various algorithm classes. The spatio-temporal algorithm improvement compared to original video sequences is statistically significant. On the average, noise-removed video sequences are preferred over original (noisy) video sequences. The Adaptive bilateral and non-adaptive bilateral two channel noise removal algorithms perform similarly on the average thus suggesting that a non-adaptive parameter tuned algorithm may be adequate.
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Sachin Deshpande, Sachin Deshpande, } "Viewer preferences for classes of noise removal algorithms for high definition content", Proc. SPIE 8291, Human Vision and Electronic Imaging XVII, 82910H (17 February 2012); doi: 10.1117/12.906498; https://doi.org/10.1117/12.906498

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