Linking luminance and lightness by global contrast normalization.

Area of Science:

• Visual perception
• Computational neuroscience
• Image processing

Background:

• The visual system interprets surface lightness from retinal image luminances.
• Understanding this process is crucial for explaining visual perception.

Purpose of the Study:

• To investigate how the visual system determines surface lightness.
• To evaluate different computational models for predicting perceived lightness.

Main Methods:

• Perceived lightness of target surfaces in custom checkerboards was measured.
• Luminances were recorded under various viewing conditions (plain, shadow, transparent media).
• Lightness matches were assessed for four observers across different reflectances and conditions.

Main Results:

• A normalized contrast measure, combining local Michelson contrast with region-based normalization, best predicted perceived surface lightness.
• This measure was nearly as accurate as actual reflectance values.
• Existing models like local luminance, Michelson contrast, edge integration, and anchoring theory showed lower predictive power.

Conclusions:

• Normalized contrast is a highly effective predictor of perceived surface lightness.
• The visual system likely employs a sophisticated normalization process for lightness perception.
• Further research is needed to understand image region segregation mechanisms.