report on detailed data of investigated colour quality indices version 1€¦ · report on detailed...

ENG05 Lightning

Report on detailed data of investigated colour

quality indices – version 1.0

Deliverable 3.1.1

of Work Package 3

WP 3 (Human perception of SSL)

Authors:

D. Renoux, J. Nonne

Laboratoire National de Métrologie et d’Essais, France

D. Sabol, P. Nemeček

Slovenský Metrologický Ústav, Slovakia

A report of the EMRP joint research project

ENG05 “Metrology for Solid State Lightning”

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DELIVERABLE REPORT DOCUMENTATION PAGE

1. Work package

WP 3 (Human perception of

SSL)

2. Deliverable number

3.1.1

3. Reporting date

30 Avril 2012

4. Title (and subtitle) Report on detailed data of investigated colour quality indices

5. Author(s) Renoux, Sabol 6. Lead author (e-mail)

[email protected]

7. Contributing researchers (institutes)

LNE (France) Renoux, Nonne;

SMU (Slovakia) Sabol, Nemeček.

8. Other contributing work packages

none

9. Lead researchers in other WPs

none

10. Supplementary notes This report is an internal project report. It is not for general circulation and should

be kept within the JRP ENG05 team. However, the contained list and description of relevant colour rendering

metrics may be published as a scientific paper.

11. Abstract

This report is the first deliverable (no. 3.1.1) of JRP ENG05 work package 3. The report is broken down in four

parts. The two first parts presents two reviews of relevant colour rendering metrics, which are composed: (i)

chronologically as they have been proposed and (ii) divided into groups with according to specific feature or

principle of the metric. Each metric is then described with the author’s introduction, detailed implementation,

general and special index computations, test colour sample set, description of subjective experiment (if any),

the author’s conclusion and our assessment and conclusion. The third part gives the results of the computed

CQIs over a broad dataset of SPDs using graph plot of results and correlation tables between indices/metrics.

Some parameter/component change effect on metrics are given as well as some implementation details.

This document will serve as base for the evaluation of performance of the new proposed or advanced metrics

derived throughout the next tasks, in particular D3.1.2, D3.1.3?

12. Key words: colour rendering, colour quality index, colour metric, colorimetry

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LIST OF FIGURES

ERREUR ! AUCUNE ENTREE DE TABLE D'ILLUSTRATION N'A ETE TROUVEE.

LIST OF TABLES

Table 1: Methods and approaches developed to quantify colour rendition properties of light sources ordered

chronologically providing year of the publication, the method’s name, principle of operation and

promoter(s). ..................................................................................................................................... 2

Table 2: Chronological list of the Reference based CQIs methods ................................................................... 3

Table 3: TCS used for CRI calculation. ............................................................................................................ 4



Table 6: TCS used for CQS calculation. ......................................................................................................... 11

Table 7: Chronological list of the Gamut based CQIs methods. ..................................................................... 14

Table 8: TCS for the Feeling of contrast CQI. ................................................................................................ 18

Table 9: Chronological list of the CQI metrics explicitly combining approaches to the colour rendition. ..... 18

Table 10: Chronological list of CQIs metrics employing statistical approaches to the colour rendition. ....... 20

Table 11: List of Memory colour based CQIs. ................................................................................................ 23

Table 13 : Correlation between metrics for all SPD ........................................................................................ 37

Table 14 : Correlation between metrics for fluorescent SPD .......................................................................... 37

Table 15 : Correlation between metrics for LED Cluster SPD ....................................................................... 38

LIST OF SYMBOLS

Ri CIE CRI Special index

Ra CIE CRI General index

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LIST OF ABBREVIATIONS

EMRP European Metrology Research Programme

NMI National Measurement Institute

JRP Joint Research Project

WP Work Package

SSL Solid State Lighting

LNE Laboratoire National de Métrologie et d’Essais

SMU Slovenský Metrologický Ústav

CQI Colour Quality Index

LED Light Emitting Diode

FL Fluorescent Light

CFL Compact Fluorescent Light

TCS Test Colour Samples

CRI Colour Rendering Index

CAT Chromatic Adaptation Transform

CCT Colour Correlated Temperature

CQS Colour Quality Scale

CCRI Categorical Colour Rendering Index

RCRI Rank Order Colour Rendering Index

FCI Feeling of Contrast Colour Rendering Index

CSA Cone Surface Area

CPI Colour Preference Index

CRC Colour Rendering Capacity

CDI Colour Discrimination Index

HDI Harmony Distortion Index

Hhr Harmony Rendering Index

HRI Harmony Rendering Index

MCRI Memory Colour Rendering Index

GA Gamut Area

CRV Colour Rendition Vector

HDI Hue Distortion Index

CSI Colour Saturation Index

SPD Spêctral Power Density

QTH Quartz Tungsten Halogen

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TABLE OF CONTENTS

1 INTRODUCTION .......................................................................................................................... 1

2 CHRONOLOGICAL REVIEW OF COLOUR RENDERING QUALITY METRIC ............ 2

3 REFERENCE SOURCE BASED METHOD .............................................................................. 3

3.1 1965: Technical report CIE 13 - 1965: Method of Measuring and Specifying Colour Rendering

Properties of light .......................................................................................................................................... 3

3.2 1967: A Flattery Index..................................................................................................................... 5

3.3 1974: Technical report CIE 13.2-1974: Method of measuring and specifying colour rendering

properties of light sources. ............................................................................................................................ 6

3.4 1974: Colour-preference index ........................................................................................................ 7

3.5 1995: Technical report CIE 13.3-1995: method of measuring and specifying colour rendering

properties of light sources. ............................................................................................................................ 8

3.6 1999: CIE Collection 1999 research note: colour rendering, TC 1-33 closing remarks.................. 8

3.7 2005: Colour quality scale ................................................................................................................ 9

3.8 2009: Modified Colour Rendering Index (“CRI00”) ...................................................................... 11

3.9 2010: CRI CAM02-UCS ................................................................................................................ 13

4 GAMUT BASED METHODS ..................................................................................................... 14

4.1 1972: Colour Discrimination Index ............................................................................................... 14

4.2 1984: Colour Rendering Capacity ................................................................................................. 15

4.3 1993: Colour Rendering Capacity (modified) ............................................................................... 15

4.4 1997: Cone Surface Area ............................................................................................................... 16

4.5 2007: Feeling of Contrast Colour Rendering Index ...................................................................... 17

5 COMBINED CQI METRICS ..................................................................................................... 18

5.1 2007: Colour Rendering & Gamut Area Index (CRI & GAI) ........................................................ 18

5.2 2011: Rank Order Colour Rendering Index (RCRI)...................................................................... 19

6 STATISTICAL APPROACH TO COLOUR RENDITION .................................................... 20

6.1 2001: Categorical Colour Rendering Index (CCRI) ....................................................................... 20

6.2 2009: Statistical Approach to colour quality of solid-state lamps .................................................. 21

7 COLOUR HARMONY IMPRESSION BASED APPROACH ................................................ 22

8 MEMORY COLOUR BASED INDEX ...................................................................................... 23

9 TEST COLOUR SAMPLES FOR COLOUR RENDERING PROPERTIES ASSESSMENT24

9.1 A dataset fro evaluating colour rendering property of lamps ......................................................... 24

9.2 Sample selection for a colour fidelity index ................................................................................... 25

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10 COMPUTATION OF INDICES AND METRICS .................................................................... 26

10.1 Introduction .................................................................................................................................... 26

10.2 Dataset of SPDs .............................................................................................................................. 26

10.3 Graphs of selected SPDs ................................................................................................................ 27

11 GLOBAL COMPARISON BETWEEN COMPUTED METRICS ......................................... 29

11.1 Comparison between CIE CRI Ra 13.3 and CIE Ra96 .................................................................. 29

11.2 Comparison between CIE CRI Ra 13.3 and CQS 7.5 Qa ............................................................... 30

11.3 Comparison between CIE CRI Ra 13.3 and CRI CAM02-UCS .................................................... 30

11.4 Comparison between CIE CRI Ra 13.3 and RCRI ......................................................................... 31

11.5 Comparison between CIE CRI Ra 13.3 and MCRI ........................................................................ 31

11.6 Comparison between CIE CRI Ra 13.3 and CFI ............................................................................ 32

11.7 Comparison between CIE CRI Ra 13.3 and CCRI ......................................................................... 32

11.8 Comparison between CIE CRI Ra 13.3 and CRI00 ....................................................................... 33

11.9 Comparison between CIE CRI Ra 13.3 and HDI/Hhr (CH for 2D) ............................................... 33

11.10 Comparison between CIE CRI Ra 13.3 and GAI ................................................. 34

11.11 Comparison between CIE CRI Ra 13.3 and GAI ................................................. 34

11.12 Comparison between CIE CRI Ra 13.3 and FCI .................................................. 35

11.13 Comparison between CIE CRI Ra 13.3 and CFI modified – CAM02-UCS ........ 35

11.14 Comparison between CIE CRI R96a, CRI R00a and CRI CAM02-UCS ............ 36

11.15 Comparison between proposal of indices/metrics and combined indices/metrics36

12 PEARSON CORRELATION BETWEEN COMPUTED METRICS ..................................... 37

13 PARAMETER/METHOD CHANGE EFFECT ON REFERENCE BASED METRICS ..... 39

13.1 Effect on TCS on CIE CRI Ra 13.3................................................................................................ 39

13.2 Effect of reference method determination on CRI Ra 13.3 ............................................................ 40

13.3 Effect of CAT method on CRV of CQS with adaptation factor D=1 ............................................. 41

13.4 Effect of index formulae change on CCRI ..................................................................................... 41

14 DETAILED IMPLEMENTATION OF COMPUTED METRICS .......................................... 42

14.1 Use of Munsell Atlas 1269 matte colour chips for CCRI categories ............................................. 42

14.2 Comparison of original and updated method for statistical index .................................................. 42

14.3 Best fit of HDI/Rhr computation using dichromatic pairs of colours ............................................ 43

15 DETAILED RESULTS OF COMPUTED METRICS .............................................................. 43

15.1 Colour rendition vectors (CRV) maps ............................................................................................ 43

15.2 Special indices charts ..................................................................................................................... 44

16 SELECTION OF LIGHT SOURCES FOR THE SUBJECTIVE EXPERIMENT ............... 44

16.1 Lamps specifications ...................................................................................................................... 45

16.2 Preliminary results for colour rendition metrics ............................................................................. 45

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17 PRELIMINARY STATEMENTS AND CONCLUSION ......................................................... 46

17.1 Metrics selection and implementation ............................................................................................ 46

17.2 Selection of light sources for the subjective experiment ................................................................ 47

17.3 Conclusion ...................................................................................................................................... 48

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1 INTRODUCTION

This report is the first deliverable for Work Package 3 (WP 3: Human perception of SSL) of the

European Metrology Research Programme (EMRP) project ENG05 Metrology for Solid State Lighting. The

focus of this deliverable is targeted on the SSL properties in photopic regime, in particular colour rendering,

i.e. the ability of the light source to render colour of objects. The colour perception is determined by the

three factors: (i) surface reflectance spectrum, (ii) spectral composition of the light source and (iii) perceptual

properties and mechanisms of the human visual system, which can allow for variations in the light source

spectra while maintaining the same colour perception (colour constancy).

This deliverable provides an overview of already existing colour quality metrics/indices (CQIs), which have

been proposed and tested to the need of quantifying colour rendering capabilities of today light sources

including: halogen (QTH), fluorescent light tubes (FL), compact fluorescent light (CFL), but more

importantly, light emitting diodes (LED) based light sources. This overview will be later used as a reference

point for the tasks of the WP 3 deliverables, in particular: D3.1.2 (Report on indoor test and assessment of

studied CQIs metrics), D3.1.3 (Study of colour rendition index of street luminaires), D3.1.4 (Technical

guideline for use and interpretation of CQIs) and D3.1.5 (Recommendation report to CIE to improve SSL

colour quality. Best practice is drawn from field trials, psycho-visual and CQIs data).

The purpose of deriving a reliable model (colour quality metric), which would be able quantitatively estimate

the perceived colour, is essential for optimisation and achieving the full potential development of LED

lighting.

The deliverable is divided into 4 parts, the two first parts aim to offer two views on the relevant CQI

metrics: (i) chronological evolution of the metrics and (ii) sorted metrics into the groups created according to

common principles for the present metrics:

i) the first part (2) holds a table with chronological list of more or less known methods proposed to

tackle the problem of governing the colour rendition properties. Four entries are presented –

publication year, the name, basic principle of the method and the author(s).

ii) the second part is divided into sections (3 to 9). Each section starts with a brief description of the

common basis of all methods placed in the subsection. After this introduction, each method is

described in the individual subsection, which contains the following entries following by

highlighting the key features of every method.

iii) The third part gives the results of the computed CQIs over a broad dataset of SPDs. The SPDs are

sorted by categories to better visualize the indices/metrics differences for each category. The general

approach is to compare the proposal to the current CRI and to compare proposals between them.

Comparisons are mainly displayed using a chart of CQI value against TCS number. Correlation

tables between indices/metrics are presented for the overall SPDs or for a category of SPDs (FL,

lED clusters). Some effects of metric parameter or component changes are also presented, followed

by few graph reflecting specific implementation of metrics.

iv) The fourth part applies to the measured SPDs selected for the subjective experiment and illustrates

the diversity between technologies, SPD shape (peaks contribution, continuous contribution, CCT)

and indices/metrics results required to conduct a valuable study of colour rendering.

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2 CHRONOLOGICAL REVIEW OF COLOUR RENDERING QUALITY METRIC

Year Method Method Base Author

1965 CIE 13: Method of measuring and specifying

colour rendering properties of light sources (CRI) Reference based index Members of CIE 13

1967 Flattery index (Rf) Reference based index D. B. Judd

1972 Color-discrimination index (CDI) Gamut based index W. A. Thorton

1974 CIE 13.2: Method of measuring and specifying

colour rendering properties of light sources (CRI) Reference based index Members of CIE 13.2

1974 Color-preference index (CPI) Reference based index W. A. Thorton

1985 Colour preference index (CPI) Reference based index H. D. Einhorn

1984 Colour rendering capacity (CRC) Gamut based index H. Xu

1985 Rb index Reference based index T. Seim

1985 Combined color rendering-color preference index Reference based index J. Schanda

1990 Categorical color rendering index (proposed by

Boyton et al)

Statistical approach:

categorical color rendering

with reference source

R. M. Boyton, L. Fargo,

B. L. Collins

1992 Colour rendering capacity (modified) Gamut based index H. Xu


colour rendering properties of light sources (CRI) Reference based index Members of CIE 13.3

1997 Cone surface area (CSA) Gamut based index S. A. Fotios

1999 Colour rendering – TC 1-33 closing remark Reference based index Member of CIE TC1-33

2001 Categorical color rendering index (CCRI)

(proposed by Yaguchi et al)




H. Yaguchi, Y. Takahashi,

S. Shioiri

2005 Color Quality Scale (CQS) Reference based index Y. Ohno, W. Davis

2007 Feeling of contrast colour rendering index (FCI) Gamut based index K. Hashimoto, T. Yano,

M. Shimizu, Y. Nayatani

2007 Colour rendering & Gamut area index (CRI &

GAI)

Reference based and gamut

based index

M. S. Rea, J.P.

Freyssinier-Nova

2009 Modified Colour Rendering Index (CRI00) Reference based index D. Geisler-Moroder, A.

Dur

2009 Harmony rendering index (HRI) Colour harmony impression

based

F. Szabo, P. Bodrogi, J.

Schanda

2009 4D Colour quality metric: CCT-CFI-CSI-HDI


evaluation based on colour

rendering vector

A. Zukauskas, R.

Vaicekauskas, F.

Ivanauskas, H.

Vaitkevicius, M. S. Shur

2010 CIECAM02UCS Colour rendering index

(CIECAM02UCS CRI) Reference based index M. R. Luo

2010 Colour rendering index based on memory colours

(MCRI) Memory colour based index

K. Smet, W. R. Ryckaert,

G. Deconinck, P.

Hanselaer

2011 Memory colour quality metric (Sa) Memory colour based index

K. Smet, W. R. Ryckaert,

M. R. Pointer, G.

Deconinck, P. Hanselaer

2011 Rank order colour rendering index (RCRI) Reference and ordinal scale

based index

P. Bodrogi, S. Bruckner,

T. Q. Khanh

Table 1: Methods and approaches developed to quantify colour rendition properties of light sources ordered

chronologically providing year of the publication, the method’s name, principle of operation and

promoter(s).

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3 REFERENCE SOURCE BASED METHOD

The principle of these methods is based on comparing the colour coordinates of the rendered colour

of a sample illuminated by: (i) the testing light source, and (ii) the corresponding reference light source in a

colour space. The distance between the tested and reference coordinates stands for the colour rendition error

of the tested source compared to the reference. Therefore the reference source methods are naturally colour

fidelity metrics, however they can easily be modified to include subjective aspects like flattery or colour

preference (Flattery index, Colour Quality Scale). The reference light source is assumed to be either

Plankian radiator or daylight depending on Correlated Colour Temperature (CCT) of the tested light source.

The first and so far the only internationally accepted method quantifying colour rendering properties

of light sources was Colour Rendering Index (CRI) established by CIE in 1965. It is archetype of the

reference source based method, which has been updated twice in 1974 and 1994, and many other methods

were derived as modifications of CRI. Therefore key features of CRI will be pointed out in the next sub-

section, which then will be followed by description of other reference source based methods.

Year Method Author

1965 CIE 13: Method of measuring and specifying

colour rendering properties of light sources (CRI) Members of CIE 13

1967 Flattery index (Rf) D. B. Judd


colour rendering properties of light sources (CRI) Members of CIE 13.2

1974 Color-preference index (CPI) W. A. Thorton

1985 Colour preference index (CPI) H. D. Einhorn

1985 Rb index T. Seim

1985 Combined color rendering-color preference index J. Schanda


colour rendering properties of light sources (CRI) Members of CIE 13.3

1999 Colour rendering – TC 1-33 closing remark Member of CIE TC1-33

2005 Color Quality Scale (CQS) Y. Ohno, W. Davis

2009 Modified Colour Rendering Index (CRI00) D. Geisler-Moroder, A.

Dur

2010 CIECAM02UCS Colour rendering index

(CIECAM02UCS CRI) M. R. Luo

Table 2: Chronological list of the Reference based CQIs methods

3.1 1965: Technical report CIE 13 - 1965: Method of Measuring and Specifying Colour

Rendering Properties of light

Reference:

CIE Publication No. 13: “Method of measuring and specifying colour rendering properties of light

sources,” CIE, Vienna, (1965).

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Author Introduction: Colour rendering of an illuminant is the effect of the illuminant on the colour

appearance of objects by conscious or subconscious comparison with their colour appearance under a

reference illuminant. The CIE standardised a method of colour rendering index calculation based on test

samples in 1965, later modified in 1974 and 1995.

Method: The CIE test method is a purely calculation method, no visual observations are needed in this

application. The aim of CRI was to develop a metric, which would evaluate the colour rendering

performance of emerging compact fluorescent lamps. It compares the colour rendition of test colour samples

illuminated by the test and reference light source.

Detail Implementation: Two types of the reference light source are assumed: (i) Plankian radiator and (ii)

daylight. The reference light source has to have the exact value of Colour Correlated Temeperature (CCT)

as the tested light source. If CCT of the tested light source is higher than 5000 K then spectral power density

of the daylight is used as the test source and in all the other cases the test source is the Plankian radiator.

As a next step tristimulus values for 8 basic and 6 supplementary coloured samples are calculated

using the spectral power distribution of the test and reference light source respectively.

The colour differences, ΔEi, are calculated in CIE 1960 UCS colour space.

General index:

8

18

1

i

ia RR ,

(3.1)

where Ri is special CRI index.

Special index:

ii ER 6.4100 ,

(3.2)

where ΔEi is defined by the following equation

2*2*2*

iiii WVUE .

(3.3)

Test Colour Samples (TCS): see the Table 3

basic TCS: TCS1 – TCS8

supplementary TCS: TCS9 – TCS14

TCS

1

TCS

2

TCS

3

TCS

4

TCS

5

TCS

6

TCS

7

TCS

8

TCS

9

TCS

10

TCS

11

TCS

12

TCS

13

TCS

14

7.5 R

6/4

5 Y

6/4

5 GY

6/8

2.5 G

6/6

10 BG

6/4

5 PB

6/8

2.5 P

6/8

10 P

6/8

4.5 R

4/13

5 Y

8/10

4.5 G

5/8

3 PB

3/11

5 YR

8/4

5 GY

4/4

Table 3: TCS used for CRI calculation.

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The method was updated in 1974 – CIE 13.2 and 1995 – CIE 13.3.

3.2 1967: A Flattery Index

Reference: D.B. Judd, „A flattery index for artificial illuminants,“ IES Transaction, pp. 593-598, (Oct 1967).

X. G. Beng, K. W. Houser, “A review of colour rendering indices and their application to

commercial light sources,” Light. Res. Technol. 36, pp 183-199, (2004).

Author Introduction: CRI (see Section 3.1) penalizes any departure from the true colours – if lower CRI is

preferred – these distortions were such as to flatter objects and be preferred by observers.

Method: similar to CRI method– but the target colours are the preferred colours as would be seen under the

ideally flattering source.

Detail Implementation: The special indices are computed as they are computed in the CIE 13 in the Section

3.1 with increments added to the preferred coordinates for the TCS. Increments are taken from the works of

Sanders, Buck and Froelich1,2,3

. The adopted values of the colour shifts are applicable for light sources with

CCT above 3500 K.

General index:

)(6.4100 ,Kff ER ,

(3.4)

where KfE , is the weighted arithmetical mean of the chromaticity difference for the test samples under the

test illuminant, with the chromaticity adjusted for one fifth of the preferred chromaticity shift.

TCS: chosen from the CRI TCS (TCS1 to TCS8, TCS13 and TCS14), the weighting is not uniform. The

scaling is adjusted to give a Rf of 90 to the reference source but cannot exceed 100.

Subjective experiment: none

Author’s conclusion: The flattery index is not recommended to be applied in its present form – but is

offered for consideration and further study.

The method was not updated with the latest edition of CIE 13.3

1 C. L. Sanders, “Color preference for natural objects,” Illum. Eng. 54, pp 452, (1959).

2 S. M. Newhall, R. W. Burnham, J. R. Clark, “Comparisons of successive with simultaneous color matching,” J. Opt.

Soc. Am. 47, pp 43, (1957). 3 C. J Bartleson, “Memory colors of familiar objects,” J. Opt. Soc. Am. 50, pp 73, (1960).

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3.3 1974: Technical report CIE 13.2-1974: Method of measuring and specifying colour

rendering properties of light sources.

Reference:

CIE, “Method of measuring and specifying colour rendering properties of light sources,” CIE

Publication No. 13.2, Vienna, (1974).

Author Introduction: This method is to be considered as the fundamental method of measuring and

specifying colour rendering properties of light sources and is recommended for type testing as well as for

testing individual lamps.

Method: the principle of the method remains coherent with the previous method introduced in 1965, see

Section (3.1), however Chromatic Adaptation Transform (CAT) – von Kries type – was added to account for

small chromaticity shifts between the tested and reference illuminants.

Detail Implementation: this metric comprises the same principle and the computation steps as for CIE 13-

1965, described in Section (3.1), in addition of using von Kries CAT applied to obtain adapted chromaticity

coordinates of the TCS under the tested illuminant. In this way it is compensated for different chromaticity

of the illuminants. The adapted chromaticity coordinates are calculated by using the following equations

ik

k

rik

k

r

ik

k

rik

k

r

ik

dd

dc

c

c

dd

dc

c

c

u

,,

,,

,

481.1518.16

4404.0872.10

,

(3.5)

ik

k

rik

k

r

ik

dd

dc

c

cv

,,

,

481.1518.16

520.5

,

(3.6)

where

vuv

c 1041

,

(3.7)

uvv

d 481.1404.0708.11

.

(3.8)

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General index:

8

18

1

i

ia RR ,

(3.9)

where Ri is special CRI index.

Special index:

ii ER 6.4100 ,

(3.10)

where ΔEi is colour difference between the reference and tested light source.

Test Colour Samples (TCS): see the Table 3

basic TCS: TCS1 – TCS8

supplementary TCS: TCS9 – TCS14

TCS

1

TCS

2

TCS

3

TCS

4

TCS

5

TCS

6

TCS

7

TCS

8

TCS

9

TCS

10

TCS

11

TCS

12

TCS

13

TCS

14

7.5 R

6/4

5 Y

6/4

5 GY

6/8

2.5 G

6/6

10 BG

6/4

5 PB

6/8

2.5 P

6/8

10 P

6/8

4.5 R

4/13

5 Y

8/10

4.5 G

5/8

3 PB

3/11

5 YR

8/4

5 GY

4/4



Author’s conclusion:

The method was updated in 1974 – CIE 13.2 and 1995 – CIE 13.3.

3.4 1974: Colour-preference index

Reference:

W. A. Thorton, “Validation of the color preference index,” J. Illum. Eng. Soc. 4, pp. 48-52, (1974).



Method: similar to CRI method (Section 3.1) – but the target colours are the preferred colours under the

reference source.

General index :

ECPI 18.7156 .

(3.11)

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Detail Implementation: The special indices are computed as they are computed in the CIE 13 with an

increment added to the preferred coordinates for the TCS. Increments are taken from the Judd’s work, see

Section 3.2. The increments refer to sky light and artificial illuminants approximating it. The method

applied for illuminants is not too different from that of D65.

TCS: chosen from the CRI TCS (TCS1 to TCS8), the weighting is uniform. The scaling is adjusted to give

a CPI of 100 to the reference source, which can be remarkably exceeded with a maximum of 156 to for a test

source with ideal preference rendering.

The method was not updated with the latest edition of CIE 13.3

3.5 1995: Technical report CIE 13.3-1995: method of measuring and specifying colour

rendering properties of light sources.

Reference:

CIE, “Method of Measuring and Specifying Colour Rendering Properties of Light Sources,” CIE

Publ. No. 109-1995, (1995).

Method: this method is a verbatim re-publication of the method published in 1974 described in detail in

Section Error! Reference source not found..

3.6 1999: CIE Collection 1999 research note: colour rendering, TC 1-33 closing remarks

Reference:

CIE, Colour rendering, TC 1-33 closing remarks, CIE Publ. No. 135/2, (1999).

Author Introduction:

Method: the principle of the method remains the same as in first method introduced in 1965, see Section

(3.1). However, amendments were added, which some of those are not disambiguous:

|Reference illuminant is either restricted to a choice from 6 recommended illuminants (D65,

D50, P4200, P3450, P2950, P2700) or the nearest daylight/black-body illuminant is found

using CIELAB space.

New TCS set: 8 from ColorChecker® and 2 skin complexion (Caucasian and Oriental)

colours.

New chromatic adaptation: Bradford transformation.

The calculation of Ra has to be performed according to a non-technical decision due to lack

of disagreement among the committee members.

Detail Implementation:

|Reference illuminant has to be selected either in CIELAB space or chosen from six

recommended illuminants.

Chromaticity coordinates and Y values are calculated for the new TCS illuminated by the test

and reference illuminant.

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Chromatic adaptation – CIECAT94 – is applied for both data sets transforming them to D65

illuminant adaptation. All further calculations are performed in D65 adapted space.

All x, y, Y parameters are transformed to CIELAB space, where colour differences are also

calculated.

Determination of Ri values and conclude on Ra. The calculation of Ra has to be performed

according to a non-technical decision due to lack of disagreement among the committee

members.

General index:

10

1

9610

196

i

ia RR ,

(3.12)

where: *100 ii EcR ,

(3.13)

where:

248.396 cRc a , for the method, in which 6 target chromaticities are to be used as

reference illuminant.

032.3/96 aLABTCCRc , when the nearest daylight/black-body chromaticity in

CIELAB is used.

TCS :

TCS No. TCS

1

TCS

2

TCS

3

TCS

4

TCS

5

TCS

6

TCS

7

TCS

8

TCS

9

TCS

10

Color

Checker No.

MCC

15

MCC

7

MCC

16

MCC

11

MCC

14

MCC

18

MCC

13

MCC

17

Cauca-

sian

skin

Orien-

tal

skin Munsell

notation

5 R

4/12

5 Y

6/11

5 Y

8/11.1

5 GY

7.08/9.1

0.1G

5.38/9.65

5 B

5/8

7.5 PB

2.9/12.75

2.5 RP

5/12


Subjective experiment : none

Author’s conclusion: the TC hopes to resolve the present issues and until then recommends to experiment

with the described method and compare the obtained values with visual experiments.

3.7 2005: Colour quality scale

Reference:

Y. Ohno, “Color rendering and luminous efficacy of white LED spectra,” SPIE 4th conference on

SSL, (2004).

Y. Ohno, “Spectral design considerations for white LED color rendering,” Opt. Eng. 44(11), 111302,

9 pages, (2005).

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W. Davis, Y. Ohno, “Toward an improved color rendering metric,” SPIE 5th conference on SSL,

(2005).

W.Davis, Y. Ohno, “Approaches to color rendering measurement,” J. Mod. Opt. 56(13), pp 1412-

1419, (2009).

W. Davis, Y. Ohno, “Color quality scale,” Opt. Eng. 49(3), 033602, 16 pages, (2010).

Author Introduction: CQS is proposal of new metric, which aspires to replace the current CRI. It shares the

same principles but employs CIELAB, CMCCAT2000 as CAT, more saturated TCS and weighting function.

Moreover, it does not penalise for increase in the colour saturation but it does penalise for hue deviations.

Method: selection of the reference illuminant is the same as for CRI, see Section 3.1. CMCCAT2000 is

applied in order to correct for chromatic adaptation. Tristimulus values after the chromatic adaptation are

calculated for the TCS illuminated by the test illuminant. The adapted and reference tristimulus values are

transformed to CIELAB colour space. Then colour differences are then calculated excluding the

contribution of increased colour saturation. The score is combines the colour differences from all TCS by

using root mean square error, on which a logarithm is then applied in order to obtain non-negative score.

Furthermore the resulted value is corrected by a factor taking into account CCT.

Detail Implementation: calculations of the colour differences, which does not penalise an increased colour

saturation

iabsatiab EE ,,, , if 0, iabC ,

(3.14)

or

2,

2

,,, iabiabsatiab CEE , if 0, iabC ,

(3.15)

where

refiabtestiabiab CCC ,,,,, .

(3.16)

Combining the colour differences

215

1

,,15

1

i

satiabrms EE ,

(3.17)

and computing the first score, which can be negative

rmsrmsa EQ 1.3100, .

(3.18)

Application of logarithmic function to achieve a positive score

110/expln10 ,1000, rmsaa QQ ,

(3.19)

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General index:

1000, aCCTa QMQ ,

(3.20)

where

612.100255.0103959.8102672.9 72113 TTTMCCT

(3.21)

for T < 3500 K, while MCCT = 1 for T ≥ 3500 K.

TCS :

TCS 1 TCS 2 TCS 3 TCS 4 TCS 5 TCS 6 TCS 7 TCS 8

7.5 P 4/10 10 PB 4/10 5 PB 4/12 7.5 B 5/10 10 BG 6/8 2.5 BG 6/10 2.5 G 6/12 7.5 GY 7/10

TCS 9 TCS 10 TCS 11 TCS 12 TCS 13 TCS 14 TCS 15

2.5 GY 8/10 5 Y 8.5/12 10 Y 7/12 5 YR 7/12 10 R 6/12 5 R 4/14 7.5 RP 4/12

Table 6: TCS used for CQS calculation.

Subjective experiment : several done and on progress, not published yet.


3.8 2009: Modified Colour Rendering Index (“CRI00”)

Reference:

D. Geisler-Moroder, A. Dur, “Color-rendering indices in global illumination methods,”J. Electron

Imaging 18(4), 043015-1, 11 pages, (2009).

Author Introduction: the authors aim to present a revised approach for the CRI calculations, in which state-

of-the-art colorimetric methods are used but procedurewise still proceed in line with the CIE method.

Method: three main improvements have been implemented and tested:

CIELAB colour space.

Linearized Bradford chromatic adaptation transform.

Colour differences are evaluated by using CIEDE2000.

The metric is computed in accordance with the computational procedure of TC 1-33 closing remarks, which

are described in detail in Section 3.6.

The metric predictions were tested by using two sets of TCS: (i) 14 samples from Munsell Atlas, see Section

Error! Reference source not found., and (ii) currently recommended 10 samples from Macbeth

ColorChecker®, see Section Error! Reference source not found..

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General index:

n

i

i

n 1

0000 CRI1

CRI ,

(3.22)

where CRIi00 stands for the special index and n = 8 or 10 depending on whether the TCS from Munsell Atlas,

or from Macbeth ColorChecker® with two skin complexions are used.

*100 ii EcR ,

(3.23)

Special index: ii Ec 0000 100CRI

(3.24)

where the constant c is set to 9.097 if n = 8 or to 6.927 if n = 10.

TCS : two sets:

14 samples from Munsell Atlas as recommended in CIE 13.3-1995, described in Section 3.5.

8 samples from Macbeth ColorChecker® and two skin complexions as recommended by CIE

TC 1-33, described in Section 3.6


Author’s conclusion: the choice of TCS set does not significantly change the general CRI.

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3.9 2010: CRI CAM02-UCS

References:

R. LUO “The quality of light sources” Coloration Technology, 127,pp 75-87, 2011

C.Li et Al. “Assessing Colour Rendering Properties of Daylight Sources part II : a new colour

rendering index: CRI-CAM02UCS – University of Leeds, 2011

C.Li et Al “Eveluation of light source based upon colour appearance and colour preference

assessments”, AIC 2007 Color Science For Industry

Author Introduction: There is a general consensus that the current CRI Ra is insufficient because of the use

of obsolete CIE metrics.

Method: The workflow is identical to the CIE CRI 13.3 with an updated colour space and colour shift

computation, the CAM02-UCS derived from the CIE CAM02.


222 '''UCS)-E(CAM02 MM baJ

General index:

ii

i

i

ER

R

UCS)-(CAM020.8100

8

1CAM02UCS-CRI

8

1

TCS : the original metric use the same TCS as the CIE CRI 13.3. One paper from K.Smet et Al (see chapter

7.) uses an updated set of 35 new TCS, and recently a set of 219 TCS was proposed (see chapter 9.) by Luo

et al.

Subjective experiment: yes, several experiments were conducted with LED light sources, classical light

sources and daylight simulators.

Author’s conclusion: the conclusions from the different papers are: the CRI-CAMUCS shows better

agreement to the visual difference than the original CIE-Ra, and the CAM02-UCS provides a solid

foundation for existing CRI and CQS.

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4 GAMUT BASED METHODS

The principle characteristic for these methods is absolute measure of area, which is determined by

chromaticity coordinates of TCSs. Larger surface suggests ability to render a broader range of colours by the

light source. However, none of these metrics speak about fidelity of the colour rendering.

Year Method Author

1972 Color-discrimination index (CDI) W. A. Thorton

1984 Colour rendering capacity (CRC) H. Xu

1992 Colour rendering capacity (modified) H. Xu

1997 Cone surface area (CSA) S. A. Fotios

2007 Feeling of contrast colour rendering index (FCI) K. Hashimoto, T. Yano, M. Shimizu, Y. Nayatani

Table 7: Chronological list of the Gamut based CQIs methods.

4.1 1972: Colour Discrimination Index

Reference:

W. A. Thornton, “Color-discrimination index,” J. Opt. Soc. Am. 62, pp 191-194, (1972).



Method: it is gamut based method. The index quantifies surface enveloped by TCS of the general CRI in the

CIE uv diagram.

General index: Gamut area is calculated by the following formula :

7887566545543443233212215.0 vuvuvuvuvuvuvuvuvuvuvuvuGA

8118 vuvu .

The Colour discrimination index is calculated as

100005.0

GACDI .

The gamut area of the illuminant C is about 0.005, thus the CDI is normalized to 100 for the illuminant C.


Author’s conclusion: it is proposed that gamut area is not only a measure of color-discrimination capability

of an illuminant but its color-rendering capability, i.e., that the two are operationally indistinguishable.

Observed suspicion that three-band illuminant might be rendering general objects (fruits, meats, skin colours

etc) better, in some sense, than does daylight.

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4.2 1984: Colour Rendering Capacity

Reference:

H. Xu, “Colour rendering capacity of illumination,” J. Illum. Eng. Soc. 13, pp. 270-276, (1984).



Author Introduction: Colour Rendering Capacity (CRC) aims to quantify the maximum possible number of

different colours, which can be displayed by a given illumination. CRC depends on the spectral power

distribution of the illuminant only.

Method: CRC computes the maximum chromaticity ranges at different luminance level in CIE 1960 uv

space, and then plots the area at the luminance level to form a curve. The relative area under the curve is

defined as the colour rendering capacity, so it is dimensionless quantifier.

TCS: this method requires no TCS.


Author’s conclusion: in lighting practise the CRC can be useful as a relevant predictor of what kind of

spectral composition of illumination can make a given chromatic environment, in general, appear more

colourful and brighter.

The method was updated in 1993 by using the CIE LUV colour space, where a colour solid forms clearly.

4.3 1993: Colour Rendering Capacity (modified)

Reference:

H. Xu, “Colour rendering capacity and luminous efficiency of a spectrum,” Light. Res. Technol. 25,

pp 131-132, (1993).



Author Introduction: CRC is a measure proposed to estimate the potential of the light of a certain spectral

power distribution for revealing a great range of different colours.

Method: The current method is an update of the method proposed in 1983 and discussed in the Section

Error! Reference source not found.. The novelty of the updated method compared to its predecessor is in

employing CIE 1976 L*u*v* colour space displaying the colour solid associated with the infinite set of all

possible spectral reflectance functions under the considered spectral power distribution.

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Detail Implementation: First, surface colour calculation is carried out by using the tested spectral power

distribution in conjunction with the reflectance spectra of optimal colours (on MacAdam limit). The colour

solid of the tested illuminant is designed in CIE L*u*v* colour space for L* levels between 0 and 100. Then

the volume of the colour solid is calculated in arbitrary units. The result is then normalised in such a way

that the volume of equienergy spectrum is equal to 1.0.

TCS: this method requires no TCS.


Author’s conclusion: Light source D65 has higher CRC than illuminant A, although the both are judged as

equally good on the criterion of CRI. The light with a spectral power distribution of high CRC has a more

chances of achieving good results.

4.4 1997: Cone Surface Area

Reference:

S. A. Fotios, “The perception of light sources of different colour properties,” PhD thesis, UMIST

UK, Manchester, (1997).



Method: Cone Surface Area (CSA) combines the measures of gamut area and source chromaticity in CIE

1976 u’v’ chromaticity diagram. CSA in fact is the surface area of a cone, which has a circular base of the

same area as the orthogonal gamut and a height of w’, which is the chromaticity coordinate.


General index:

area surface curved base of area CSA

rLrCSA 2,

(4.1)

where 2r stands for area of the base and rL for the curved surface area respectively. r is radius of the

base, which is estimated from the gamut area by the following equation:

GAr ,

(4.2)

and L is the length of the slope of the cone defined as:

22 'wrL .

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(4.3)

Lastly, w’ is perpendicular height of the cone defined by the following equation:

''1' vuw .

(4.4)

TCS: none



4.5 2007: Feeling of Contrast Colour Rendering Index

Reference:

K. Hashimoto, T. Yano, M. Shimizu, Y. Nayatani, “New method for specifying color-rendering

properties of light sources based on feeling of contrast”, Col. Res. and Appl. 32, 361-371, (2007).

Author Introduction:

Method: is based on the principle that a light source that increasing the feeling of contrast (also referred to

as visual clarity or colour discrimination) also increases the saturation of coloured objects. The FCI metric

estimates the feeling of contrast as a function of the CIELAB gamut area of four highly saturated coloured

samples of Munsell Atlas: red, green, yellow and blue. The samples are illuminated by the test light source

(GATest) and D65 reference illuminant (GAD65)

Detail Implementation: The tristimulus values of the test illuminants are transformed using the CIECAT94,

the gamut area are computed in the 3D L*a*b* space by the sum of the area of (RGB) and ( RGY) triangles.

Un updated version in CIECAM is proposed in the paper.

General index:

23

65

100

D

Test

GA

GAFCI

(4.5)

TCS:

TCS 1 TCS 2 TCS 3 TCS 4

5 R 4/12 5.5 G 5/8 5 Y 8.2/10 4.5 B 3.2/10

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Table 8: TCS for the Feeling of contrast CQI.

Subjective experiment: yes, 20 lamps were rated by observer for the feeling of contrast and compared to

computed CFI and CRI Ra 13.3.

Author’s conclusion: The concept of FCI and CRI Ra are completely different to each other, the whiter LED

cluster (R,G,B) have high CFI value and are ont properly estimated by Ra, but can be evaluated

quantitatively by using FCI and Ra together.

5 COMBINED CQI METRICS

2007 Colour rendering & Gamut area index (CRI & GAI) Reference based and

gamut based index

M. S. Rea, J.P.

Freyssinier-Nova

2011 Rank Order Colour Rendering Index (RCRI) Reference and ordinal

scale based index

P. Bodrogi, S. Bruckner,

T. Q. Khanh

Table 9: Chronological list of the CQI metrics explicitly combining approaches to the colour rendition.

5.1 2007: Colour Rendering & Gamut Area Index (CRI & GAI)

Reference:

M.S. Rea, J.P. Freyssinier-Nova, “Color Rendering: a tale of two metrics”, Col. Res. Appl. 33, pp

192-202, (2007).

M.S. Rea, J.P. Freyssinier-Nova, “Color Rendering: beyond pride and predjudice,” Col. Res. Appl.

35, pp 401-209, (2010).

Author Introduction: There are several aspects of colour rendering and it should not be measured by any

single metric.

Method: This metric combines and takes advantages two metrics: CRI and GAI already described in Section

3.5 and a gamut area respectively, to quantify two aspects of the colour rendering properties: (i) fidelity of

colours and (ii) colour discrimination.

Detail Implementation: see Eq in 3.5 for CRI and the following formulae for GAI. Where the reference

gamut is the gamut area of the EES (equal energy stimulus). The gamut area are computed with the TCS of

the CRI Ra 13.3.

EES

Test

GA

GAGAI 100

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General index: considering the CRI Ra and the GAI values

TCS: same as CRI 13.3

Subjective experiment: in 2007 three experiments: (i) using warm light sources only (CCT < 4000 K), (ii)

using cold light sources only ((CCT > 5000 K) and (iii) both types of the light sources, have been reported,

which consisted of two tasks:

1. Observers performed Farnsworth-Munsell 100 hue test.

2. Observers had to judge naturalness and vividness of a collage with two species of birds (blue and red

colour).

In average 12 observers of age between 19 – 62 years participated in the experiments.

In 2010 an experiment, in which 18 observers (10 males, 8 females of age between 21 and 38 years) had to

judge attractiveness and naturalness of fruits and vegetables and mark three hues which the most influenced

their decision.

Both experiments lead the same conclusion.

Author’s conclusion: a good light source should have CRI > 80 and GAI between 80 and 100.

5.2 2011: Rank Order Colour Rendering Index (RCRI)

Reference:

P. Bodrogi, S. Bruckner, T. Q. Khanh, “Ordinal scale based description of Colour Rendering,” Col.

Res. Appl. 36, pp. 272-285, (2010).

P.Brodogi et al., Research report and proposal for a new assessment procedure – written for CIE TC

1-69: Color rendition by white light sources (2009).

Author Introduction: In practice, a rank order scale may be more suitable than a continuous scale, the main

motivation is to meet the main’s non-expert user’s expectation.

Method: The method use the CRI-CAMUCS colour difference, then for every TCS (1 to 17) five absolute

differences are calculated, the smallest difference corresponds to a ranking level (1 out of 5), then the

number of ranking (N1,N2,..) over the 17 TCS are computed and used for the general index.


222 '''UCS)-E(CAM02 MM baJ

General index:

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31

21

17100

NNRCRI

TCS: 17 TCS chosen from the Macbeth test colour (12) chart and CQS (5).

Subjective experiment: yes, several experiments for the assessment of visual difference to establish the

metric had been performed and validation experiment on real still life are underway. In September 2009 an

already 6 observers and 5 2700k light sources was in the phase to be extended with 2 observers and 5 and

further 5 4500K lights sources.

Author’s conclusion: The present rank-order rendering index (RCRI) solves the problem of user

interpretation with discrete increments. The RCRI yields to lower values than CRI Ra, CQS, CRI-CAMUCS,

except for the best light sources and keeps the same order than CRI Ra. RCRI showed an overall

performance of 73% of good prediction. Alternative RCRI are proposed but not validated.

6 STATISTICAL APPROACH TO COLOUR RENDITION

1990 Categorical color rendering index (proposed by

Boyton et al)




R. M. Boyton, L. Fargo,

B. L. Collins

2001 Categorical color rendering index (CCRI)

(proposed by Yaguchi et al)




H. Yaguchi, Y.

Takahashi, S. Shioiri

2009 4D Colour quality metric: CCT-CFI-CSI-HDI


evaluation based on colour

rendering vector

A. Zukauskas, R.

Vaicekauskas, F.

Ivanauskas, H.

Vaitkevicius, M. S. Shur

Table 10: Chronological list of CQIs metrics employing statistical approaches to the colour rendition.

6.1 2001: Categorical Colour Rendering Index (CCRI)

Reference:

H. Yaguchi, Y. Takahashi, S. Shioiri, “A proposal of color rendering index based on categorical

color names,” Int. Lightning Congress Istanbul, Vol. II, (2001).

H. Yaguchi, N. Endoh, T. Moriyama, S. Shioiri, “Categorical color rendering of LED light sources,”

CIE Expert Symposium on LED Light Sources, Tokyo, (2004).

H. Yaguchi, “Color categories in various colour spaces”, presentation 9th CIC Scottsdale, (2001).

Author Introduction: Colour categorization of colours plays a important role in many practical applications.,

such as colour communication, colour signalling, colour coding and so on. The colour name is considered as

one of the attribute of colour appearance independently of the viewing condition. The purpose is to take into

account colour categorization rather than the colour difference for evaluating the quality of a light source.

Method: The method uses eleven colour categories, of 4 different J regions, bounded in the Ch plane of the

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CIECAM97s space. First, Four colour-chips for the D65 illuminant lying in each category bounding ‘i’ are

determined, lets denote Si the corresponding region in the Ch plane, then these colour-chips are used to

compute the region St under the test light source. The special index is computed by the ratio of the

intersection of St and Si dived by St.


St

StSiCCRIi

100

General index: the general index is the average of the special indices.

21

1

21/)(i

CCRIiCCRI

TCS: 292 colour-chips from the Munsell glossy atlas.

Subjective experiment: yes, to establish the reference colour categories and to test the method.

Author’s conclusion: the author states from its subjective experiments than prediction with CCRI is better

than CIE Ra for white LED light sources and recommend the CCRI, rather than CIE Ra, for evaluating the

colour rendering quality of such light sources.

6.2 2009: Statistical Approach to colour quality of solid-state lamps

Reference:

A.Zukauskas et al., “Statistical Approach to colour quality of solid-state lamps”, IEEE journal of

selected topics in quantum electronics, vol n°6 (2009).

A.Zukauskas et al., “Colour rendition properties of solid-state lamps”, Journal of physics D : applied

physics 43 (2010).

Author Introduction: Since its introduction the CIE CRI metric is criticized for numerous drawbacks and

with the introduction of LED lighting the criticism reach a disruptive level. One way could to refine the CRI

method, or an alternative way is the development of more radical concept by employing a large number of

TCS.

Method: The method computes a colour fidelity index (CFI), a colour saturation index (CSI), a hue

distortion index (HDI) and uses the CCT to form the 4-D metric. These indices are computed using the CRV

determined by a reference light source calculated by the same method than the CIE CRI 13.3 method and a

CAT applied to the test source identical to the CIE CRI 13.3.

Detail Implementation: The CFI computation use an elliptical cylinder in the Yxy space with a 0.7%

threshold for luminance and the interpolated Mac Adams ellipse. The CSI and HDI exploits the magnitude

and the direction of the CRV.

General index: there are percentages of count for the CFI, CSI, and HDI over the all the TCS.

TCS: 1269 Munsell matte colours altlas

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Author’s conclusion: the 4D proposed metric for rating the colour quality of white lamps might be of

moderate significance for common Fls but unveils important colour rendition of solid-state lamps, in

particular for LED clusters, which have low colour fidelity but render the colours more saturated. The

authors outlines that the method requires to be updated with more recent colorimetric computation.

7 COLOUR HARMONY IMPRESSION BASED APPROACH

2009 Harmony rendering index (Hhr) Colour harmony impression based F. Szabo, P. Bodrogi, J. Schanda

Reference:

P. Bodrogi, P. Csuti, P. Horváth, J. Schanda, “Why does the CIE Colour Rendering Index fail for

white RGB LED light sources?” CIE Expert Symposium on LED Light Sources, Tokyo, (2004).

F. Szabo, P. Bodrogi, J. Schanda, “Experimental modeling of Colour Harmony,” Col. Res. Appl. 35,

pp 34-49, (2010).

F. Szabó, P. Bodrogi, J. Schanda, “A colour harmony rendering index based on predictions of colour

harmony impression,” Lighting Res. Technol. 41, pp 165-182, (2009).

F. Szabo, P. Bodrogi, I. Zilzi, J. Schanda, “Visual experiment on colour rendering harmony : a

formlua and a rendering index”, available online through http://cie2.nist.gov/TC1-69/….

Author Introduction: The failure of the current CIE CRI for modern (especially LED based) has been

demonstrated. There is no agreement about the factors to be included in the new colour quality metric. But

the colour harmony rendering property was declared, by the CIE meeting TC 1-69 - Bejin 2007, to be an

observable factor in colour quality.

Method: the method evaluates the difference between the colour harmony of the test light source and a

reference light source. The colour harmony is computed from predictive harmony formulae (CHF2M,

CHF2D,CHF3M,CHF3T ) applying to pairs and triads of for monochromatic, dichromatic and trichromatic

colours.

Detail Implementation: the reference light source is calculated by the same method than the CIE CRI 13.3

method. The colour harmony formulae uses the JCh correlates of the CIECAM02.

General index:

n

i

testirefihr CHFCHFkR1

,,100

where CHFi,ref and CHF i,test are calculated respectively for the reference and test source; CHF2M,

CHF2D,CHF3M,CHF3Tcan be substituted in the above formula. Recent publication uses the absolute value of

the difference (CHFi,ref - CHF i,test).

TCS: 17 pairs of Munsell matte colours and 5 triads of Munsell matte colours.

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Subjective experiment: several for quantify the colour harmony formulae and test the harmony rendering

index (Hhr).

Author’s conclusion: The harmony rendering index (Rhr) showed good correlation s with the results of

subjective experiments. The authors suggest using Rhr to supplement the current rendering index of the CIE

to describe colur quality of light sources.

8 MEMORY COLOUR BASED INDEX

2010 Colour rendering index based on

memory colours (MCRI) Memory colour based index

K. Smet, W. R. Ryckaert, G.

Deconinck, P. Hanselaer

2011 Memory colour quality metric (Sa) Memory colour based index K. Smet, W. R. Ryckaert, M. R.

Pointer, G. Deconinck, P. Hanselaer

Table 11: List of Memory colour based CQIs.

References:

K. Smet, W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer, “Memory colours and colour

quality evaluation of conventional & SSL lamps,” Opt. Express 18, pp, 26229-26244, (2010).

K. Smet,W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer, “Colour Appearance rating of

familiar real objects,” ,” Col. Res. Appl. 36, pp 192-200, (2011).

K Smet, W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer, “Correlation between colour

quality metric predictions & visual appreciation of light sources,” Opt. Express 19, pp. 8151-8166,

(2011).

K Smet, W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer, “Optimal color quality of LED

cluster based on memory colours,” ,” Opt. Express 19, pp. 6903-6912, (2011).

K Smet, W.R. Ryckaert, M.R. Pointer, G. Deconinck, P. Hanselaer” Optimisation of colour quality

of LED lighting with reference to memory colours”, Lighting Research Technology, published

online 11 January 2012.

Author Introduction: The current metric, i.e. CRI Ra 13.3, does not always correspond to the actual

perceived light source. Colour quality is not just fidelity but also preference, attractiveness, colour

discrimination and colour harmony. Daylight and Planckian radiator used as reference may not be the

“optimal” light source for comparison.

Method: The memory colour rendering index is derived from the similarity of colour objects rendered under

the tested light source. The similarity is based on a visual rating experiment of nine familiar objects.

Detail Implementation: the chromatic adaptation transform to D65, using CAT02, is applied on tristimulus

of the CIE 1964 10° observer. Then the adapted tristimulus converted in IPT colour space are inputted in the

specific similarity distribution Si(X) of objects. The specific Si(X) is rescaled using a sigmoid-like function.

ENG05 Lighting

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)10...1(

)())]()(())([(

2

1

2,

1,

4,5,

5,3,

2,

1,

i

eXS i

ii

ii

iiT

i

ii a

aX

aa

aa

a

aX

ii

General index: the general index, Rm, is computed from the general degree of similarity (Sa) using a

geometrical mean of the individual objects.

n

n

i

ia SS

1

3

21

21

)1(

2100,

p

dp

dp

im p

i

p

i

e

eR

with )ln(2 ii Sd

TCS: spectral reflectance of a green apple, a banana, an orange, dried lavender, a smurf figurine, strawberry

yoghurt, a sliced cucumber, a cauliflower and Caucasian skin.

Subjective experiment: Yes, for metric elaboration, comparison with other metric and validation.

Author’s conclusion: The authors found, from their visual experiments, that the ranking with MCRI gives

better prediction than those obtained with CIE Ra and CQS.

9 TEST COLOUR SAMPLES FOR COLOUR RENDERING PROPERTIES

ASSESSMENT

2012 A dataset fro evaluating colour

rendering property of lamps

Improvement of fidelity

approach M.R. Luo

2012 Sample selection for a colour fidelity

index

Improvement of fidelity

approach Zs. Kosztyan

9.1 A dataset fro evaluating colour rendering property of lamps

At Leeds university a procedure was applied to a collection of 100.000 SPD of colours samples to set up 219 TCS. The

procedure uses 3 steps :

Targets Evenly distributed in CIELAB : 36 hues regions , 3 lightness levels and 3 chroma levels.

Distances (ΔE00) to TCS targets less than 2.5

Test colour constancy index (CII) with illuminants A, F2 and F11and using CMCCON02.

Targets adjustment until good colour constancy is achieved.

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9.2 Sample selection for a colour fidelity index

A test colour sample set including 90 references samples and 90 metameric samples plus 3 skin colours have been

composed.

The method used classified colours samples, from the Leed’s collection of 100.000 SPD, into clusters according their

spectra correlation. A set of representative metameric sample for each reference is constituted and the one giving the

best colour consistency (CII) is selected.

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10 COMPUTATION OF INDICES AND METRICS

10.1 Introduction

We present in the following the results of a set of reviewed indices or metrics, for colour rendition applied on

a collection of 122 spectral power densities (SPD) of classical and LED-based lightings.

The metrics have been implemented in a C++ program exactly as the authors recommended to do it with the

original data (test colour samples/TCS and parameters) or referenced data (Munsell Atlas, GretaMacbeth

colour checkers). Some of them have been compared to their original implementation – mainly Excel ®

spreadsheets – whenever they are provided by the CIE or directly by the authors.

10.2 Dataset of SPDs

The collection has been established from the SPDs of author’s Excel ® spreadsheet, CIE publications for

standard illuminants, and LNE’s measurements.

The collection can be broken down in the following main subsets:

7 SPDs of incandescent and halogen lamps, and planckian radiator with or without optical filter

49 SPDs of fluorescent tubes and compact lamps, including some CIE standards (Fn, F3.n)

5 SPDs of miscellaneous lamps (HMI, Mercury arc, Xenon arc)

9 SPDs of HPS lamps, including some CIE standards (HPn)

52 SPDs of 3 subsets of LED lamp types: phosphors converted (PC), phosphors converted with

NUV excitation (NUV), and LED clusters.

The SPDs are tabulated, charted sorted and identified in the Excel file spd_eng05_v2.xls. The SPDs have

been sorted to easily see the result per type. Similar SPDs in each main subset have been removed; a more

accurate selection using correlation analysis will be performed later.

The SPD which are not belonging to LNE’s measurements are copied from CQS, MCRI, CAM-UCS CRI,

RCRI Excel ® spreadsheet and CIE disks.

At the end a global correlation between metrics is computed doubling the number of SPD of the less

populated subsets to better balance the main types:

LED based 52

Fluorescent : 49

Other : 33

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10.3 Graphs of selected SPDs

Incandescant/Halogen spectra (7)

0,0000

0,2000

0,4000

0,6000

0,8000

1,0000

1,2000

380 430 480 530 580 630 680 730 780

Wavelenght (nm)

Sp

ectr

al

den

sit

y

HMI/Hg/Xe spectra (5)

0,0000

0,2000

0,4000

0,6000

0,8000

1,0000

1,2000

380 430 480 530 580 630 680 730 780

Wavelenght (nm)

Sp

ectr

al

den

sit

y

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HPS spectra (9)

0,0000

0,2000

0,4000

0,6000

0,8000

1,0000

1,2000

380 430 480 530 580 630 680 730 780

Wavelenght (nm)

Sp

ectr

al

den

sit

y

LED clusters & phosphor converted spectra (43)

0,00000

0,20000

0,40000

0,60000

0,80000

1,00000

1,20000

380 430 480 530 580 630 680 730 780

Wavelenght (nm)

Sp

ectr

al

den

sit

y

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11 GLOBAL COMPARISON BETWEEN COMPUTED METRICS

The results of the general index of computed metrics are plotted against the number of the targeted SPDs.

The computation is done with the original implantation for the dataset of 122 SPDs.

11.1 Comparison between CIE CRI Ra 13.3 and CIE Ra96

SPD categories

0 7 14 21 28 35 42 49 56 63 70 77 84 91 98 105 112 119

SPD N°

QTH/incandescent Fluorescent HMI/Hg/Xe HPS LED clusters LED PC LED PC NUV

Comparison CIE Ra 13.3 / CIE R96a

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CIE R96a

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11.2 Comparison between CIE CRI Ra 13.3 and CQS 7.5 Qa

11.3 Comparison between CIE CRI Ra 13.3 and CRI CAM02-UCS

Comparison CIE Ra 13.3 / CQS 7.5

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

Qa

Comparison CIE Ra 13.3 / CRI CAM02-UCS

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CRI-CAMUCS

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11.4 Comparison between CIE CRI Ra 13.3 and RCRI

11.5 Comparison between CIE CRI Ra 13.3 and MCRI

Comparison CIE Ra 13.3 / RCRI

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

RCRI

Comparison CIE Ra 13.3 / MCRI

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

MCRI

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11.6 Comparison between CIE CRI Ra 13.3 and CFI

11.7 Comparison between CIE CRI Ra 13.3 and CCRI

Comparison CIE Ra 13.3 / CFI

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CFI

Comparison CIE Ra 13.3 / CCRI

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CCRI

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11.8 Comparison between CIE CRI Ra 13.3 and CRI00

11.9 Comparison between CIE CRI Ra 13.3 and HDI/Hhr (CH for 2D)

Comparison CIE Ra 13.3 / HDI (modified) 2D |ref-test|

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

HDI - 2D |r-k|

Comparison CIE Ra 13.3 / CRI R00a

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CRI R00a

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11.10 Comparison between CIE CRI Ra 13.3 and GAI

11.11 Comparison between CIE CRI Ra 13.3 and GAI

Comparison CIE Ra 13.3 / GAI

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

GAI

Comparison CIE Ra 13.3 / GAS

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

GAS

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11.12 Comparison between CIE CRI Ra 13.3 and FCI

11.13 Comparison between CIE CRI Ra 13.3 and CFI modified – CAM02-UCS

Comparison CIE Ra 13.3 / FCI

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

FCI

Comparison CIE Ra 13.3 / CFI - CAM02-UCS

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CFI

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11.14 Comparison between CIE CRI R96a, CRI R00a and CRI CAM02-UCS

11.15 Comparison between proposal of indices/metrics and combined indices/metrics

Comparison CIE R96a / R00a / CRI CAM02-UCS

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE R96a

CRI R00a

CRI-CAMUCS

Comparison of metric proposals

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

GAI+CRI

CQS + GAS

CRI-

CAMUCS

RCRI

MCRI

2/3

HDI+1/3CRI

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12 PEARSON CORRELATION BETWEEN COMPUTED METRICS

Table 12 : Correlation between metrics for all SPDs

Table 13 : Correlation between metrics for fluorescent SPDs

Metric/Metric CIE CIE CQS CRI RCRI MCRI CFI CCRI HDI CFI GAI GAS FCI 2*HDI

All SPD (134) Ra Ra96 Qg CAM xy CAM +Ra +Qg +Ra +Ra

CIE Ra 13.3 0,976 0,953 0,983 0,909 0,748 0,836 0,948 0,751 0,832 0,795 0,780 0,723 0,961

CIE R96a 0,966 0,983 0,916 0,769 0,849 0,942 0,734 0,852 0,769 0,787 0,719 0,939

CQS Qa 0,978 0,922 0,839 0,853 0,917 0,584 0,860 0,839 0,899 0,825 0,861

CRI-CAMUCS 0,944 0,815 0,844 0,945 0,667 0,857 0,803 0,828 0,769 0,915

RCRI 0,825 0,804 0,885 0,572 0,817 0,728 0,783 0,728 0,827

MCRI 0,565 0,709 0,244 0,567 0,752 0,899 0,884 0,587

CFI - xy 0,811 0,615 0,981 0,614 0,660 0,597 0,798

CCRI 0,721 0,835 0,838 0,725 0,599 0,915

HDI - 2D |r-k| 0,593 0,424 0,276 0,209 0,904

CFI-CAM-UCS 0,641 0,655 0,572 0,787

GAI+CRI Ra 0,845 0,694 0,692

GAS + CQS Qg 0,954 0,620

FCI+CRI Ra 0,556

2*HDI+CRI Ra

Average 0,861 0,861 0,869 0,871 0,820 0,708 0,756 0,830 0,561 0,758 0,726 0,686 0,679 0,797


FLuorescent Ra Ra96 Qg CAM xy CAM +Ra +Qg +Ra +Ra

CIE Ra 13.3 0,99 0,99 0,99 0,95 0,97 0,91 0,96 0,82 0,91 0,90 0,99 0,93 0,98

CIE Ra96 1,00 0,99 0,96 0,95 0,92 0,97 0,79 0,94 0,90 0,98 0,90 0,96

CQS Qa 0,99 0,96 0,94 0,92 0,98 0,78 0,95 0,91 0,98 0,89 0,96

CRI-CAMUCS 0,96 0,95 0,90 0,98 0,77 0,94 0,91 0,97 0,88 0,96

RCRI 0,90 0,91 0,94 0,77 0,94 0,85 0,94 0,87 0,93

MCRI 0,81 0,93 0,81 0,82 0,87 0,96 0,93 0,96

CFI - xy 0,87 0,79 0,96 0,77 0,91 0,86 0,90

CCRI 0,74 0,92 0,94 0,95 0,83 0,93

HDI - 2D |r-k| 0,73 0,69 0,88 0,93 0,92

CFI-CAM-UCS 0,83 0,90 0,80 0,89

GAI+CRI Ra 0,89 0,74 0,87

GAS + CQS Qg 0,96 0,99

FCI+CRI Ra 0,97

2*HDI+CRI Ra

Average 0,95 0,94 0,94 0,94 0,91 0,91 0,88 0,92 0,80 0,89 0,85 0,87 0,88 0,94

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Table 14 : Correlation between metrics for LED Cluster SPDs


LED clusters Ra Ra96 Qg CAM xy CAM +Ra +Qg +Ra +Ra

CIE Ra 13.3 0,98 0,88 0,99 0,85 0,31 0,85 0,94 0,95 0,83 0,44 0,06 0,07 0,99

CIE Ra96 0,91 0,97 0,84 0,30 0,83 0,93 0,93 0,82 0,39 0,09 0,08 0,96

CQS Qa 0,92 0,80 0,58 0,79 0,80 0,72 0,78 0,61 0,48 0,40 0,81

CRI-CAMUCS 0,89 0,40 0,84 0,93 0,91 0,84 0,49 0,17 0,15 0,96

RCRI 0,51 0,73 0,85 0,77 0,73 0,50 0,22 0,12 0,82

MCRI 0,19 0,18 0,04 0,22 0,79 0,81 0,70 0,17

CFI - xy 0,90 0,80 0,99 0,50 0,10 -0,01 0,83

CCRI 0,95 0,89 0,42 -0,06 -0,16 0,96

HDI - 2D |r-k| 0,77 0,22 -0,21 -0,19 0,99

CFI-CAM-UCS 0,54 0,13 -0,02 0,81

GAI+CRI Ra 0,64 0,41 0,33

GAS + CQS Qg 0,87 -0,08

FCI+CRI Ra -0,06

2*HDI+CRI Ra

Average 0,70 0,70 0,73 0,73 0,66 0,40 0,64 0,66 0,59 0,64 0,48 0,22 0,18 0,65

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13 PARAMETER/METHOD CHANGE EFFECT ON REFERENCE BASED METRICS

13.1 Effect on TCS on CIE CRI Ra 13.3

Comparison CIE Ra 13.3 / Ra & Macbeth 99

-20

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

CIE Ra &

Macbeth 99

Comparison CIE Ra 13.3 / Ra Munsell Matte 1269

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CIE Ra 13.3

Ra & Munsell

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13.2 Effect of reference method determination on CRI Ra 13.3

Effect of reference illuminant determination

on CIE R96a using CIE coefficients

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

Ill (a,b)

CCT u,v

Effect of reference illuminant determination

on CIE R96a using the same coefficients

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

Ill (a,b)

CCT (u,v)

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13.3 Effect of CAT method on CRV of CQS with adaptation factor D=1

a

b

Colour rendition vectors

CQS - CFL 2700K

-60 -40 -20 0 20 40 60

-60

-40

-20

0

20

40

60

80

100

CMCCAT2000 Qa 75.7

CIECAT94 Qa 75.0

CMCCAT97 Qa 75.5

CAT02 Qa 75.6

13.4 Effect of index formulae change on CCRI

Comparison of updated CCRI metric

ratio (Si St)/St versus (Si St)/Si

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

CCRI %

ref

CCRI

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14 DETAILED IMPLEMENTATION OF COMPUTED METRICS

14.1 Use of Munsell Atlas 1269 matte colour chips for CCRI categories

a

b

Categories /Yaguchi criterion

for CCRI computation

- 90 - 60 - 30 0 30 60 90

- 100

- 70

- 40

- 10

20

50

80 category n° 0

category n° 1

category n° 2

category n° 3

category n° 4

category n° 5

category n° 6

category n° 7

category n° 8

category n° 9

category n° 10

category n° 11

category n° 12

category n° 13

category n° 14

category n° 15

category n° 16

category n° 17

category n° 18

category n° 19

category n° 20

14.2 Comparison of original and updated method for statistical index

Comparison of updated statistical methods

Colour Fidelity Index

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100 110 120

SPD N°

Value

In (x,y) with

MA ellispes

In Cam02-

UCS space

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14.3 Best fit of HDI/Rhr computation using dichromatic pairs of colours

15 DETAILED RESULTS OF COMPUTED METRICS

15.1 Colour rendition vectors (CRV) maps

UCS axe1

UCS axe2

CRV RGB 5000K

Index:CQS Qa=77.1

-100 -80 -60 -40 -20 0 20 40 60 80 100

-60

-40

-20

0

20

40

60

80

100

CRV

y = 1,0811x - 4,5895

R2 = 0,9316

50

70

90

110

130

150

50 60 70 80 90 100 110 120 130 140 150

Rhr computed from 2D pairs

Au

tho

rs R

hr

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UCS axe1

UCS axe2

CRV RGBY 2700K

CQS Qa=85.3

-100 -80 -60 -40 -20 0 20 40 60 80 100

-60

-40

-20

0

20

40

60

80

100

CRV

15.2 Special indices charts

Comparison of special indices - Macbeth chart

LED CW 5000K

-40

-20

0

20

40

60

80

100

120

0 20 40 60 80 100

TCS N°

ind

ex v

alu

e

CRI Ra 13,3

CRI CAM02-UCS

CRI R96a

16 SELECTION OF LIGHT SOURCES FOR THE SUBJECTIVE EXPERIMENT

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16.1 Lamps specifications

Lamps selection for the subjective experiment

0

0,2

0,4

0,6

0,8

1

1,2

380 430 480 530 580 630 680 730 780

Wavelenght (nm)

Spectral density

FL 5000K

LED NUV 5000K

LED CW 2700K

LED RGB 5000K

LED WR 2700K

CFL 2700K

LED RGBY 2700K

HAL 2700K

LED WW 2700K

16.2 Preliminary results for colour rendition metrics

Prediction for subjective test

0

20

40

60

80

100

120

FL

5000K

LED

NUV

5000K

LED CW

5000K

LED

RGB

5000K

LED WR

2700K

CFL

2700K

RGBY

2700K

HAL

2700K

LED WW

2700

CIE Ra

CQS

MCRI

CRICAM-UCS

RCRI

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17 PRELIMINARY STATEMENTS AND CONCLUSION

17.1 Metrics selection and implementation

The selection of metrics took into account the CIE TC1-69 recommendations for relevant metrics to

complement and supplement the current index for colour rendition, some metric/indices had been added like

update of the current CRI (R00a) and statistical methods (CFI/CSI/HDI).

The selected metrics have been implemented in a C++ program for extensive application using a large

number of SPD and TCS and for further modifications. Most of them had been checked against the published

results either with reference SDPs, or either compared to existing implementation under excel ®

spreadsheets. Despite of very detailed equations and formulae, on papers and Phd dissertation online, the

computation of Harmony Rendering Index HRI was found different from the authors. The authors have been

contacted but we do not yet receive any answer. With few modifications a HRI yielding to similar results in

magnitude and correlating at a level of 93% over about 80 SPD has been used for this study and we expect,

with the help of the authors to correct the situation before the end of the study or we should give up with this

metric.

Comparative results

The following statements are preliminary statements, which are suggested from the computation of 13

metrics/indices, with and without parameters or component changes, applied over the selected 122 SPDs. A

part of the computation performed is presented, and more computation on metrics is planned for the study of

colour rendition.

Global comparison of metrics

The comparison of results of metric proposals against the sorted SPD can be visualized on the figure of the

paragraph 12.15, which exhibits large differences in magnitude. These proposals are for some of them

combined metrics of fidelity indices with indices measuring other quality dimension. The preferred indices to

supplement the fidelity index are based on a gamut area. These gamut indices introduce a great change for

the LED clusters (table 15 of section 13) due to their high saturation properties and then present a radical

change in measuring the quality colour rendition. The correlation tables (tables 14,15,16 of section 13)

clearly shows that the introduction of LED, particularly LED clusters, gives large difference between

indices/metrics, between current and proposals, and between proposals that were indented to improve the

quality metric for colour rendition of LED based lighting. Taking into account the overall low correlation

between indices/metrics, the difference in approach we can conclude that we are far to find a consensus on

proposals for complementing, supplementing or replacing the actual colour rendering metric.

The updated methods and components of the current CRI of metric proposals as CIE CRI96, CRI-CAMUCS,

CRI00, show some differences and improvements (eliminating negative special indices- see 16.2) that could

result in better ranking of LED light sources but still stay very similar to the actual CRI. For FL sources

correlation is 99% and for LED clusters correlation is superior to 97%. Due to the high correlation with

current CRI and because they are fidelity index these updated metrics probably should not fix nor address

some issues with visual LED sources ranking involving other quality dimensions of colour appearance, from

the customer acceptance side, as for instance the level of colourfulness, the colour discrimination, the level

colour contrast, and other quality attribute of colour appearance like harmony, naturalness.

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The CQS Qa updates and improves the current CRI method on many points, and also discounts the positive

chroma shift, due to a higher chroma than the reference source, in the colour shift computation. The metric

reflects that the increase of chroma/saturation has a positive effect on observer preference, and then is no

longer a pure fidelity metric. The metric, while not changing too much the classical lighting scores, increases

the scores of LED PC and LED clusters, and more considerably the very low scores of those technologies.

The MCRI yields to the lowest correlation with other indices/metrics and exhibits highest quality index

values for LED clusters and a similar lowering of index values for all other lighting types (FL, QTH, NUV)

having a high CRI Ra. We wonder if this systematic behaviour should be verified for all cases.

The statistical method of Zukauskas et al. (see 6.2) is a 4D metrics (CCT, CFI, CSI ,HDI) but a single index

is not derived, the method is interesting to describe the basic aspects of the colour rendition. The

computation of CFI (fidelity) in the 1931 xy space with the MacAdam’s ellipses gives similar results than the

CFI computed with CAM02-UCS space and using the CRI-CAMUCS colour shift formula (see graphs 11.6

and 11.13). The computation of CSI and HDI with CMA02-UCS give different results because the original

method exploits vectors in the space 1931 xy is not uniform (results not presented). Further work will be

done to derive a quality metric from theses 4 parameter/indices using the CAM02-UCS model.

It appears that the increase of saturation generated by most of LED lighting sources having broad radiation

peaks in the visible spectrum becomes a key issues in the development of a new metric. Most a the new

metrics proposals claims to account for customer’s preference but leading to low fidelity indices. But most of

these new metrics presents low correlation between them for LED lightings and some very different metrics

present a significant correlation like the MCRI and “CQS Qa+ GAS” (Pearson coefficient=0,81).

Parameter/method change effects on metrics

It can be seen from the paragraph 14.1 that the choice and number of TCS can significantly change the

colour fidelity index to a higher or lower value, some different ranking could be obtained for fidelity metrics

(graph not presented). More investigation must be done to estimate the change in ranking the light sources

accounting for TCS characteristics and to set up a more robust set of TCS considering also the mesmerism

and the colour constancy of these TCS as developed in 9.2.

The way to determine the reference source, from CCT or distance to fixed illuminants appears not to change

the results of colour quality metric.

The different implemented CATs yield to very close results. Chromatic adaptation is a key stage, which is

directly linked to the colour shifts magnitude between the adapted source and the reference source. Most of

the metrics use an adaptation factor D equal to 1. It has been found that the adaptation factor can have a little

effect on indices values, so more research (literature) will be done on that point

17.2 Selection of light sources for the subjective experiment

The selected light sources cover all technologies: FL, CFL, PC LED, NUV PC LED, and LED clusters (tri-

chromatic and quadri-chromatic).

The measured SPDs of the selected lamps for the subjective experiment provide us with sufficient variation

in indices values (see paragraph 17.2) and difference in ranking to expect a valuable subjective experiment.

The nine adopted lighting sources can also be grouped into two clusters of CCT, 2700K and 5000K, and then

be ranked separately if the CCT of light source account for observer’s judgment.

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17.3 Conclusion

A large review of colour rendering metric has been conducted and the metrics have been sorted and detailed

for a further analysis and a computer implementation. The relevant metrics, including those judged worthy of

consideration by the CIE, have been successfully implemented, except the HRI, in a modular C++, which

enable us to change the parameters, methods and data. These implemented metrics had been applied to a set

of 122 selected TCS to visualize the differences between metric results as well as some parameter/method

change effects. Correlations between metrics have been computed for the 122 SPDs or subsets of SPDs : the

lowest correlation between metrics, current and proposals, have been found for the LED clusters. The

selected results are presented with charts and tables. With the detailed review, showing the difference in

approaches and the difference of results illustrated by comparison charts and correlation tables, we can

notice that we are apparently far to find a consensus for a colour rendering metric to better rank the new light

sources, especially LED light sources. This preliminary study helps us in designing the future subjective

experiment, in selecting test light sources. The completed program will enable us to quickly analyse,

validate, alter and tune the metrics to obtain better correlation with the subjective scoring that we will get

with our future subjective experiment.

report on detailed data of investigated colour quality indices version 1€¦ · report on detailed...

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