Report on quality metrics related to colour quality

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Title: Report on quality metrics related to colour quality - Deliverable D435
Author(s): Renoux, D.
Year: 2013,
Funding programme: EMRP A169: Call 2009 Energy
Project title: ENG05: Lighting: Metrology for Solid State Lighting
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Report on quality metrics related to
colour quality
EMRP-ENG05-4.3.5
Version 1.1
D. Renoux
Laboratoire National de Métrologie et d’Essais, France
A report of the EMRP Joint Research Project
Metrology for Solid State Lightning
www.m4ssl.npl.co.uk
EMRP-ENG05-4.3.5
Version 1.1
Title
:
Report on quality metrics related to colour quality
Reference
Version
:
:
EMRP-ENG05-4.3.5
1.1
Date
:
02-April-2013
Dissemination level
:
PUBLIC
Author(s)
:
D. Renoux
Laboratoire National de Métrologie et d’Essais, France
Keywords
:
Quality metrics, colour specification, colour rendering, SSL, LED,
Abstract
:
This is a report on quality metrics related to colour quality of artificial white
light sources for general lighting. Background information is given on
colorimetric parameters used for colour specifications. Then a summary a
of the performed review on colour rendering metric ( D311) is presented:
principles, approaches and results of implemented relevant metrics are
sorted and exposed. The performed subjective experiment of this project
and its main results (D312) are described and commented. The last section
concludes on quality metrics for colour quality with information on new
recommendations/practices, labs and CIE activity, and finally highlights
outcomes of the work done for this project.
Contact
:
http://www.m4ssl.npl.co.uk/contact
About the EMRP
The European Metrology Research Programme (EMRP) is a metrology-focused European programme of coordinated R&D that
facilitates closer integration of national research programmes. The EMRP is jointly supported by the European Commission and the
participating countries within the European Association of National Metrology Institutes (EURAMET e.V.). The EMRP will ensure
collaboration between National Measurement Institutes, reducing duplication and increasing impact. The overall goal of the EMRP is to
accelerate innovation and competitiveness in Europe whils t continuing to provide essential support to underpin the quality of our lives.
See http://www.emrponline.eu for more information.
About the EMRP JRP “Metrology for Solid State Lighting”
In the EMRP Joint Research Project (JRP) “Metrology for Solid State Lighting”, the following partners cooperate to create a European
infrastructure for the traceable measurement of solid state lighting: VSL (Coordinator), Aalto, CMI, CSIC, EJPD, INRIM, IPQ, LNE,
MKEH, NPL, PTB, SMU, SP, Trescal, CCR, TU Ilmenau and Université Paul Sabatier. See http://www.m4ssl.npl.co.uk/ for more
information.
The research leading to these results has received funding from the European Union on the basis of
Decision No 912/2009/EC.
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SUMMARY
The report addresses the colour specification issues of artificial light sources for the purpose of general
lighting. The introduction gives the scope of our study: we consider the white lighting for interior applications
and we exclude specific applications. We recall the main steps from the introduction of incandescent lamps
till today lighting solutions with discharge (fluorescent) and LED-based lighting. We stress that for the
fluorescent technology the poor colour rendering slowed down the purchase of consumers and that the
official current index for colour rendering gives poor prediction of LED lighting. We need true metrics to
provide the industry and the consumer with better quality indices and thus to accelerate the deployment of
the very efficient LED technology. The first part of the report presents the background information and the
work done for this project. To introduce colour specification of a white light sources a short insight of relevant
colorimetry is given. We list the different chromatic diagrams and uniform coordinates spaces and colour
appearance models. Those diagrams and spaces are the bases to understand the main colour parameter
specification : the Colour Correlated Temperature (CCT), the CCT names, the chromaticity coordinates (x,y),
the metamerism index MI, and finally the colorimetric metrics underlying the complex calculation of colour
rendering indices. Then the results of the conducted review on colour rendering indices/metrics are
summarized. The study sorts out the numerous approaches and methods of colour rendering demonstrating
the multidimensional aspect of colour rendering. The recent activity and work of the CIE TC1-69, in charge of
colour rendering, is reported with the list of promising candidates for new colour rendering metrics. The
prediction results of implemented relevant metrics over a large set of SPDs sorted by types is exposed and
commented. The assessment of metrics by field trial is shortly described, subjective and objective results are
summed up in a table, as well as correlation coefficients. The metrics giving the best prediction for the
conducted subjective are plotted as radar chart for the different subsets of sources types to show the
improvement on the current CIE CRI. The second part of the report focuses on quality metrics related to
colour rendering with the help of background information given in part one. We start from a definition of a
quality metric and stress that the core of the quality metric represented by the colour rendering could not
provide absolute information about the colour property of the light source but an information relative to the
corresponding reference illuminant. The activity to achieve a new metrics by laboratories and CIE is given in
few lines. Recommended specification or practices applying to colour rendering metric of SSL are exposed.
We close that part with a synthesis of the findings of this study that we will address as recommendation to
the CIE.
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TABLE OF CONTENTS
1.
INTRODUCTION ......................................................................................................................3
2.
COLOUR SPECIFICATIONS OF A WHITE LIGHT SOURCE ............................................4
2.1.
Chromatic diagrams and colour spaces ........................................................................................4
2.2.
Correlated Colour Temperature (CCT) and chromaticity.............................................................5
2.3.
Colour Metamerism Index ...........................................................................................................7
2.4.
Colour Rendering Indices and metrics .........................................................................................7
2.4.1.
Introduction .......................................................................................................7
2.4.2.
Review of colour rendering indices/metrics ........................................................7
2.4.3.
Activity and work of the TC 1-69 of CIE ...........................................................10
2.4.4.
Results of implementation of relevant metrics ...................................................11
2.4.5.
Metrics assessment by field trials .....................................................................15
3.
QUALITY METRICS FOR COLOUR QUALITY..................................................................19
3.1.
Quality metric specification.......................................................................................................19
3.2.
Synthesis and update on colour quality metrics for lightings ......................................................20
3.2.1.
Common specification of colour property of white lighting :.............................20
3.2.2.
Activity for new colour rendering metrics.........................................................20
3.3.3.
Recommended specification for colour rendering from the ASSIST program.....20
3.3.4.
Summary of the work achieved for this task ......................................................21
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LIST OF FIGURES
Figure 1 : The 1931 CIE chromatic space (x,y) with the Planckian locus and the isotemperature lines ......... 6
Figure 2 : SPD colour, number and type of light source ..............................................................................12
Figure 3 : Comparison of CRI 13.3 Ra and CQS Qa 7.5 .............................................................................12
Figure 4: Comparison of CRI 13.3 Ra and CRI-CAM02UCS ....................................................................13
Figure 5: Comparison of CRI 13.3 Ra and MCRI ......................................................................................13
Figure 6: Comparison of metric proposals ...................................................................................................14
Figure 7 : Graph of the LED SPD for the subjective experiment .................................................................15
Figure 8 : Views of the subjective room for the colour rendition experiment ...............................................15
Figure 9 : Radar charts of 6 index/metric proposals .....................................................................................18
LIST OF TABLES
Table 1 : Nominal CCT names for white tint with CCT ranges and illuminants ............................................ 5
Table 2: Fidelity and preference attributes.................................................................................................... 9
Table 3 : Metric’s predictions and average subjective scores .......................................................................16
Table 4 : Pearson correlation coefficients ....................................................................................................17
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
SSL
Solid State Lighting
GLS
General Lighting Services
CFL
Compact Fluorescent lamps
QTH
Quartz Tungsten Halogen
LED
Light Emitting diode
OLED
Organic Light Emitting diode
LED-PC
LED Phosphor-Converted
CIE
Commission Internationnale de l’Eclairage
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
ordinal scale based Colour Rendering Index
FCI
Feeling of Contrast Colour Rendering Index
HRI
Harmony Rendering Index
MCRI
Memory Colour Rendering Index
GAI
Gamut Area Index
FDI
Fidelity Distortion Index
HDI
Hue Distortion Index
CSI
Colour Saturation Index
SPD
Spectral Power Density
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1. Introduction
This report addresses the colour quality specification issues of artificial light sources, for the purpose of
General Lighting Services. We will consider the colour quality of white light sources mainly for interior lighting
applications such as office lighting, household lighting and public space lighting. We will exclude from this
investigation specific interior lighting like medical lighting, scenic lighting or any other lighting applications
having specific constraint or dedicated to produce special visual or aesthetic effect.
th
Artificial lighting began at the end of the 19 century with incandescent bulbs – luminous efficacy around 12
lumen/watt – and a good deal of incandescent lamps, gradually banned from the market, are still in use in
offices and in homes. The QTH lamps, similar in technology – and a bit more efficient, luminous efficacy
th
around 18 lumen/watt - started to be commercialized in the early 20 to naturally and gradually replace the
incandescent lamps.
A breakthrough in efficiency arrived with fluorescent tubes – efficiency around 80 lumen/watt – introduced in
the mid 20th century generating light in a very different way than the formers and also, that is our main
concern, with very different optical characteristics. Due to having a base incompatible with household
luminaries, these lamps were only used in office and public spaces. Yet this changed with the recent
introduction of CFL (Compact Fluorescent lamps) featured with compatible bases and which generate light
with similar optical characteristics than fluorescent tubes.
The second revolution came up with LED (Light Emitting Diode). The LED technology, well known for a long
time as signalling devices and low-level flux coloured light emitters, quickly and sharply gained in optical
white luminous flux with the help of recent important research and development. Today these lamps, with
efficiency up to 100 lumen/watt and beyond – outperform fluorescent technologies and are starting to be
broadly deployed on the market. Once again the way to produce the light is different from the previous, as
well as the optical characteristics.
We all recall the visual impact of poor colour rendering from the early fluorescent lighting, like those have
been installed and still installed in many pedestrian tunnels. This poor colour rendering slowed consumers
purchase, and we saw the same phenomenon with the first LED lamps.
On other hand, many subjective experiments performed the last decade with LED-based lightings only or
with traditional lightings showed that the current colour rendering general index Ra, of the CIE CRI 13.3, was
unable to predict the obtained subjective ranking and also provide worse prediction for LED-based lighting in
comparison to traditional lighting.
Today we need true quality metrics for colour appearance of the traditional (QTH, CFL, FL) and new lighting
sources (LED, OLED) to provide the industry as well as the consumers and organisations with better quality
criteria. The goal is to promote better LED lighting, and thus accelerate its adoption with the benefit of
significantly reducing global energy consumption.
The way to specify colour appearance of a light source, with background information, will be presented and
commented in this report. Colour rendering metrics, as a major component of colour quality metrics, will be
briefly reviewed, see deliverable D311 for detailed information, and analysed through the work done for their
assessment and drafted in the deliverable D312.
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2.
Colour specifications of a white light source
Prior to introduce colorimetric parameters and specification we first recall some key principles and bases of
colorimetry. Then we will go trough the relevant CIE colorimetric parameters to characterising white light
sources. The last sections will develop the indices and metrics related to the colour rendering quality and
investigated in the project, covering the current CIE index and new proposals of colour rendering indices or
metrics. The colorimetric parameters and colour spaces presented in the following sub-sections are defined
in several CIE in publications [1][2][3][4][5][6] and used in many standards.
Colorimetric parameters are derived either directly from the emitted spectrum of the light source or from their
interaction with objects through their reflectance spectra.
2.1.
Chromatic diagrams and colour spaces
CIE 1931 (x,y) chromatic diagram
The human vision, under well-lit condition, can be seen as the combination of three photoreceptors
stimulated by light : the LMS cones receptors covering respectively a Short, Medium and Long wavelength
range in the visible spectrum. CIE defined two sets of colour-matching functions (CMFs), the second set (
(?), (?), (?)) ,having only positive values, was adopted for convenient application. The tristimulus values
(X,Y,Z) of a stimulus are then obtained from the colour stimulus function P(?) of the light source and the
CMFs using the formula [f1], with k a normalizing constant to match absolute photometric quantity on Y.
X = K ? P(? ). (? )d? ; Y = K ? P(? ).
(? )d? ; Z = K ? P(? ).
(? )d? ;
[f1]
The 1931 chromaticity coordinates (x,y) are calculated from the the tristimulus values (X,Y,Z) using the
formula [f2] :
x= X/( X+Y+Z) ; y=Y/(X+Y+Z);
[f2]
After the definition of the CIE 1931 (x,y) chromatic diagram, related to the 1931 standard colorimetric
observer, the CIE defined successively several uniform chromatic diagrams and uniform colour spaces with
associated colour difference formula. All the coordinates of the new spaces can be calculated from the
tristimulus value (X,Y,Z) of the stimulus, the list is given hereafter :
CIE two-dimensional UCS chromatic diagram and extension to three-dimensional uniform colour space
§
the two-dimensional CIE 1960 (u,v) UCS diagram (obsolete – but in use for CCT computation)
§
the three-dimensional CIE 1964 uniform coordinate space (W*,U*,V*) – (obsolete for colorimetric
specification, but still in use for the CRI calculation – see section 2.4).
§
the two-dimensional CIE 1976 UCS diagram (u’,v’) (in use [7])
§
the three-dimensional CIELUV or CIE 1976 (L*,u*,v*) uniform coordinate space (in use)
§
the three-dimensional CIELAB or CIE 1976 (L*,a*,b*) uniform coordinate space (most popular)
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CIE Colour Appearance Model (CIECAM)
§
CIECAM97s [4]
§
CIECAM02 [5]
§
CAM02-UCS [11]
The CAM models are used to predict colour appearance attributes for distinct viewing conditions, the inputs
of CIECAM are the tristimulus values of the stimulus (X,Y,Z) , the tristimulus values of an adapting white
point, the adapting background, and the surround luminance information. More complex visual phenomena
are embeded in these models like chromatic adaptation transform (CAT), parametric degree of adaptation,
and non-linear cones responses.
Li et al derived an uniform space, called CAM02-UCS, from the CIECAM02 and proposed an updated CIE
colour rendition index (CAM02UCS-CRI) whose principle is the same than the CIE CRI 13.3 but
implemented in this new UCS.
2.2.
Correlated Colour Temperature (CCT) and chromaticity
The CCT is one of the most popular and used parameter to specify a light source, natural or artificial like of
daylight phases and light emitted from bodies elevated at very high temperature, or any white artificial light
sources The colour temperature of a light source is defined by the temperature of the closest Planckian
radiator in the (u,v) chromatic space.
Note : u=u’ and v= 2v’/3 with (u, v’) the coordinates of the CIE 1976 UCS.
Today the common ways to communicate to consumer quantitative or qualitative information about the
spectral content of light sources are nominal CCT names and CCT in Kelvin. Nominal CCT names were
introduced in the ANSI standard C78.376 – “Specification for the chromaticity of fluorescent lamps” [8]. In the
following table 1 some examples are given with CCT ranges in Kelvin and well known illuminants and CIE
reference illuminants, notice that there is no agreement on CCT ranges.
Nominal CCT
names
CCT ranges
Illuminant examples
CIE reference illuminants
Warm white
< 3000 K
Incandescent (2600- 2900 K)
A
QTH (2700 – 3000 K)
‘Neutral’ white
[3000-4000 K]
Discharge lamps
Cool white,
Daylight
> 4000 K
Discharge lamps, direct and indirect
daylight, cloudy daylight (8000 K),
xenon/mercury arcs
6500 K
B (4874 K) , C (6774 K), E,
D50, D55, D65, D75
Table 1 : Nominal CCT names for white tint with CCT ranges and illuminants
It can be seen from the figure 1 that the CCT can relatively well express the balance between the reddish or
bluish tint of the light source, the Planckian locus following roughly the blue-red axis, but does not enable to
express the greenish or magenta shade of the light source for a given CCT.
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A more accurate information is given by chromaticity coordinates, (x,y) or (u’,v’) but generally reserved for
professional or industrial specifications like LED chromaticity binning for lamps or luminaires manufacturers
and are not meaningful characteristics for consumers, buyers, retailers.
We later will see that colour rendering indices and other developed indices for colour rendering or colour
quality could rely upon only on the difference with a reference illuminant and therefore indices could not
reflect important information about colour rendering or property of the light source. For instance a daylight
simulator and an incandescent lamp could obtain high and equal CIE CRI but we all know that they have
very different colour rendering or property.
Figure 1 : The 1931 CIE chromatic space (x,y) with the Planckian locus and the isotemperature lines
(reprinted from wikipedia.org)
With no better meaningful parameter to reflect the spectral content of a light source, we must keep the CCT
specification as a colour quality metric parameter and colour specification for common users.
The 1931 chromaticity coordinates (x,y) ou (u’,v’) are used for colour specification in standard requirements
and for professional usage along the lighting chain. One other important specification is the distance Duv , in
the CIE 1976 chromatic diagram (u,v), of the test light source from the Planckian locus. This distance can be
expressed with a signed value to indicate if the light source is above (+) or below (-) the Planckian locus.
The ANSI_NEMA_ANSLG C78.377 “Specification for the chromaticity of Solid-State-Lighting Products” was
the first published standard specifying range of chromaticities for SSL. Eight CCT and Duv targets are
defined with tolerance. Background information is given with the equivalent tolerance quadrangle in (x,y) or
(u’,v’) space. These quadrangles are approximately 7-MacAdams ellipse [10]. One MacAdams ellipse
corresponding to the perceptual threshold of colour difference. The specification of colour rendering is
based on the general index Ra of CIE CRI 13.3 Ra.
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2.3.
Colour Metamerism Index
Metamerism phenomenon is the matching of apparent colour, under a given illuminants, of objects having
different spectral power distributions, such objects are named metamers with respect to that illuminant. The
CIE special colour metamerism index is defined in publication 15.2 [1] and its supplement [2]. It is calculated
from the colour-difference index ?E of the two metamers under a specified reference illuminant .
The CIE Metamerism Index (MI) of a light source is derived by calculating the mean colour distance of 5
metamers in the visible spectrum and in 3 the ultraviolet range, computed either in the CIELAB or CIELUV
colour space.
The metamerism index is generally used to qualify daylight simulators. But this property to preserve
mesmerism has been many times quoted as a relevant property of colour rendering quality but is rarely used
to specify light sources for GLS.
2.4.
Colour Rendering Indices and metrics
2.4.1. Introduction
The study and assessment of colour rendering indices and metrics, current and proposals, were the major
concern of the research achieved to address the perception of colour quality of light sources : (1) a large
review and implementation of proposals of colour rendering indices/metrics has been performed (D311) and
(2) a real-life colour experiment was conducted with 43 individuals subjected to 9 lights sources (D312). We
present in this report the synthesis of the outputs of these deliverables, details and complete references can
be found in the related reports.
2.4.2. Review of colour rendering indices/metrics
We sorted out the different approaches and implementations of the current index and proposals of new
index/metric for colour rendition. There is no universal definition of the colour quality of a light source but
several rendering properties of light source have been identified with a related metric proposal, the metric
quantifying the quality of the properties. These different properties are related to the different aspects of the
colour appearance of objects. The relevance of the properties could vary depending on the targeted
application : expectation of individuals are quite different with the environment, for instance a living room and
a office room.
We expose in the following subsections the main “trade-offs” and key points issued from the review of colour
rendering indices or metrics. These main trade-offs address almost all key points of a colour rendering
metric, revealing the dimension of colour rendering of light sources. We close this section with the activity of
the CIE TC1-69 for a new colour rendering metric.
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A- Fidelity versus preference
Colour properties roughly belong into two categories related to:
§
the ability to preserve the appearance of objects lighted by the assessed source,
§
the ability to enhance the appearance of objects lighted by the assessed source.
A common designation for the metrics belonging to the first category is “fidelity metric” and for the metrics
belonging to the second category is “preference metric”.
We can extend the definition of fidelity metric by a metric quantifying the preservation of a given set of colour
appearance attributes and likewise the definition of a preference metric is a metric quantifying the
enhancement of a given set of colour appearance attributes.
Colour appearance attributes for fidelity and preference
A table of some visual colour attributes is given below with an attempt to sort them under fidelity attribute or
preference attribute. There is no strict definition or consensus about this classification for all the attributes.
The table hereafter is similar to that one presented by F. Viennot during a TC CIE meeting [10].
We can be confuse about the meaning and the use of an attribute either for fidelity or preference
assessment. K. Smet reported in a paper that the subjectively rated naturalness attribute and the subjectively
rated fidelity attribute over a set of experiments, performed by different laboratories, were negatively
correlated, or with converse trends. Since the publication of the CRI CIE 13.3 in 1974 many papers outlined
that perceived naturalness is enhanced if the colour appearance of some common objects is more colourful.
(base of the flattery index). Then naturalness should not be a strictly a fidelity attribute, but if we define
naturalness as “the colour match with the internal representation we have of natural or familiar objects” we
should say that naturalness addresses fidelity. J. Schanda et Al reported that the harmony coefficient they
derived, supposedly maximum for the reference light, could be greater for the test light source than the
reference light and then the harmony index was expressed as an absolute difference of harmony
coefficients. We can wonder if colour harmony should be considered as a “preference” or “fidelity” attribute
since the test light source could have an enhanced harmony.
The basic fidelity metric is calculated from the colour-difference index ?E between the test light and the
reference light on a set of test colour samples. By extension a fidelity metric can rely on the absolute
difference of an attribute between the test light and the reference light on a set of colour samples, quantifying
the preservation of the attribute by the test light.
A preference metric can be calculated as a signed difference of attributes between the test light source and a
reference light source. But a preference metric can be calculated as a fidelity metric but discounting colour
shifts in the computation when the effect of these colour shift enhance appearance (gain of contrast, gain of
chroma, ect) as it is done for the Colour quality Scale general index CQS Qa.
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Fidelity
Preference
Preservation of visual attribute
Enhancement of visual attribute
colour consistency
vividness, colourfulness, visual clarity,
preservation of metamerism
attractiveness
colour discrimination
naturalness (?)
naturalness (?), memory colours (?),
colour harmony (?)
colour harmony (?)
memory colour (?)
colour categorisation
Table 2: Fidelity and preference attributes
B - Reference illuminants versus memory colour of objects
Both fidelity metric or preference metric can be implemented by comparison of the test light with reference
illuminants. Reference illuminants are defined by their spectrum.
Memory/Reference objects are defined by their reflectance spectrum and by their optimal coordinates in a
chromatic space or a by a distribution of a perceptual characteristic in a chromatic space. It is needed to
proceed to subjective experiments to establish these memory objects. The best examples are the
chromaticity-coordinates increments added to the reference illuminants of the “flattery index” and the
similarity functions of the “memory colour rendering index” (MCRI).
Note on reference illuminants :
Since the publication of CIE CRI 13.3 the referent illuminants are the CIE phases daylight (CCT >5000 K)
and Planckian/Blackbody radiator/emitter (CCT < 5000K). Daylight has always been considered as the ideal
colour rendering light source providing a large colour variety with a lot of shades and excellent discrimination
of all colours and surely contribute the more to built up our colour memory or internal representation of
natural objects. This is less true for the Planckian radiator, but since the advent of artificial light
incandescent/QTH lamps, whose spectra is very close to Planckian radiator, are the most familiar sources.
C - Continuous rating versus ranking
Peter Brodogi and Al develop an ordinal scale based colour rendering index (RCRI), to meet the user’s
expectation in term of specification to provide him with simple information about the colour quality. They
claim that customer is not able to state on colour quality equality from close indices and unable to fix
difference thresholds while comparing indices.
D – Statistical binary colour test versus representative colour test
Zukauskas et Al developed a statistical approach to qualify colour rendering, rather than working on a
limited set of test colour samples they developed an analysis on a 1269 Munsell Mat colour Atlas. The
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method compute the following indices with given tolerances, in the (L,x,y) space, applied on differences with
the reference light :
§
CFI: counts of TCS rendered with high fidelity,
§
CSI: counts of TCS rendered with an increased saturation,
§
HDI: counts of TCS rendered with an important hue distortion.
E- One component metric versus multi-component metric
New proposals combine several metrics/indices to take into account several aspects of colour rendering. A
proposal of a fidelity metric combined and a preference metric is given by S.Rea et AL who recommended to
use the CRI in conjunction with the GAI (Gamut Area Index).
Other indices were suggested to supplement the CRI, or a fidelity metric, like the gamut indices, the Feeling
of Contrast Index FCI , the Harmony Rendering Index (HRI). It is not communally admitted that the increase
of gamut well represents an increase of colour discrimination or increase of chroma for all colour shades.
Zukauskas et Al recommended a 4-dimension colour quality-metric that can be used as simple
communication tools to the end user. The four components of this metric are the CCT, the colour fidelity
index CFI, the colour saturation index CSI and the hue distortion index HDI (see point D above).
Many researchers involved in the development of colour rendering metrics claim that it is not possible to
specify the colour rendering properties of light sources with a single parameter, some recommend to use
separate indices for different task and different environment. Others are in favour to keep one index with
simple calculation for applicability.
2.4.3. Activity and work of the TC 1-69 of CIE
The TC well acknowledges that the CIE CRI 13.3 is based on outdated colorimetric metrics and gives poor
prediction for LED lighting. New indices are considered by the TC, not based only on “colour fidelity” but also
taking into account “colour preference”.
The CIE 2010 activity report included also the following indices as promising candidates : FCI , RCRI , HRI
and CCRI. At the last workshop on colour quality held at Hangzhou, September 2012, the program included
the presentation by experts of following indices as the most promising CRIs : CQS, nCRI, GAI and the MCRI.
From press release, LED magazine Feb. 2012, we know that no consensus was yet found, and that some
members were in favour to quickly adopt the CQS and other members wanting a more precise measure of
colour rendering. The final version of nCRI specification, which a refinement of the CRI-CAM02UCS, has
just been distributed to TC 1-69 members in December 2012.
We just include at the time of this report drafting a recent paper from YF Chou et AL. [21] describing the final
refinements of the CRI-CAM02UCS to form the nCRI. The new index is calculated with 273 TCS broken
down in 4 groups: 90 colour constant , 90 colour inconstant, 90 reflectance difference, 3 complexion. The
colorimetric observer is the 10° observer, average colour difference is calculated as the RMS of CIECAM02UCS colour differences and general index as exponential function of this colour difference as done
for the CQS Qa calculation.
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2.4.4. Results of implementation of relevant metrics
The results are those obtained and reported for deliverable D311. The aim was to compare different colour
rendering indices /metrics in conjunction with specific implementations on a set of 122 SPDs representative
of all lighting technologies.
Fourteen indices/metrics have been implemented. The selection of metrics took into account the CIE TC1-69
recommendations for relevant metrics to complement and supplement the current index for colour rendering
specification, we added a statistical method (CFI/CSI/HDI) in our investigation, the results are then given for
the following indices/metrics:
§
CRI Ra : current CIE CRI 13.3 general index [2],
§
CQS Qa : proposal Colour Quality Scale general index [12],
§
MCRI Sa : Memory Colour rendering Index general index [18] ,
§
CRI-CAM02UCS : updated CRI Ra with the CAM02-UCS [11],
§
CRI Ra96 : CIE proposal to update the CRI Ra [3],
§
RCRI : ordinal scale Colour Rendering Index [19],
§
CCRI : Colour Category Rendering Index [16],
§
HRI : Harmony Rendering Index [17],
§
FCI : Feeling of Contrast Index [14],
§
GAI : Gamut Area Index [13],
§
CQS Qg : Gamut Area Scale [12],
§
CFI stat : Colour Fidelity Index – statistical [15],
§
Combination “X“ + “Y”: combination using the mean value of the indices “X” and “Y”.
SPDs data base
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.
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Numerical results
The graphical prediction of implemented metrics are plotted versus the n° of SPD clustered by type of light
source as presented in figure n°2. The following graphs are excerpts of the deliverable D311 for some
indices/metrics considered as good candidates for a new CRI and do not summarize all the results.
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
Figure 2 : SPD colour, number and type of light source
Comparison CIE Ra 13.3 / CQS 7.5
Value
120
100
80
CIE Ra 13.3
60
Qa
40
20
0
0
10
20
30
40
50
60
70
80
90
100
110
120
SPD N°
Figure 3 : Comparison of CRI 13.3 Ra and CQS Qa 7.5
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Comparison CIE Ra 13.3 / CRI CAM02-UCS
Value
120
100
80
CIE Ra 13.3
60
CRI-CAMUCS
40
20
0
0
10
20
30
40
50
60
70
80
90
100
110
120
SPD N°
Figure 4: Comparison of CRI 13.3 Ra and CRI-CAM02UCS
Comparison CIE Ra 13.3 / MCRI
Value
120
100
80
CIE Ra 13.3
60
MCRI
40
20
0
0
10
20
30
40
50
60
70
80
90
100
110
120
SPD N°
Figure 5: Comparison of CRI 13.3 Ra and MCRI
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Comparison of metric proposals
Value
120
GAI+CRI
100
CQS + GAS
80
60
CRICAMUCS
40
MCRI
20
0
0
10
20
30
40
50
60
70
80
90
100
110
120
SPD N°
Figure 6: Comparison of metric proposals
Comments on the obtained results implemented indices/metrics
The results of indices/metrics applied on 122 SPDs do not assess them, since three is no comparison with
subjective ratings, but rather the results give the general behaviour of indices/metrics globally and against
the source type and enable to perform comparison between them.
For example on figure 5 we can see that LED clusters obtain very high MCRI values in comparison to CRI
Ra, and that for continuous spectra (QTH, fluorescent, LED NUV) the MCRI values are systematically
smaller for high values of CRI Ra. We can deduce that MCRI increases or decreases with the chroma of the
light source and that most of LED sources have better prediction than traditional sources.
Other comments, on the presented graphs in this report, are that CRI-CAMUCS is very similar to CRI Ra but
with increased values when CRI Ra is low for LED clusters. We observe the same phenomenon for CQS Qa
but with superior increase for LED clusters. On figure 6 we can compare the combined metric “CRI Ra+GAI”
and “CQS Qa + GAS” and observe that the Qa+GAS gives much greater predictions for LED sources.
A global finding of the D311 is that there is a good correlation between the 14 implemented test metrics for
fluorescent sources, with an average correlation coefficient equal to 0.90. This average correlation coefficient
drops to 0.57 for LED sources but stays greater than 0.70 for the fidelity metrics : CRI Ra, CRI-CAM02UCS,
CQS Qa and CIE CRI Ra96. Combined metric “fidelity + gamut” and MCRI obtain low correlation with the
average set of metrics, “CRI+FCI” obtains the lower coefficient of correlation of 0.18 with the whole set,
indicating a likely big change in colour rendition prediction with theses metrics.
Other specific findings were brought on specific implementation. The methods for the reference source
determination, CCT and the closest reference in a uniform chromatic diagram, performed for the computation
of CIE Ra96. The CCRI method gives interesting results increasing prediction for LED sources but also
increasing the predictions for HPS sources – this was corrected with a small modification of the main
formulae. We assessed also the impact of the TCS using the metric set or a Macbeth Digital Colour Checker
or the 1296 Matte Colour Chips Munsell Atlas. We observed significant differences on the general index
depending on the set of TCS and also variables differences between metrics on special indices (see D311
for details).
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2.4.5. Metrics assessment by field trials
A real living room has been built to carry out a subjective experiment in a common and real environment.
The test room has been furnished and decorated with many coloured natural and artificial objects. A special
lighting system has been designed: it enables to uniformly light the test room and to quickly select a lighting
source out of 12 and changing only the spectrum of the illumination in the test room. The experiment has
been performed with 43 people and nine lighting sources, including 6 led-based lightings, broken down into
two CCT domains centred around 2700 K and 5000 K.
1
LED WW
2700
0,9
Spectral density
0,8
LED WR
2700k
0,7
0,6
LED
RGBY
2700K
LED
RGB
5000k
LED
NUV
5000k
LED CW
5000k
0,5
0,4
0,3
0,2
0,1
0
380
480
580
680
780
Wavelenght (nm)
Figure 7 : Graph of the LED SPD for the subjective experiment
Figure 8 : Views of the subjective room for the colour rendition experiment
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The index/metric predictions with the average subjective scores for preference are given in the table 3 below
for each of the nine lighting. We can notice that the subjective scores of the cold sources vary in a large
interval (87 to 40) while the warm sources vary in a smaller interval (78 to 97) with two equal scores (88) and
one score very close (91).
Light sources
FL 5000K
LED NUV
5000K
LED CW
5000K
LED RGB
5000K
LED WR
2700K
LED WW
2700
RGBY
2700K
HAL 2700K CFL 2700K
CRI Ra
93,70
98,10
70,68
35,58
88,56
82,78
76,20
99,70
82,00
CQS Qa
96,45
99,10
71,33
62,89
90,48
79,40
79,06
96,91
75,78
MCRI Sa
92,71
91,90
75,87
95,36
94,38
88,36
95,22
94,32
82,90
CRI-CAMUCS
94,30
98,51
71,02
49,72
86,79
78,82
80,27
99,02
75,97
RCRI
100,00
100,00
56,09
56,09
98,00
74,40
80,90
100,00
74,40
CCRI
91,96
94,63
73,92
55,06
82,75
77,07
71,93
86,06
77,58
CRI Ra96
95,21
98,52
71,30
47,50
88,23
82,02
80,79
99,28
79,49
HRI
97,92
99,36
104,51
66,93
95,16
101,01
92,16
100,11
99,32
FCI
111,09
108,59
86,23
178,79
138,31
118,83
148,20
123,00
116,31
GAI
97,71
90,14
78,39
134,56
63,44
48,35
57,57
48,93
49,02
CQS Qg
103,41
100,84
88,54
139,64
109,28
97,29
110,69
97,36
98,20
CRI + GAI
95,71
94,12
74,54
85,07
76,00
65,57
66,89
74,32
65,51
CQS Qa + Qg
99,93
99,97
79,94
101,27
99,88
88,35
94,88
97,14
86,99
Ra96+ CCRI
93,59
96,58
72,61
51,28
85,49
79,55
76,36
92,67
78,54
CFI stat
94,25
100,00
16,71
11,11
52,72
33,57
24,90
100,00
30,42
Subj. Preference
85,35
86,74
63,95
40,23
88,14
77,91
88,14
97,44
90,93
Table 3 : Metric’s predictions and average subjective scores
The correlation, between the predictions of fifteen metrics and the subjective scores of preference , have
been computed using the Pearson and Spearman coefficients of correlation and considering different
rounding of subjective and objective data.
These correlation coefficients have been computed on the whole set of lightings and the five possible
subsets with respect to the CCT (cold/warm) and the technology (all technology/LED technology). From
these correlations, with the subjective scores, a set of best metrics in term of prediction of “preference” has
been drawn and is illustrated with radar charts for all subsets of lighting sources (see figure 9). We are well
aware that the number of samples is too low to correctly state on some subsets, but the goal here is to see
the trend.
The results of tables of Pearson (see table 4) and Spearman coefficients of correlation wit subjective scores
(see D312) obtained from our experiment can be summarized as follow :
§
the fidelity metrics are well correlated to “cold sources”, the current metric CRI Ra fails for warm
sources and new fidelity metrics and CQS Qa perform better for warm sources,
§
the gamut metrics and FCI do not correlate well for “cold sources” and “warm sources” but correlate
very well for “warm LED”,
§
the MCRI also correlates very well for “warm LED” but fails for all other source subsets,
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§
other metrics like CCRI or HRI, based on specific properties of colour rendering, produce interesting
results for some cases but globally do not perform better than the current CRI.
§
statistical method, like CFI which is based on fidelity, can also give interesting results and better
results than the current index for some cases.
§
combined metrics (CRI+GAI, Qa+Qg) do not correlate in general, excepted for warm LED and warm
sources.
Metrics
all
sources
cold
sources
warm
source
LED
sources
cold LED
warm
LED
mean
rank
CRI Ra
0.912
0.998
0.576
0.923
0.998
-0.044
0.727
6
CQS Qa
0.762
0.961
0.581
0.839
0.948
0.500
0.765
4
MCRI
0.130
0.016
0.235
0.162
-0.159
0.991
0.229
12
CRI-CAMUCS
0.862
0.996
0.634
0.921
0.996
0.596
0.834
1
RCRI
0.783
0.894
0.602
0.842
0.860
0.725
0.784
3
CCRI
0.776
0.999
0.531
0.812
0.999
0.052
0.695
7
CRI Ra96
0.892
0.998
0.612
0.937
0.998
0.381
0.803
2
HRI
0.699
0.782
-0.075
0.682
0.791
-0.945
0.322
9
FCI
-0.413
-0.712
0.020
-0.379
-0.731
0.941
-0.212
13
GAI
-0.761
-0.690
0.006
-0.797
-0.757
0.945
-0.342
15
CQS Qg
-0.628
-0.715
-0.041
-0.577
-0.740
0.991
-0.285
14
CRI + GAI
-0.191
0.613
0.486
-0.222
0.463
0.577
0.287
10
CQS Qa + Qg
0.056
0.106
0.430
0.067
-0.054
0.910
0.252
11
Ra96+ CCRI
0.851
0.999
0.594
0.885
0.999
0.064
0.732
5
CFI stat
0.609
0.916
0.654
0.618
0.889
0.202
0.648
8
Table 4 : Pearson correlation coefficients
A general observation can be drawn from this specific subjective experiment and applies for the preference
attribute : the indices /metrics which reward very high chroma can fails, a too high-chroma could lead to
deprecated perceived quality, but on other hand the metrics which reward increased chroma seem to better
rank warm LED sources having close subjective scores of preference.
We know that is observation just relies on a set of 3 warm LED-based sources, one quadric-chromatic , one
phosphor-converted and one phosphor-converted with a red LED, representing all type of LED-based warm
lights but with just small differences of rated preference between two of them. We also know that the RGB
5000K presented a very strong saturation and was somehow atypical for a general lighting application.
Nevertheless it is interesting to have such cases for general validation, but to better understand how metric
perform for prediction it is more subjective experiments with more test cases.
The result of Pearson correlation of six metrics are plotted using radar charts (see figure 9) with the current
CRI Ra to better visualize the improvement of the metric/index on the different subsets. Even if the
correlation with the warm lights sources subjective preference is not high we can see the gain against the
current CRI predictions.
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CRICAM UCS
Pearson correlation
all sources
Pearson correlation
all sources
CRI Ra
warm LED
cold sources
cold LED
warm source
CRI Ra
warm LED
cold sources
cold LED
warm source
LED sources
LED sources
CRI Ra96
Pearson correlation
all sources
cold sources
cold LED
warm source
all sources
CRI Ra
warm LED
cold sources
cold LED
warm source
LED sources
LED sources
Ra96+ CCRI
all sources
CQS Qa
Pearson correlation
CRI Ra
warm LED
Pearson correlation
RCRI
CFI stat
Pearson correlation
CRI Ra
CRI Ra
all sources
warm LED
cold sources
cold LED
warm LED
cold sources
cold LED
warm source
warm source
LED sources
LED sources
Figure 9 : Radar charts of 6 index/metric proposals
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2.5.
Measurement and processing of spectral data
We did not investigate the methods of measurement of spectral data for this WP related to perceived quality
of SSL. Radiometric measurement and use of spectro-radiometer are addressed in WP1.
The reference documents we use are the CIE publication 15:2004, the CIE publication n°63 – 1984 and the
IESNA LM-79-08. Actually the most useful standard for SSL is the IESNA-LM79.
We performed in a previous study [22], at LNE-CNAM, an uncertainty analysis on chromaticies (x,y), CCT
and CRI using the Monte-Carlo method of propagation of uncertainty and following the recommendation of
the supplement 1 to ISO/BIPM GUM (guide for uncertainty measurement) [20].
For a reference setup based on a single monochromator/PMT the expanded uncertainties on special indices
Ri were comprised between 0.2 and 1.0 for 5 typical LED lightings. With a CCD spectro-radiomer, Minoltat
CS-2000, the expanded uncertainties on special indices were comprised between 0.4and 1.6 for 5 typical
LED lightings. The maximal expanded uncertainties for the CIE CRI Ra was 0.2 for the reference setup and
was 0.4 with the CS-2000 for the same 5 typical LED lightings.
The study also revealed that the calculation of (x,y), CCT and CRI with different implementations
(commercial software, diffused excel spreadsheets from different authors, CIE program) can yield to
significant discrepancies up to 0.9 on the special index R9. The differences on Ra were smaller than 0.3.
3.
Quality metrics for colour quality
3.1.
Quality metric specification
The goal of a quality metric is to provide users (not only end-users : manufacturer, integrator, retailers,
consumers) with a reliable and meaningful information about the quality of a product. The delivered
Information could be a numerical value (95%), a class (class A, B, C), or an ordinal category : for instance
“excellent”, “very good”, “good”, “fair”, ”poor”.
The minimum requirement for a basic quality metric could be:
§
§
§
Define the set of parameters that can be scaled to quality factors or performance criteria,
Specify measurement standards or measurement methods for these parameters,
Provide guidance to interpret quality metric specifications in term of absolute quality and comparative
quality of the product.
For colour rendering most of the indices/metrics are by definition the core of a colour quality metric, they
specify a method to compute, from the source spectrum, a numerical value, generally scaled between 0 and
100, representing the quality of the colour rendering of the light source. It should be recall here that this
value is for most of the metrics, at least the fidelity metrics, a relative value expressing an average colour
shift with the colours rendered by a reference illuminant of the same CCT. The properties of the reference
illuminants vary considerably with the CCT, for instance number of colour shades, colour discrimination,
colour identification or naming, and only the CCT specifies this reference illuminant. Special indices provide
the same kind of information, the magnitude of colour shift, TCS by TCS. The only factor dealing with the
absolute rendering is the CCT weighting factor of the CQS, this factor accounts for the drop of gamut for low
CTT of the test light source.
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3.2.
Synthesis and update on colour quality metrics for lightings
Usually the colour quality specification of a lighting source is a part of the colour property specification. The
worldwide official general index of colour rendering quality is the CIE CRI 13.3 Ra. The TC1-69 recognized
that the CIE CRI 13.3 is based on outdated colorimetric metrics and give poor prediction for LED lighting. But
industry would not replace it before CIE TC1-69 endorses new metrics and publishes them.
3.2.1. Common specification of colour property of white lighting :
1.
CCT names : “warm white”, “(neutral)white”, “cool white”, “daylight”
2.
CCT value in Kevin (K) and distance from spectrum locus Duv
3.
Chromaticity (x,y) or (u’,v’)
4.
Relative spectral density (graph)
5.
Colour rendering quality: CRI 13.3 Ra and special indices (or just the Ra/R9) or other see 3.3.3.
Photometric measurement protocol and chromaticity specification for SSL can be respectively found in
references [9] and [7]. The reference [9] is not a protocol for measurement of chromaticity but describes the
measurement of photometric quantities by means of spectroradiometers and then could be applied to tristimulus determination of TCS for colour rendering . It is interesting also to note that in reference [7] Duv
tolerances are given for the defined CCT ranges, these tolerances should limit the low values of the index of
colour rendering. Other developments aiming at traceable measurement of SSL with spectroradiometers can
be found the WP1 (D131 to D134) of the ENG05 project.
3.2.2. Activity for new colour rendering metrics
Many laboratories have been working on new metrics for colour rendition and published proposals. The CIE
2010 activity report listed the following indices as promising candidates : CQS, GAI , MCRI, FCI, RCRI , HRI
and CCRI supported by 17 (+/-) research reports from 10 labs/groups from 7 different countries. At the last
workshop on colour quality held at Hangzhou, September 2012, the program included the presentation by
experts of the following indices as the most promising CRIs : CQS, nCRI, GAI and the MCRI.
From press publication, LED magazine, we learnt that the TC1-69 was close to recommend a dual standard,
one being the CQS. According to J. Schanda answering the article, is was demonstrated that prediction of
current CRI and CQS with a given TCS could change from high to low prediction with a real metameric
samle of that given TCS.
Since then the final version of the new metric, called nCRI, has just been distributed to TC1-69 members in
December 2012. This new metric is a refinement of the CRI-CAM02UCS which uses the 10° observer,
several elaborated sets of TCS with metameric and “reflectance difference” TCS sets and features, likewise
CQS, new formula for the general and special indices (RMS value, exponential form).
3.3.3. Recommended specification for colour rendering from the ASSIST program
The Alliance for Solid-State Illumination Systems and Technologies (ASSIST), an US initiative gathering
researchers, manufacturers, and government organizations, established in 2002 by the Lighting Research
Center (LRC) published guidelines and recommendations. One interesting paper [22] is downloadable “The
Class A Color Designation for Light Sources » by M. S. Rea, & J. P. Freyssinier , this paper finalizes the
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approach already described in a previous paper “Color “Rendering: a tale of two metrics” [13]. The author
argue that the currents metrics used by industry, CCT and CRI, is not meaningful for consumers and
propose a “class A color” designation for general illumination : white illumination with CRI = 80 and 80=
GAI =100.
3.3.4. Summary of the work achieved for this task
We conducted a large review of all proposals for colour rendering quality. There are numerous proposals
putting into question all the principles of the current rendering metric, the CIE CRI 13.3. This review showed
that the concept of “colour quality rendering” is multi-dimensional, each dimension could reflect a specific
property of colour rendering - most of them can be sorted out under the fidelity properties category and the
preference properties category. The fidelity properties are related to the preservation of colour appearance of
objects under the test light source while the preference properties are related to the enhancement of the
colour appearance of objects under the test light source, the enhancement being necessarily a departure
from fidelity, those two aspects cannot be achieved at the same time. In both two approaches, fidelity or
preference, there is an underlying question: what the is the reference light. Preservation attributes can be
derived from the absolute colour shift between the test light and the reference light, and preference attribute
derived from some specific signed difference of the test light to the reference light – like a increase of chroma
in favour of subjective preference.
Most of methods/principles of current CRI are given alternative methods: ordinal scale against continuous
scale, statistical description rather than limited test colours, colour naming or categorization rather average
colour shifts. All the proposal use updated chromatic diagrams or colorimetric metrics. One the most original
metric is the MCRI using “colour memory” of objects rather than reference illuminants [18], and is based on
similarity functions, subjectively built, in a relevant chromatic diagram.
The well recognized property of LED-based lighting is the enhancement of chroma/saturation of colour.
Several new proposals attempt to take it into account and derived index/metric supplementing or
complementing the CRI. Two solutions are proposed/recommended to take into account this increase of
chroma (1) to supplement the CRI with the GAI (Lighting Research Centre metric [13][20]), (2) to
complement the CRI discounting in the colour difference index calculation the contribution of the increase of
chroma (NIST - CQS [12]).
The computation and application, over a broad set of spectra representing all technologies, of relevant
metrics, including those listed by CIE as promising candidates, enable us to watch the behaviour of each of
current and proposals of index/metric. It confirmed that the new metrics do not bring so much to traditional
lights and that the greatest discrepancies are obtained for LED-based lightings. We observed that new
based-fidelity metrics (including CQS) increase the low scores of many LED-clusters or LED-PC lighting
sources and that gamut effectively increase for these light sources, with very high values reached for the
FCI. The MCRI has a special behaviour not balanced between traditional/LED-based lighting. The
dependence with the set and choice of TCS is clearly highlighted.
We set up a subjective experiment in a real living room, with LED-based and traditional lighting, to assess
metrics and to learn about the subjective “preference” and its relationship to other subjective attributes like
perceived “fidelity”, “naturalness”, ”vividness” and “chart colour quality”. All attributes co-vary in the same
direction, and may be if specific relationship can be observed for the appearance a limited set of objects, as
found in other subjective experiments, they likely could not apply on a large set of objects and materials.
The main results of the subjective experiment is (1) that fidelity metrics perform quite well for the “cold
sources”, (2) the current CRI fails (correlation=0) to predicting the “warm led”, (3) the fidelity metrics,
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including CQS, with updated colorimetric metric perform better for “warm sources” and “warm led sources”
than the current CRI, and (4) gamut and MCRI perform very well to predicting preference for “warm led
sources” but not for “warm source” and completely fails for “cold sources”. As a result combined metric
“fidelity+gamut” do not give good prediction on the whole set of lightings “cold an warm”. The ordinal scale
based CRI – RCRI derived forom the CRI-CAM02UCS – seems to improve predictions for “warm LED” but
obtains lower correlation for “cold LED”. The HRI, based on harmony, provides a high negative correlation for
“warm LED” and a null correlation for “warm sources”. The CRI-CAMUCS globally gets the greatest
correlation coefficients, but the spearman correlation for “warm sources” is still low (0,35).
This subjective experiment was not conducted to give the “truth” on colour rendering metrics but was
developed as a facility to assess metrics in a real environment and with real lightings, with no bias or
artefacts, simply asking people to rate their preference in those real conditions.
LNE still continues to investigate colour rendering metrics and colour quality specification through its own
program of R&D, testing activities for consumer’s organisations, customers, and future projects.
The last discussions at the CIE meetings turned around the CQS and the nCRI, a refinement of CRICAM02UCS whose specification has been recently distributed. The CIE is not a international regulation body
but the most recognized organization for lighting specifications and probably the industry will follow the new
metrics, single or dual, endorsed by CIE. Then our contribution will be to address a recommendation to CIE
outlining the outcomes of this study and to inquire for the new metric nCRI implementation details for further
assessment.
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References
[1]
CIE publication 15: 2004 – Colorimetry – third edition.
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