1. CATEGORICAL COLOR RENDEING OF LED LIGHT SOURCES H. Yaguchi, N. Endoh, T. Moriyama and S. Shioiri CIE Expert Symposium on LED Light Sources June 7, 2004, Tokyo

2. INTRRODUCTION Energy efficiency and color rendering –CIE CRI is based on color difference Importance of color name –To communicate color information in every day life. –To categorize objects and recognize them. –Color name does not depend on viewing condition. Color rendering based on color categorization

3. PURPOSE To evaluate color rendering of LED light sources to take account of color categorization. The Subjective method by categorical color naming experiment. The Objective methods using the CIE color rendering index and the categorical color rendering index.

4. EXPERIMENT

5. Color samples 292 Munsell color chips V: 2, 4, 6, 8 C: 0, 2, 4, 6, H: 5R, 10R, 5YR, 10YR, 5R,

6. Spectral power distributions of light sources

7. Categorical Color Naming Sort samples into 11 basic color categories: –red (aka), gree (midori), yellow (ki), blue (ao), orange (daidai), pink (momo), purple (murasaki), brown (cha), white (shiro), gray (hai), and black (kuro). Sorting was repeated three times. Select samples sorted into the same category for all three trials.

8. Subjects 9 subjects Age from 21 to 55 Normal color vision checked by the ND- 100 hue test.

9. Color samples in the Munsell hue circle, named consistently

10. R P Y B G R P Y B G R P Y B G R P Y B G R P Y B G Result Observer Kn, v=6 LED(7500K) (5100K) (4000K) (2800K) D65 Munsell color circles Same color samples are named different color

11. Percent Correct N D65 : number of samples sorted into a color category consistently for three trials under the D65 simulator. N s : number of samples, under test light source, sorted into the same color category as in the case of the D65 simulator. N d : number of samples, under test light source, sorted into the different color category from the case of the D65 simulator. Percent correct = 100(N s - N d )/ N D65

12. Percent Correct for 8 color names

13. CIE color appearance model (CIECAM97s)

14. Color map in CIECAM97s 75<J55<J<75 35<J<55J<35

15. How to calculate ● CCRI for each test sample ● Averaged CCRI Categorical color rendering index (CCRI) × × × × Boundary of color samples under the test light source “St” Boundary of color samples under D65 × × × × Cmax HminHma x Cmin Chroma Hue angle Boundary of Color name “Si”

16. CCRI of 5 light sources

17. R i, CCRI i and percent correct D65LED75LED510LED40LED28 red R9R9 835214-5 CCRI red 9788909183 PC red 10073837363 yellow R 10 9094749491 CCRI yellow 90100 8278 PC yellow 10083857880 green R 11 9568636452 CCRI green 9585918885 PC green 10082918178 blue R 12 9256536172 CCRI blue 97100 98 PC blue 10093898681

18. Correlation among R i, CCRI i and PC

22. R a, CCRI a and percent correct Light sources D65LED75LED51LED40LED28 T cp (K)64207500510040002800 RaRa 9684798075 CCRI a 95 9187 PC a 10080827774

23. Correlation among R a, CCRI a and PC

24. SUMMARIES Color rendering qualities of white LED (blue LED and yellow phosphor type) light sources of four different T cp were examined with PC, R i, and CCRI i. Correlation between CCRI and PC for different color temperature light sources is higher than those between CIE CRI and PC.

25. CONCLUSION If you like to take account of color categorization in order to evaluate practical color rendering quality of white LED light sources, the CCRI can be recommended rather than R a.