A statistical reduced-reference method for color image quality assessment

TL;DR

This paper introduces a new reduced-reference image quality assessment method that leverages color information through statistical modeling of steerable pyramid coefficients, showing high consistency with human perception.

Contribution

It proposes a novel NSS-based RR IQA framework using MGGD modeling and KLD measure, specifically optimized for color image quality assessment.

Findings

Best performance with CIELAB color space and KLD measure

Effective in aligning with human visual perception

Validated on TID 2008 benchmark and FRTV Phase I

Abstract

Although color is a fundamental feature of human visual perception, it has been largely unexplored in the reduced-reference (RR) image quality assessment (IQA) schemes. In this paper, we propose a natural scene statistic (NSS) method, which efficiently uses this information. It is based on the statistical deviation between the steerable pyramid coefficients of the reference color image and the degraded one. We propose and analyze the multivariate generalized Gaussian distribution (MGGD) to model the underlying statistics. In order to quantify the degradation, we develop and evaluate two measures based respectively on the Geodesic distance between two MGGDs and on the closed-form of the Kullback Leibler divergence. We performed an extensive evaluation of both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID 2008 benchmark and the FRTV Phase I validation process. Experimental results demonstrate the effectiveness of the proposed framework to achieve a good consistency with human visual perception. Furthermore, the best configuration is obtained with CIELAB color space associated to KLD deviation measure.