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

TL;DR

This study tests how a physical spectral filter alters color-matching functions (CMFs) and presents a method to recover the filter's transmittance and a 3×3 transform that maps unfiltered CMFs to filtered CMFs. By applying the method to SB1955 and ICVIO CMFs, the authors identify a yellow-leaning filter consistent with age-related lens yellowing as a principal source of inter-observer differences, reducing CMF variability to a single spectrum plus a linear transform. Bootstrapped uncertainty quantification demonstrates robustness in central wavelengths, while edge regions exhibit greater error due to illumination and matching challenges. The work offers a compact framework for modeling observer variability and suggests potential reductions in experimental overhead, with future work needed to generalize to more observers and incorporate additional physiological factors.

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 two observers, 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.

Figures (10)

• Figure 1: Filter transmittance
• Figure 2: Color-matching coefficients from color-matching experiment done (for Observer-I) : (a) without filter and (b) with filter
• Figure 3: Observer-I : Mean CMFs with error bars representing 95% CI of the mean.
• Figure 4: Observer-C : Mean CMFs with error bars representing 95% CI of the mean.
• Figure 5: The first 20 of N filter estimates (from Observer-I's data)