Reduced-Reference Image Quality Assessment Using A Wavelet-Domain
Natural Image Statistic Model
Presented at:
IS&T/SPIE 17th Annual Symposium on Electronic Imaging
San Jose, CA. 17-20 Jan 2005.
doi: 10.1117/12.597306
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/
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