Color Models in Image Processing: A Review and Experimental Comparison

2025-10-01

AI Summary

The selection of color models often lacks perceptual grounding, leading to suboptimal choices for human-centered applications. Method: This paper systematically reviews and empirically evaluates major color spaces—including RGB, CMYK, YUV, CIELAB, CIELUV, and the HS* family—through theoretical analysis, color conversion experiments, perceptual uniformity assessment, and computational efficiency comparison. Evaluation is conducted along three dimensions: device dependence, chromatic consistency, and computational complexity. Results: HS*-based models significantly outperform traditional models in visual perceptual consistency; CIELAB offers superior perceptual uniformity but incurs high computational cost; RGB and CMYK exhibit strong device dependence; YUV prioritizes compression efficiency. This work establishes the first multi-metric empirical framework for color model selection, identifies HS* as the preferred choice for human–computer interaction and vision-perception tasks, and charts a direction toward lightweight perceptually grounded color modeling.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Reviewing color models' theoretical foundations and practical applications
• Evaluating color models through experiments on device dependency
• Identifying gaps in existing color models and future research directions

Innovation

• Reviewing traditional and perceptual color models
• Conducting experiments on device dependency and complexity
• Identifying HS* family as most human perception aligned