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

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.Index Terms-Wide color gamut, color gamut mapping, content characterization, content selection, quality of experience.

Perceived Color Gamut in Images: From Boundary to Difference

“…As our selected stimuli covered a group of representative image contents from the whole gamut, it would be promising to generalize to predict any image, with a colorimetrically explicit forward model, and hopefully, the average results across all images would better reflect the usefulness of a given display gamut design. A similar framework has also been recently proposed (Lee et al, 2020 ). While the simplistic and colorimetric comparison between two images has been shown to be predictive, future work can explore image color appearance modeling (Johnson et al, 2010 ) and spatial gamut mapping (Bonnier et al, 2006 ; Vazquez-Corral and Bertalmío, 2018 ), especially when there are more variations in display parameters and image contents.…”