对比多种色彩模型，发现HS*最符合人眼感知。
Color Models in Image Processing: A Review and Experimental Comparison
- 梳理主流色彩模型理论与计算特性
- 实验表明HS*家族最贴近人类视觉感知
- 适合图像处理与感知计算领域研究者参考
色彩表示在计算机视觉与人机交互中至关重要。本文综述了多种色彩模型与空间，分析其理论基础、计算特性及实际应用。涵盖传统模型（如RGB、CMYK、YUV）、感知均匀空间（如CIELAB、CIELUV）以及基于模糊的方案。通过一系列实验，从设备依赖性、色度一致性、计算复杂度等角度评估不同模型。结果揭示现有模型存在不足，且HS*家族在人眼感知匹配度上表现最优。论文还指出现有模型的优缺点，并提出开放挑战与未来方向，为图像处理、感知计算、数字媒体等领域研究提供参考。
原文摘要 · Abstract (English)
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.
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