研究目的
To model and quantify the effects of inter-observer variation in colour sensitivity on colour rendition measures, specifically using IES TM-30-15 metrics, and to compare this variation with other sources of ambiguity in lighting.
研究成果
Inter-observer variation significantly affects colour rendition measures, with differences up to 5-10 units in TM-30 metrics. This variation is larger than that due to field size differences but smaller than variations from different object colours. Age-related differences are substantial, and light source spectral characteristics influence susceptibility to variation. The findings highlight the need to communicate this variability in lighting specifications and suggest opportunities for designing less variable light sources.
研究不足
The study relies on simulated observer populations, which may not fully capture real-world variability. The light sources selected are not comprehensive, limiting generalizability. The computational approach assumes ideal conditions and does not account for all physiological factors or real-world lighting scenarios. Future work is needed to develop direct computation methods for inter-observer variation indices.
1：Experimental Design and Method Selection:
The study uses Monte Carlo simulations based on individual colorimetric observer models to simulate populations of observers with realistic variations in colour sensitivity. The methodology involves re-implementing IES TM-30-15 computations in MATLAB to allow for variable colour matching functions.
2：Sample Selection and Data Sources:
Two simulated populations of observers are used: a US Age population with 1000 observers sampled from a US census age distribution, and an Age Group population with 500 observers aged 25 and 500 aged 65. Light sources include nine contemporary types (e.g., incandescent, RGB LEDs) selected from the IES TM-30 spreadsheet for their spectral diversity.
3：Light sources include nine contemporary types (e.g., incandescent, RGB LEDs) selected from the IES TM-30 spreadsheet for their spectral diversity.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: Computational tools include MATLAB for simulations, the IES TM-30-15 Excel spreadsheet for verification, and spectral data of light sources. No physical equipment is mentioned; the study is computational.
4：Experimental Procedures and Operational Workflow:
Steps involve: (a) Simulating observer sensitivities using Asano et al.'s model, (b) Transforming LMS sensitivities to CMFs, (c) Computing colour rendition measures (Rf, Rg) for each observer and light source using modified TM-30 procedures, (d) Analyzing variations through plots and statistical measures like standard deviations.
5：Data Analysis Methods:
Data is analyzed using statistical measures (e.g., mean, standard deviation), graphical representations (e.g., Rg vs. Rf plots, colour vector graphics), and ellipse fitting to quantify variability. Comparisons are made with other sources of variation like field size differences.
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