Simple color appearance model (sCAM) based on simple uniform color space (sUCS).

Area of Science:

• Color Science
• Computer Vision
• Image Processing

Background:

• Existing color appearance models and uniform color spaces have limitations in accuracy and scope.
• The need for a robust and versatile color model for diverse applications is critical.

Purpose of the Study:

• To develop and validate a new color appearance model, sCAM, and its associated uniform color space, sUCS.
• To evaluate the performance of sUCS against state-of-the-art uniform color spaces in terms of uniformity and hue linearity.
• To assess the accuracy of sCAM in fitting various color appearance datasets.

Main Methods:

• Developed the sCAM color appearance model and the sUCS uniform color space.
• Input data processed from XYZ D65 and RGB color spaces.
• Extensive accuracy testing using 28 datasets for space uniformity and 6 datasets for hue linearity.

Main Results:

• sUCS demonstrated top-tier performance, ranking best or second-best in space uniformity and hue linearity across multiple datasets.
• sCAM achieved the best performance in fitting all evaluated one- and two-dimensional color appearance datasets.
• The model's accuracy was extensively validated.

Conclusions:

• The developed sCAM model and sUCS uniform color space provide a simple yet comprehensive solution for color-related applications.
• sUCS offers significant improvements in uniformity and hue linearity compared to existing models.
• Field testing is recommended for all color-related applications to confirm the model's practical utility.