Weighted Geometric Mean (WGM) method: A new chromatic adaptation model.

Area of Science:

• Vision Science
• Computational Neuroscience
• Color Physics

Background:

• Human color perception relies on chromatic adaptation to maintain stable color constancy under varying illumination.
• Chromatic adaptation encompasses sensory (automatic visual system adjustments) and cognitive (knowledge-based) processes.
• The geometric mean is hypothesized as a key mathematical principle in peripheral sensory adaptation.

Purpose of the Study:

• To propose and validate the Weighted Geometric Mean (WGM) model for chromatic adaptation.
• To assess the WGM model's ability to predict incomplete adaptation along the Planckian or Daylight locus.
• To compare the WGM model's performance against existing models like CAT16 and vK20.

Main Methods:

• Development of the Weighted Geometric Mean (WGM) chromatic adaptation model.
• Testing the WGM model using various corresponding color datasets.
• Comparative analysis against the CAT16 and vK20 chromatic adaptation models.

Main Results:

• The WGM model demonstrated significant improvements over CAT16 and vK20.
• The WGM model accurately predicts the degree of both sensory and cognitive adaptation.
• The model's predictions are physiologically plausible, aligning with human visual system capabilities.

Conclusions:

• The WGM model offers a more accurate and physiologically plausible approach to chromatic adaptation.
• This model advances our understanding of how the human visual system achieves stable color perception.
• The WGM model provides a valuable tool for research in color vision and visual perception.