CSV: Image Quality Assessment Based on Color, Structure, and Visual System

TL;DR

This paper introduces CSV, a full-reference image quality estimator that assesses color, structure, and visual perception, outperforming many existing methods across multiple image quality databases.

Contribution

The paper proposes a novel image quality assessment method that emphasizes perceptual color degradations and integrates structural and visual system features.

Findings

CSV ranks among the top two in quality estimation performance.

It effectively quantifies perceptual color degradations.

It performs well across multiple benchmark databases.

Abstract

This paper presents a full-reference image quality estimator based on color, structure, and visual system characteristics denoted as CSV. In contrast to the majority of existing methods, we quantify perceptual color degradations rather than absolute pixel-wise changes. We use the CIEDE2000 color difference formulation to quantify low-level color degradations and the Earth Mover's Distance between color name descriptors to measure significant color degradations. In addition to the perceptual color difference, CSV also contains structural and perceptual differences. Structural feature maps are obtained by mean subtraction and divisive normalization, and perceptual feature maps are obtained from contrast sensitivity formulations of retinal ganglion cells. The proposed quality estimator CSV is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases, and it is always among the top two performing quality estimators in terms of at least ranking, monotonic behavior or linearity.