Reduced-reference image quality assessment using a wavelet-domain natural image statistic model

Research output: Contribution to journalConference article

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/.

Original languageEnglish (US)
Article number20
Pages (from-to)149-159
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5666
DOIs
StatePublished - 2005
EventProceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging X - San Jose, CA, United States
Duration: Jan 17 2005Jan 20 2005

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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