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Journal of Applied Mathematics
Volume 2014, Article ID 294870, 10 pages
http://dx.doi.org/10.1155/2014/294870
Research Article

Total Variation Based Perceptual Image Quality Assessment Modeling

1School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China
2Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang 621010, China
3Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, School of Information and Engineering, Southwest University of Science and Technology, Mianyang 621010, China

Received 12 February 2014; Accepted 10 March 2014; Published 1 April 2014

Academic Editor: X. Song

Copyright © 2014 Yadong Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Visual quality measure is one of the fundamental and important issues to numerous applications of image and video processing. In this paper, based on the assumption that human visual system is sensitive to image structures (edges) and image local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain. The proposed measure compares TVs between a distorted image and its reference image to represent the loss of image structural information. Because of the good performance of TV model in describing edges, the proposed TVPIQA measure can illustrate image structure information very well. In addition, the energy of enclosed regions in a difference image between the reference image and its distorted image is used to measure the missing luminance information which is sensitive to human visual system. Finally, we validate the performance of TVPIQA measure with Cornell-A57, IVC, TID2008, and CSIQ databases and show that TVPIQA measure outperforms recent state-of-the-art image quality assessment measures.