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

Abstract

High 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-bypixel 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 (ΔE 00 , ΔE 94) alongside two HDR-specific metrics (ΔE Z [J z a z b z ], ΔE ITP [IC T C P ]) and a spatial CIE L*a*b* extension (ΔE S 00). We also present a novel spatial extension to ΔE ITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, ΔE SC ITP , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.

Perceptual video quality assessment: the journey continues!

“…This approach aims to make traditional SDR quality metrics more applicable to HDR videos by considering the expanded luminance range of HDR. In addition, some researchers have focused on the chromatic aspects of HDR video quality, such as color fidelity (Abebe et al, 2015), HDR Uniform Color Spaces (Rousselot et al, 2019), and color difference models (Choudhury et al, 2021). HDR-VQM (Narwaria et al, 2015b) utilizes spatiotemporal analysis to simulate human perception.…”