Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

Abstract

AbstractHigh dynamic range (HDR) and wide color gamut imagery has an established video ecosystem, spanning image capture to encoding and display. This drives the need for evaluating how image quality is affected by the multitudes of ecosystem parameters. The simplest quality metrics evaluate color differences on a pixel‐by‐pixel basis. In this article, we evaluate a series of these color difference metrics on four HDR and three standard dynamic range publicly available distortion databases consisting of natural images and subjective scores. We compare the performance of the well‐established CIE L*a*b* metrics (ΔE00, ΔE94) alongside two HDR‐specific metrics (ΔEZ [Jzazbz], ΔEITP [ICTCP]) and a spatial CIE L*a*b* extension (). We also present a novel spatial extension to ΔEITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.