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

arXiv: https://arxiv.org/abs/2406.19520 · HTML: https://mlsanity.com/paper/2406.19520/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Color-Emotion Associations in Art: Fuzzy Approach](https://mlsanity.com/paper/2311.18518/) (2023)
- [A color-difference formula for evaluating color pairs with no separation -- Delta ENS](https://mlsanity.com/paper/1904.11293/) (2019)

## Built on by (most cited first, 5 of 5)

- [COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation](https://mlsanity.com/paper/2507.11488/) (2025)
- [Color Models in Image Processing: A Review and Experimental Comparison](https://mlsanity.com/paper/2510.00584/) (2025)
- [Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data](https://mlsanity.com/paper/2604.03235/) (2026)
- [Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization](https://mlsanity.com/paper/2605.09339/) (2026)
- [Not All Color Categories Are Equally Stable: A Multilingual Free Color Naming Experiment](https://mlsanity.com/paper/2607.10465/) (2026)

## Abstract (arXiv)

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.