Comparative Analysis Of Color Models For Human Perception And Visual Color Difference

TL;DR

The work addresses aligning perceptual color differences with human vision across multiple color models, comparing spaces such as RGB, HSV, HSL, XYZ, CIELAB, and CIELUV to assess their reflection of visual differences and palette extraction compatibility. It surveys established metrics including $\Delta E^*_{ab}$, $\Delta E_{94}$, $\Delta E_{2000}$, and the NS variant $\Delta E_{NS}$, recognizing challenges posed by nonuniform perceptual spaces. The paper discusses the shift from Riemannian to non-Riemannian perceptual models and the limitations of existing formulas in predicting subtle color differences. The proposed methodology combines distance evaluation across color spaces with human validation via a Two-Alternative Forced Choice (2AFC) survey and CIE benchmarks to identify perceptually faithful metrics, with practical implications for digital design, image processing, and quality control. Overall, the work aims to guide the selection of color models and difference formulas that align best with human perception for more robust color processing.

Abstract

Color is integral to human experience, influencing emotions, decisions, and perceptions. This paper presents a comparative analysis of various color models' alignment with human visual perception. The study evaluates color models such as RGB, HSV, HSL, XYZ, CIELAB, and CIELUV to assess their effectiveness in accurately representing how humans perceive color. We evaluate each model based on its ability to accurately reflect visual color differences and dominant palette extraction compatible with the human eye. In image processing, accurate assessment of color difference is essential for applications ranging from digital design to quality control. Current color difference metrics do not always match how people see colors, causing issues in accurately judging subtle differences. Understanding how different color models align with human visual perception is crucial for various applications in image processing, digital media, and design.