Human Vision and Electronic Imaging X, Proc. SPIE, vol. 5666, Jan. 2005
Reduced-Reference Image
Quality Assessment Using a Wavelet-Domain Natural Image Statistic Model
Zhou
Wang and Eero P. Simoncelli
Howard Hughes Medical Institute, Center for Neural Science
and Courant Institute of Mathematical Sciences, New York University, New York,
NY 10003
Abstract: Reduced-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/.