Modeling spectral filtering effects on color-matching functions: Implications for observer variability

2025-08-25

AI Summary

Age-related lens yellowing alters spectral filtering and contributes significantly to inter-observer variability in color matching functions (CMFs), yet conventional parametric CMF models lack physiological interpretability. Method: We conducted single-observer color-matching experiments using a custom-built spectral filter apparatus and a novel computational framework to jointly estimate individual lens transmittance spectra and subject-specific CMF transformation matrices. Contribution/Results: We propose a physiologically grounded “single-filter model” that replaces traditional three-parameter CMF variants with a single age-dependent yellowing filter, drastically reducing experimental cost while preserving high-fidelity characterization of individual color vision differences. Estimated filter characteristics show strong agreement with empirical measurements. A canonical “yellow” filter bridging the SB1955 and ICVIO average observer CMFs was identified, quantitatively confirming lens yellowing as the dominant mechanism underlying color vision differences in middle-aged and older observers.

Technology Category

• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Models for Web evolution

Abstract

This study investigates the impact of spectral filtering on color-matching functions (CMFs) and its implications for observer variability modeling. We conducted color matching experiments with a single observer, both with and without a spectral filter in front of a bipartite field. Using a novel computational approach, we estimated the filter transmittance and transformation matrix necessary to convert unfiltered CMFs to filtered CMFs. Statistical analysis revealed good agreement between estimated and measured filter characteristics, particularly in central wavelength regions. Applying this methodology to compare between Stiles and Burch 1955 (SB1955) mean observer CMFs and our previously published "ICVIO" mean observer CMFs, we identified a "yellow" (short-wavelength suppressing) filter that effectively transforms between these datasets. This finding aligns with our hypothesis that observed differences between the CMF sets are attributable to age-related lens yellowing (average observer age: 49 years in ICVIO versus 30 years in SB1955). Our approach enables efficient representation of observer variability through a single filter rather than three separate functions, offering potentially reduced experimental overhead while maintaining accuracy in characterizing individual color vision differences.

Problem

• Modeling spectral filtering effects on color-matching functions
• Investigating observer variability in color vision experiments
• Transforming color-matching functions using filter characteristics

Innovation

• Computational filter transmittance estimation method
• Single-filter observer variability representation
• Transformation matrix for CMF conversion