# Color Models in Image Processing: A Review and Experimental Comparison

arXiv: https://arxiv.org/abs/2510.00584 · HTML: https://mlsanity.com/paper/2510.00584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 127 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Edge and Identity Preserving Network for Face Super-Resolution](https://mlsanity.com/paper/2008.11977/) (2020)
- [Color-Emotion Associations in Art: Fuzzy Approach](https://mlsanity.com/paper/2311.18518/) (2023)
- [COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation](https://mlsanity.com/paper/2507.11488/) (2025)
- [Comparative Analysis Of Color Models For Human Perception And Visual Color Difference](https://mlsanity.com/paper/2406.19520/) (2024)
- [Fuzzy color model and clustering algorithm for color clustering problem](https://mlsanity.com/paper/2407.06782/) (2024)

## Built on by (most cited first, 4 of 4)

- [Toward a Universal Color Naming System: A Clustering-Based Approach using Multisource Data](https://mlsanity.com/paper/2604.03235/) (2026)
- [Not All Color Categories Are Equally Stable: A Multilingual Free Color Naming Experiment](https://mlsanity.com/paper/2607.10465/) (2026)
- [Beyond Color Geometry: Evaluating Human-Like Color Representations in Vision Models](https://mlsanity.com/paper/2607.13647/) (2026)
- [Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments](https://mlsanity.com/paper/2609.39130/) (2026)

## Abstract (arXiv)

Color representation is essential in computer vision and human-computer interaction. There are multiple color models available. The choice of a suitable color model is critical for various applications. This paper presents a review of color models and spaces, analyzing their theoretical foundations, computational properties, and practical applications. We explore traditional models such as RGB, CMYK, and YUV, perceptually uniform spaces like CIELAB and CIELUV, and fuzzy-based approaches as well. Additionally, we conduct a series of experiments to evaluate color models from various perspectives, like device dependency, chromatic consistency, and computational complexity. Our experimental results reveal gaps in existing color models and show that the HS* family is the most aligned with human perception. The review also identifies key strengths and limitations of different models and outlines open challenges and future directions This study provides a reference for researchers in image processing, perceptual computing, digital media, and any other color-related field.