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CATEGORICAL COLOR RENDEING OF LED LIGHT SOURCES

CATEGORICAL COLOR RENDEING OF LED LIGHT SOURCES

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CATEGORICAL COLOR RENDEING OF LED LIGHT SOURCES

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

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

  11. Percent Correct • ND65: number of samples sorted into a color category consistently for three trials under the D65 simulator. • Ns: number of samples, under test light source, sorted into the same color category as in the case of the D65 simulator. • Nd: number of samples, under test light source, sorted into the different color category from the case of the D65 simulator. • Percent correct = 100(Ns - Nd)/ ND65

  12. Percent Correct for 8 color names

  13. CIE color appearance model (CIECAM97s)

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

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

  16. CCRI of 5 light sources

  17. Ri, CCRIi and percent correct

  18. Correlation among Ri, CCRIi and PC

  19. Correlation among Ri, CCRIi and PC

  20. Correlation among Ri, CCRIi and PC

  21. Correlation among Ri, CCRIi and PC

  22. Ra, CCRIa and percent correct

  23. Correlation among Ra, CCRIa and PC

  24. SUMMARIES • Color rendering qualities of white LED (blue LED and yellow phosphor type) light sources of four different Tcp were examined with PC, Ri, and CCRIi. • 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 Ra.