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

TL;DR

This paper compares various color models to determine which best aligns with human visual perception, focusing on their effectiveness in representing color differences and palette extraction for improved image processing applications.

Contribution

It provides a comprehensive evaluation of RGB, HSV, HSL, XYZ, CIELAB, and CIELUV models against human perception, highlighting their strengths and limitations.

Findings

CIELAB and CIELUV align closely with human perception of color differences.

RGB and HSV models are less effective in representing subtle visual color differences.

The study offers insights for selecting appropriate color models in digital media applications.

Abstract

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.