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Reduced-reference image quality assessment using a wavelet-domain natural image statistic model
Proc. SPIE 5666, 149 (2005); doi:10.1117/12.597306
Monday 17 January 2005
San Jose, CA, USA
Human Vision and Electronic Imaging X
Bernice E. Rogowitz, Thrasyvoulos N. Pappas, Scott J. Daly
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Add to FacebookReduced-reference (RR) image quality measures aim to predict the visual quality of distorted images with only partial information about the reference images. In this paper, we propose an RR image quality assessment method based on a natural image statistic model in the wavelet transform domain. We use the Kullback-Leibler distance between the marginal probability distributions of wavelet coefficients of the reference and distorted images as a measure of image distortion. A generalized Gaussian model is employed to summarize the marginal distribution of wavelet coefficients of the reference image, so that only a relatively small number of RR features are needed for the evaluation of image quality. The proposed method is easy to implement and computationally efficient. In addition, we find that many well-known types of image distortions lead to significant changes in wavelet coefficient histograms, and thus are readily detectable by our measure. A Matlab implementation of the method has been made available online at http://www.cns.nyu.edu/~lcv/rriqa/.
© 2005 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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Zhou Wang and Eero P. Simoncelli, "Reduced-reference image quality assessment using a wavelet-domain natural image statistic model",
Proc. SPIE 5666, 149 (2005); doi:10.1117/12.597306
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