Self-similarity measure for assessment of image visual quality

Authors:
Nikolay Ponomarenko;Lina Jin;Vladimir Lukin;Karen Egiazarian
Affiliations:
National Aerospace University, Dept. of Transmitters, Receivers and Signal Processing, Kharkov, Ukraine;Tampere University of Technology, Institute of Signal Processing, Tampere, Finland;National Aerospace University, Dept. of Transmitters, Receivers and Signal Processing, Kharkov, Ukraine;Tampere University of Technology, Institute of Signal Processing, Tampere, Finland
Venue:
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Year:
2011

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Abstract

An opportunity of using self-similarity in evaluation of image visual quality is considered. A method for estimating self-similarity for a given image fragment that takes into account contrast sensitivity function is proposed. Analytical expressions for describing the proposed parameter distribution are derived, and their importance to human vision system based image visual quality full-reference evaluation is proven. A corresponding metric is calculated and a mean squared difference for the considered parameter maps in distorted and reference images is considered. Correlation between this metric and mean opinion score (MOS) for five largest openly available specialized image databases is calculated. It is demonstrated that the proposed metric provides a correlation at the level of the best known metrics of visual quality. This, in turn, shows an importance of fragment self-similarity in image perception.