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Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences
Author(s) -
Choudhury Anustup,
Wanat Robert,
Pytlarz Jaclyn,
Daly Scott
Publication year - 2021
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.22588
Subject(s) - gamut , computer science , artificial intelligence , color space , high dynamic range , metric (unit) , color difference , pixel , computer vision , tone mapping , image quality , range (aeronautics) , color depth , color image , distortion (music) , dynamic range , pattern recognition (psychology) , image processing , image (mathematics) , enhanced data rates for gsm evolution , amplifier , operations management , materials science , computer network , bandwidth (computing) , composite material , economics
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‐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 ( Δ E 00 , Δ E 94 ) alongside two HDR‐specific metrics ( Δ E Z [J z a z b z ], Δ E ITP [IC T C P ]) and a spatial CIE L * a * b * extension ( Δ E 00 S ). We also present a novel spatial extension to Δ E ITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, Δ E ITP SC , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.