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 ofhandling, 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.

What Color is White Solid-State Lighting?

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?

Learning Objectives1. 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.

What Color is White Solid-State Lighting?Presenters: Alfred R. Borden, The Lighting Practice; Naomi J. Miller, Pacific Northwest National Laboratory; Willem Sillevis Smitt, Xicato;Kevin Willmorth, Lumenique, LLC

Alfred R. Borden

Early problems with color shift and consistency

Problems with 3000 K MR-16 LED replacement lamps

Recent examples of the shift and consistency problems

Test of the proposed solution shows the same problem

Test of the proposed solution shows the same problem

3000 K CCT, +90 CRI

Correlated Color Temperature& Color Rendering IndexAre the most accessible standards of any value?

Naomi J. Miller

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

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!

Perceiving ColorSpectral Power Distribution (SPD)

Object Spectral ReflectanceHuman Observer Response and Interpretation

Spectral Power Distributions

Spectral Power Distributions

Quantifying ColorThe CIE System of Colorimetry

Chromaticity Diagrams & Chromaticity CoordinatesColor Mixing

Color Spaces

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

470475480

485

490

495

500

505

510515520 530

540550

560570

580590

600610620

700

x

y

• 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]

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

470475480

485

490

495

500

505

510515520 530

540550

560570

580590

600610620

700

Spectrum LocusPlanckian locus

x

yCIE 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

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

470475480

485

490

495

500

505

510515520 530

540550

560570

580590

600610620

700

Spectrum LocusPlanckian locus

x

yCIE 1931 (x, y) Chromaticity Diagram• Use for plotting

chromaticity • Use for light sources,

not for determining absolute appearance of objects!

• 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

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.70.0

0.1

0.2

0.3

0.4

0.5

u

v

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

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 0.70.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

u'

v'

• 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

Color AppearanceCorrelated Color Temperature

DuvMacAdam Ellipses

Δu’v’

Can you picture (0.4369, 0.4041)?Can you picture 3000 K?

0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.320.26

0.28

0.30

0.32

0.34

0.36

0.38

u

2500 K3000 K

5000 K

10000 K

575

4000 K

580570

6000 K

585

20000 K

10000 K

7000 K

6000 K

5000 K

4000 K

3000 K

2500 K

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!

0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.320.26

0.28

0.30

0.32

0.34

0.36

0.38

u

2500 K3000 K

5000 K

10000 K

575

4000 K

580570

6000 K

585

20000 K

10000 K

7000 K

6000 K

5000 K

4000 K

3000 K

2500 K

+ Duv

- Duv

CCT + DuvCIE 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)

Flies in the Color Ointment• Illuminance adaptation

(Hunt Effect)• Chromatic adaptation• And a million other

perceptual quandaries…

Consistent chromaticityWhen 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

Color RenderingColor Rendering Index (CRI, Ra)

R9IES TM-30-2015 Color Metrics

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

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

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.

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.

380 430 480 530 580 630 680 730 7800%

20%

40%

60%

80%

100% Reference Source LED Hybrid Blue Pump (2)

Wavelength (nm)

Rela

tive

Pow

er

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.

Thanks!Naomi Miller

Pacific Northwest National Laboratories

Portland OR

Naomi . Miller @ PNNL . gov

with a special nod to Dr. Michael RoyerPacific Northwest National Laboratories

Portland ORMichael . Royer @ PNNL . gov

Willem Sillevis Smitt

44

Quantifying White Points and ColorDo you know which colors these are?

(0.4599, 0.4106)(0.4369, 0.4041)(0.4053, 0.3907)(0.3804, 0.3767)

45

Quantifying White Points and ColorDo 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)

46

Quantifying White Points and ColorDo 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

47

Quantifying White Points and ColorDo 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 0.0003,000K Duv 0.0003,500K Duv 0.0004,000K Duv 0.000

CCT and CIE 1960 DuvMore intuitive metric for white

48

Quantifying White Points and Color

CIE 1931, “x,y” coordinates CIE 1976, “u’, v’” coordinates Simple metric specific for white

49

Quantifying White Points and ColorDuv positive: Greenish / Yellowish

Duv negative: Pinkish

Proposal for acceptable and un-acceptable color differences

Consistent White – How Can We Make It?1. Process Control and Product Design2. Measurement Accuracy3. Colorimetric Framework

1. Process Control and Product Design for Consistent White Accurately Matching Primaries Keeping them consistent

1. Process Control and Product Design for Consistent White Accurately Matching Primaries Keeping them consistent

1. Process Control and Product Design for Consistent White Accurately Matching Primaries Keeping them consistent

1. Process Control and Product Design for Consistent White Accurately Matching Primaries Keeping them consistent

1. Process Control and Product Design for Consistent White Accurately Matching Primaries Keeping them consistent

1. Process Control and Product Design for Consistent White Accurately Matching Primaries Keeping them consistent

58

Color Consistency Over the Life of the Product 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

2. Measurement Accuracy Found on LED datasheet:“[Manufacturer] maintains a tolerance of ±0.007 on x and y color coordinates in the CIE 1931 color space”

2. Measurement Accuracy

Proposed Tester Accuracy:Duv +/- 0.0005 (0.5mDuv)CCT +/- 20kAt application temperature

3. Colorimetric Framework

3. Colorimetric Framework 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 ]

3. Colorimetric Framework Improved color

matching functions significantly improve consistency between measurements and visual observations

[Csuti et al, PLDC 2011]

Measurement Accuracy and Photometric Framework – Does It Fully Resolve the Issue?

Pick the “Standard Observer” ;-)

Aging

Recommendations for Achieving Lit Spaces Lit with Consistent Color

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)

K. Willmorth

Quality IdentificationStepping beyond single level classification

CCT is listed in rounded values (3000K, 4000K, etc..

Virtually no product delivers exactly that value

Non- Sequitur Presentation 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”

Disconnect Between LED and Luminaire

3K LED

3K LED

3KLED

3KLED

3KLED

3K Output +/- 0K ~-100K -50 ~ -

150K -150 ~ -

300K

Clear TIR Optic

Diffuse R + Clear Lens

Specular R + Diffuser

Diffuse R +Diffuser

The MacAdam Confusion The steps are from a center point

2X Value1X Value

The MacAdam Confusion Without a universal center comparison point,

representations are irrelevant

Manufacturer A 2 Step

Manufacturer B 2 Step

Manufacturer C 2 Step

CCT Matched Value Color Metric 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

Color Distortion Accepted 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?

Incandescent CCT Matching Basis

Incandescent to Neutral White Model

65K Ideal Daylight CCT Match Basis

65K Ideal Daylight to Neutral White Model

The One-ness Issue 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

Multiple Facets Involved 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

Lighting Qualities ClassificationA Concept for multi-dimensional description of white lighting qualities

The IP Model

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/

immersion• 1-8 classification (8 highest)

A standard delivering classification information with some depth

Concept : LQC – Lighting Qualities Classification

Uniformity

LightQuality

Human Factors

LQC

• CCT Variation / COA• Duv• MacAdams Variable

• S/P Ratio• Flicker

• Color Fidelity• Saturation Effects• Spectral Consistency

(lowest R value)

Aggregate multi-dimensional quality classification1-5 values – 5 highest

Uniformity 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

Quality 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

Human Factors (optional?) 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

Applied LQC Rating Examples 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

Questions & Discussion

This concludes The American Institute of ArchitectsContinuing Education Systems Course