Modeling spectral filtering effects on color-matching functions: Implications for observer variability
This work addresses observer variability modeling in color vision research, offering a potentially more efficient method for characterizing individual differences, though it appears incremental as it builds on existing datasets and hypotheses.
This study tackled the problem of modeling observer variability in color-matching functions by investigating spectral filtering effects, finding that a 'yellow' filter can transform between different datasets, aligning with age-related lens yellowing hypotheses.
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.