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.

本研究通过对 RGB、HSV、HSL、XYZ、CIELAB 和 CIELUV 等多种颜色模型进行比较分析，评估它们在准确反映人类感知颜色方面的有效性，从而评价它们在准确反映视觉颜色差异和与人眼兼容的主导调色板提取方面的能力，以便应用于图像处理，数字媒体和设计等相关领域。

人类感知和视觉颜色差异的色彩模型比较分析

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

Human Skin detection deals with the recognition of skin-colored pixels and
regions in a given image. Skin color is often used in human skin detection
because it is invariant to orientation and size and is fast to process. A new
human skin detection algorithm is proposed in this paper. The three main
parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue,
Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The
objective of proposed algorithm is to improve the recognition of skin pixels in
given images. The algorithm not only considers individual ranges of the three
color parameters but also takes into ac- count combinational ranges which
provide greater accuracy in recognizing the skin area in a given image.

本文提出了一种新的人类皮肤检测算法，该算法基于 RGB、HSV 和 YCbCr 颜色模型，通过考虑三个颜色参数的各自和联合范围来提高对给定图像中皮肤像素的识别精度。