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What Color is Solid State Lighting - Panel Discussion

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Panel Discussion. Alfred Borden
Principal, The Lighting Practice; Naomi Miller
Senior Lighting Engineer, Pacific Northwest National Laboratory; Willem Sillevis Smitt,- Xicato; Kevin Willmorth, Lumenique LLC

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What Color is Solid State Lighting - Panel Discussion

  1. 1. Credit(s) earned on completion of this course will be reported to AIA CES for AIA members. Certificates of Completion for both AIA members and non-AIA members are available upon request. This course is registered with AIA CES for continuing professional education. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the AIA of any material of construction or any method or manner of handling, using, distributing, or dealing in any material or product. ___________________________________________ Questions related to specific materials, methods, and services will be addressed at the conclusion of this presentation.
  2. 2. The method for describing and quantifying the color of white lighting has always been insufficient. Metrics such as color temperature and color rendition never quite satisfied designers or specifiers of legacy sources using filaments, phosphors and gaseous excitation. The practical result is that it can be nearly impossible to find different sources in a lighting design that truly match color. The myriad of possibilities and pitfalls for white lighting offered by emerging solid-state technology demands a new vocabulary of descriptive terms, redress of manufacturing protocols, creation of new test metrics and rebuilding of specification standards. This panel will address these issues from the viewpoint of product developers, marketers, lighting designers and visual researchers. The presentation will review the roots and limitations of current methods, the opportunities for building new standards and market pressures that impede their adoption. The panelists intend for this presentation to push the industry into changing how it answers the basic question: What Color is White solid-state Lighting?
  3. 3. 1. Understand the deficiencies in current metrics as a means to describe, test and communicate the color of white solid-state lighting, including Correlated Color Temperature, Color Rendering Index, LM- 79 and MacAdam Ellipses. 2. Understand the use of color metrics to quantify color similarities and differences, and how to specify different lighting products in a single room and get them all to match. 3. Understand the importance of manufacturing quality and testing methods in the development of products that can be reliably specified to produce a specific color performance 4. Develop an appreciation for the effect of lighting color on human visual performance and physical health. 5. Understand the attributes of hue, value and saturation that must be considered in order to formulate an effective color metric.
  4. 4. Presenters: Alfred R. Borden, The Lighting Practice; Naomi J. Miller, Pacific Northwest National Laboratory; Willem Sillevis Smitt, Xicato; Kevin Willmorth, Lumenique, LLC
  5. 5. Alfred R. Borden
  6. 6. Early problems with color shift and consistency
  7. 7. Problems with 3000 K MR-16 LED replacement lamps
  8. 8. Recent examples of the shift and consistency problems
  9. 9. Test of the proposed solution shows the same problem
  10. 10. Test of the proposed solution shows the same problem
  11. 11. 3000 K CCT, +90 CRI
  12. 12. Are the most accessible standards of any value?
  13. 13. Naomi J. Miller
  14. 14. COLOR… • is one of the key attributes of lighting quality • is rooted in human perception COLOR METRICS… • allow for communication of color attributes • attempt to characterize human perception, but aren’t always perfect • have changed and improved over time • establish standards for specifying products and holding manufacturers accountable
  15. 15. Halogen 99 CRI , 2917 K, Duv 0.000 Compact Fluorescent 82 CRI, 2731 K, Duv 0.003 LED 84 CRI, 2881K , Duv 0.000 Metrics aren’t perfect!
  16. 16. Perceiving Color Spectral Power Distribution (SPD) Object Spectral Reflectance Human Observer Response and Interpretation
  17. 17. Spectral Power Distributions
  18. 18. Spectral Power Distributions
  19. 19. Quantifying Color The CIE System of Colorimetry Chromaticity Diagrams & Chromaticity Coordinates Color Mixing Color Spaces
  20. 20. 470 475 480 485 490 495 500 505 510 515 520 530 540 550 560 570 580 590 600 610 620 700 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 y x • Used for color of light, not objects • Colors of the spectrum appear around upper edge (in nm) • Bottom edge displays non-spectral colors; “purple line” • Colored background is theoretical only; cannot be displayed accurately • Equal energy point at (0.33, 0.33) CIE 1931 (x, y) Chromaticity Diagram [Adapted from NIST Spreadsheets]
  21. 21. 470 475 480 485 490 495 500 505 510 515 520 530 540 550 560 570 580 590 600 610 620 700 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 y x Spectrum Locus Planckian locus CIE 1931 (x, y) Chromaticity Diagram • Black body locus (also called Planckian locus) • Chromaticity is the x- y point on the diagram, but does not specify a spectrum
  22. 22. 470 475 480 485 490 495 500 505 510 515 520 530 540 550 560 570 580 590 600 610 620 700 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 y x Spectrum Locus Planckian locus Illuminant A CIE 1931 (x, y) Chromaticity Diagram • Use for plotting chromaticity • Use for light sources, not for determining absolute appearance of objects!
  23. 23. • MacAdam ellipses plot the just- noticeable- differences (those shown are 10 JNDs) • X-y color space is not perceptually uniform!!! CIE 1931 (x, y) Chromaticity Diagram
  24. 24. 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 v u u = 4x / (-2x + 12y + 3) v = 6y / (-2x + 12y + 3) CIE 1960 (u, v) Chromaticity Diagram • Simply linear transformation of CIE 1931 x-y • u-v coordinates • Intended to be more uniform (although not perfect) • Used for calculating CCT and Duv
  25. 25. u’ = 4x / (-2x + 12y + 3) v’ = 9y / (-2x + 12y + 3) CIE 1976 (u’, v’) Chromaticity Diagram 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 v' u' • Further transformation of CIE 1960 UCS (multiply v by 1.5) • u’-v’ coordinates • Is the most uniform available (still does not apply to objects) • Used for calculating Δu’v’ for color shift
  26. 26. Color Appearance Correlated Color Temperature Duv MacAdam Ellipses Δu’v’
  27. 27. Can you picture (0.4369, 0.4041)? Can you picture 3000 K?
  28. 28. 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 u 575 580 570 585 Correlated Color Temperature CIE 1960 (u, v) Chromaticity Diagram • Iso-CCT lines are perpendicular to Planckian locus in CIE 1960 UCS • CCT and chromaticity are not the same • Two sources that appear very different can have the same CCT!
  29. 29. 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 u 575 580 570 585 + Duv - Duv CCT + Duv CIE 1960 (u, v) Chromaticity Diagram • Duv adds a second dimension to better convey appearance • Iso-CCT lines shown are ± 0.02 Duv • Typical limits for white light are -0.006 to 0.006 (but depends on CCT)
  30. 30. Flies in the Color Ointment• Illuminance adaptation (Hunt Effect) • Chromatic adaptation • And a million other perceptual quandaries…
  31. 31. Consistent chromaticity When does consistent chromaticity between multiple light sources really matter? • When objects and surfaces are white or light in color • When they are viewed side-by-side • When the scene is not complex or the light source spills beyond the frame • When the application is color- critical
  32. 32. Color Rendering Color Rendering Index (CRI, Ra) R9 IES TM-30-2015 Color Metrics
  33. 33. Color Rendering Index (CRI) Ra• Intended to be a fidelity metric • Reference is blackbody radiation (< 5000 K) or a representation of daylight (> 5000 K) at same CCT as test illuminant. “Reference” does not mean “ideal” illuminant. • Compares chromaticity of eight (pastel) test color samples under test illuminant to reference illuminant • Averages (and scales) differences of each sample to result in single number • Maximum score of 100 if all samples match exactly • CRI is part of a larger system that includes 14 (now 15) total samples • Applicable to sources near blackbody locus
  34. 34. Because color space is skewed at red… R9=0+ is Good; R9=50+ is Very Good; R9=75+ is Excellent [Equivalent R9 CRI = 100 – (100-R9)/4 ] Special Color Rendering Index R9 • Same calculation method as CRI (Ra) • Saturated red • Red is particularly important for human skin complexion • Often considered a valuable supplement to CRI (Ra) • Can be gamed by manufacturers to get higher scores • Doesn’t communicate color saturation • Does not work well for very discrete SPDs (i.e., RGB LED) • Red colors seem to get short-changed Limitations of CRI
  35. 35. IES TM-30-2015 Color Rendering Metrics • TM-30 two-metric system (fidelity [Rf] and gamut [Rg]) • Rf quantifies average color rendition of 99 color evaluation samples (CES) selected to represent real objects uniformly distributed in color space, still related to the reference source • Rf ranges from 0 to 100 • Rg quantifies the average increase or decrease of color saturation. 100 means identical saturation to reference. Can range above and below 100.
  36. 36. TM-30-2015 Color Rendering Metrics• TM-30 two-metric system (fidelity [Rf] and gamut [Rg]). • Harder to game. • Rf and Rg are still averages. • Spreadsheet tool offers information and graphics on specific colors and hue bins. 0% 20% 40% 60% 80% 100% 380 430 480 530 580 630 680 730 780 RelativePower Wavelength (nm) Reference Source LED Hybrid Blue Pump (2)
  37. 37. Conclusions + Notes It is important to understand the limitations/intended use of the various color metrics.  Learn to use standard color photometry and tools to calculate a large range of metrics.  Even if light sources match when new, they may shift apart over time.  Metrics get you in the ballpark, but if you are a designer, you must evaluate color with your own eyes.
  38. 38. Thanks! with a special nod to Dr. Michael Royer Pacific Northwest National Laboratories Portland OR Michael . Royer @ PNNL . gov
  39. 39. Willem Sillevis Smitt
  40. 40. 44 Do you know which colors these are? (0.4599, 0.4106) (0.4369, 0.4041) (0.4053, 0.3907) (0.3804, 0.3767)
  41. 41. 45 Do you know which color these are? (0.4599, 0.4106) (0.4369, 0.4041) (0.4053, 0.3907) (0.3804, 0.3767) CIE 1931, “x,y” coordinates Or these? (0.2625, 0.5274) (0.2505, 0.5214) (0.2357, 0.5113) (0.2251, 0.5015)
  42. 42. 46 Do you know which color these are? (0.4599, 0.4106) (0.4369, 0.4041) (0.4053, 0.3907) (0.3804, 0.3767) CIE 1931, “x,y” coordinates Or these? (0.2625, 0.5274) (0.2505, 0.5214) (0.2357, 0.5113) (0.2251, 0.5015) CIE 1976, “u’, v’” coordinates
  43. 43. 47 Do you know which color these are? (0.4599, 0.4106) (0.4369, 0.4041) (0.4053, 0.3907) (0.3804, 0.3767) CIE 1931, “x,y” coordinates Or these? (0.2625, 0.5274) (0.2505, 0.5214) (0.2357, 0.5113) (0.2251, 0.5015) CIE 1976, “u’, v’” coordinates How about these? 2,700K Duv 0.000 3,000K Duv 0.000 3,500K Duv 0.000 4,000K Duv 0.000 CCT and CIE 1960 Duv More intuitive metric for white
  44. 44. 48 CIE 1931, “x,y” coordinates CIE 1976, “u’, v’” coordinates Simple metric specific for white
  45. 45. 49 Duv positive: Greenish / Yellowish Duv negative: Pinkish
  46. 46. 1. Process Control and Product Design 2. Measurement Accuracy 3. Colorimetric Framework
  47. 47.  Accurately Matching Primaries  Keeping them consistent
  48. 48.  Accurately Matching Primaries  Keeping them consistent
  49. 49.  Accurately Matching Primaries  Keeping them consistent
  50. 50.  Accurately Matching Primaries  Keeping them consistent
  51. 51.  Accurately Matching Primaries  Keeping them consistent
  52. 52.  Accurately Matching Primaries  Keeping them consistent
  53. 53.  Results from external LM-80 testing on Xicato Modules  At maximum rated current and temperature  10,000 hours  Plot shows individual parts at 0h and 10,000h  (0 and 10,000h connected by a line)  Worst case shift:  CCT +37K  Duv +0.0016 58
  54. 54.  Found on LED datasheet: “[Manufacturer] maintains a tolerance of ±0.007 on x and y color coordinates in the CIE 1931 color space”
  55. 55. Proposed Tester Accuracy: Duv +/- 0.0005 (0.5mDuv) CCT +/- 20k At application temperature
  56. 56.  Problem: Same Measurement Results (CCT, duv)  clearly different appearance  Cause: Color matching functions used to quantify color from spectral data [Csuti, Shanda, Harbers and Petluri, PLDC 2011, Getting Colour Right: Improved Visual Matching with LED Light Sources ]
  57. 57.  Improved color matching functions significantly improve consistency between measurements and visual observations [Csuti et al, PLDC 2011]
  58. 58.  Pick the “Standard Observer” ;-)  Aging
  59. 59.  Initial Color Consistency (LM-79)  ≤ 1x2 SDCM or  mDuv ≤ +/- 1 and CCT variation ≤ +/- 50K  Maintained Color Consistency –  Ask for LM-80 data represented in CCT and Duv  Ask for worst case conditions  Ask for largest shifters (B0) – not averages!  Light Source Manufacturer Measurement Consistency  ≤ +/-20K (CCT) and ≤ +/-0.5 mduv  Test 80 and 95CRI parts of same CCT from same manufacturer to validate their testing capability  on a white wall (color matching functions)
  60. 60. K. Willmorth
  61. 61. Stepping beyond single level classification
  62. 62. CCT is listed in rounded values (3000K, 4000K, etc.. Virtually no product delivers exactly that value
  63. 63.  A “3500K” product delivering 3358K with a 98CRIe will not appear the same as a “3500K” product with a 98CRIe delivering 3795K  TM-30 does not resolve this Range of “3500K”
  64. 64. 3K LED 3K LED 3K LED 3K LED 3K LED 3K Output +/- 0K ~-100K -50 ~ - 150K -150 ~ - 300K Clear TIR Optic Diffuse R + Clear Lens Specular R + Diffuser Diffuse R + Diffuser
  65. 65.  The steps are from a center point 2X Value 1X Value
  66. 66.  Without a universal center comparison point, representations are irrelevant Manufacturer A 2 Step Manufacturer B 2 Step Manufacturer C 2 Step
  67. 67.  Two sources with identical represented performance do not appear the same  Without any center anchor point for comparisons, there is no way to determine if products from disparate providers will appear the same  Averaged color performance conceals color distortion within spectral output
  68. 68.  We all know incandescent distorts color, yet accept it as having a high color rendering value  Daylight is assumed to be a singular color, and assign it as perfect in color performance What would happen if we used a central neutral white (5K?) to compare EVERYTHING to?
  69. 69. Incandescent CCT Matching Basis
  70. 70. Incandescent to Neutral White Model
  71. 71. 65K Ideal Daylight CCT Match Basis
  72. 72. 65K Ideal Daylight to Neutral White Model
  73. 73.  No one quality of light is universal to all needs  No single metric value can describe white  Over simplifying leads to confusion  Reliance on averaged values describing one facet of quality leads to errors between observation and metric representation
  74. 74.  Uniformity  CCT specific and range within production  Duv  McAdams steps from color point  Quality  Fidelity  Gamma effect  Lowest performance value  Human Factors  Flicker  S/P ratio
  75. 75. A Concept for multi-dimensional description of white lighting qualities
  76. 76. Dust Water Impact Q • Size of particle • Environmental (movement) • 1-6 classification (6 highest) • Size of object • Impact energy • 1-9 classification (8 highest) • Volume of water • Pressure/direction/immersio n • 1-8 classification (8 highest) A standard delivering classification information with some depth
  77. 77. Uniformit y Light Quality Human Factors LQ C • CCT Variation / COA • Duv • MacAdams Variable • S/P Ratio • Flicker • Color Fidelity • Saturation Effects • Spectral Consistency (lowest R value) Aggregate multi-dimensional quality classification 1-5 values – 5 highest
  78. 78.  Applied Metrics  CCT Limits (variation from the stated value)  Duv – Deviation above or below the Plankian locus  MacAdam steps from center point of stated CCT value @ Plankian locus intersection  Rating 1-5, based on combined results of values  1 delivers the least uniform performance  5 delivers optimal uniformity
  79. 79.  Applied Metrics  Average CRIe or TM-30 Rf  Lowest specific “R” value included in average  TM-30 Rg value  Rating 1-5, based on combined results of values  1 delivers the lowest color quality  5 delivers optimal color performance and rendering
  80. 80.  Applied Metrics  S/P Ratio  Flicker rating (Frequency, % and Index combined)  Rating 1-5, based on combined results of values  1 delivers the lowest visual performance  5 delivers optimal visual performance
  81. 81.  LQC-235  Factory space with medium color demand, high visual performance demands  Places emphasis on economy and visual performance  LQC-33  Budget specification with no human factors consideration  LQC-55  High color performance for retail/museum  LQC-555  Inspection task light for critical visual performance
  82. 82. This concludes The American Institute of Architects Continuing Education Systems Course

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