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Proceedings Paper

Reduced-reference image quality assessment using a wavelet-domain natural image statistic model
Author(s): Zhou Wang D.V.M.; Eero P. Simoncelli
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Paper 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 quality assessment method based on a natural image statistic model in the wavelet transform domain. In particular, we observe that the marginal distribution of wavelet coefficients changes in different ways for different types of image distortions. To quantify such changes, we estimate the Kullback-Leibler distance between the marginal distributions of wavelet coefficients of the reference and distorted images. 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 distortion lead to significant changes in wavelet coefficient histograms, and thus are readily detectable by our measure. The algorithm is tested with subjective ratings of a large image database that contains images corrupted with a wide variety of distortion types.

Paper Details

Date Published: 18 March 2005
PDF: 11 pages
Proc. SPIE 5666, Human Vision and Electronic Imaging X, (18 March 2005); doi: 10.1117/12.597306
Show Author Affiliations
Zhou Wang D.V.M., New York Univ. (United States)
Eero P. Simoncelli, New York Univ. (United States)

Published in SPIE Proceedings Vol. 5666:
Human Vision and Electronic Imaging X
Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Scott J. Daly, Editor(s)

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