Wide Color Gamut Image Content Characterization: Method, Evaluation, and Applications
This work addresses the need for reliable content characterization in studies involving quality of experience for wide color gamut stimuli, though it appears incremental as it builds on existing datasets and evaluation scenarios.
The paper tackles the problem of characterizing wide color gamut image content based on perceived quality, proposing a framework with four quantitative criteria (coverage, total coverage, uniformity, total uniformity) and applying it to analyze datasets and enhance gamut mapping evaluations.
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.