Total Variation Based Perceptual Image Quality Assessment Modeling

Yadong Wu,Ran Duan,Hongying Zhang

doi:10.1155/2014/294870

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.

Highlights

Visual quality evaluation has numerous uses in practice and plays a central role in shaping many visual processing algorithms and systems, as well as their implementation, optimization, and testing
In this paper, based on the assumption that human visual system is sensitive to image structures and image local luminance, we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain
We validate the performance of the proposed TVPIQA measure and compare it with other seven IQA measures, that is, peak signal-to-noise ratio (PSNR), SSIM [1], information content weighted PSNR (IW-PSNR) [4], information content weighted SSIM (IW-SSIM)

Summary

Introduction

Visual quality evaluation has numerous uses in practice and plays a central role in shaping many visual processing algorithms and systems, as well as their implementation, optimization, and testing. The mean opinion score (MOS), subjective quality measurement, has been used for many years It is very expensive and time consuming, which makes it impractical for image processing applications. In the proposed PIQA metric, two human visual sensitivity factors, image structures and luminance changes in enclosed regions, are considered. From HVS perspective, another important factor to be considered is luminance change of smooth and enclosed regions, as HVS is very sensitive to luminance change Based on these ideas, we propose a TVPIQA metric in spatial domain, in which we use TV to describe the image structure and the energy of enclosed regions in the difference image to measure luminance changes. The energy of enclosed regions in a difference image is used to measure the missing luminance information that is sensitive to human visual system.

Related Work

PIQA Metric Based on TV Model

Analysis of the Proposed TVPIQA Measure

Experimental Results

Conclusions