Wide Color Gamut Image Content Characterization: Method, Evaluation, and Applications

TL;DR

This paper introduces a new framework for characterizing wide color gamut images based on perceived quality changes, with practical applications in content selection and evaluation, enhancing reliability in quality of experience assessments.

Contribution

The paper presents a novel characterization framework with quantitative criteria for wide color gamut images, improving analysis and content selection processes.

Findings

Proposed four quantitative criteria: coverage, total coverage, uniformity, total uniformity.

Applied framework to improve gamut mapping evaluation reliability.

Enabled better understanding of existing wide color gamut datasets.

Abstract

In this paper, we propose a novel framework to characterize a wide color gamut image content based on perceived quality due to the processes that change color gamut, and demonstrate two practical use cases where the framework can be applied. We first introduce the main framework and implementation details. Then, we provide analysis for understanding of existing wide color gamut datasets with quantitative characterization criteria on their characteristics, where four criteria, i.e., coverage, total coverage, uniformity, and total uniformity, are proposed. Finally, the framework is applied to content selection in a gamut mapping evaluation scenario in order to enhance reliability and robustness of the evaluation results. As a result, the framework fulfils content characterization for studies where quality of experience of wide color gamut stimuli is involved.