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An alternative colour rendering index based on memory colours. Kevin SMET KaHo Sint-Lieven – K.U.Leuven Light & Lighting Laboratory – ESAT/ELECTA Gebroeders Desmetstraat 1, 9000 Gent ( Belgium ) Tel: +32 92 65 87 13 kevin.smet@ kahosl.be. All data is for personal or TC1-69 use .

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an alternative colour rendering index based on memory colours

Analternative colour rendering index basedonmemorycolours

Kevin SMET

KaHo Sint-Lieven – K.U.Leuven

Light & Lighting Laboratory – ESAT/ELECTA

Gebroeders Desmetstraat 1, 9000 Gent (Belgium)

Tel: +32 92 65 87 13

kevin.smet@kahosl.be

Kevin Smet – Analternative colour rendering index

slide2

All data is forpersonalor TC1-69 use.

The basicidea, methodsused, visualresults & modeling have been written in a paper that is underreview

forpublication in “Journal of Vision”.

Resultscomparing the Memory Colour based index with CIE CRI, CQS and visualresultslike the Thurstonescalings, kindlyprovidedby Sophie Jost-Boissard, as well as withvisualscalingexperimentsthatwillbeperformed in the nearfuturewillbepublished in a next paper.

Kevin Smet – Analternative colour rendering index

slide3

BasicIdea

&

TheoreticalMethod

Kevin Smet – Analternative colour rendering index

basic idea
BasicIdea:
    • Absolute assessment
      • No need of referenceilluminants!
      • No dependenceoncorrelatedcolour temperature: test sourceswith different CCT canbecompared.
    • More realisticway of evaluating colour rendering.
  • (*) Memory colours define the colours that are recalled in association with familiar objects in long-term memory (Bartleson, 1960)..

Referencing is done to memorycolours* of familiarobjects,

NOT to a referenceilluminant!

Kevin Smet – Analternative colour rendering index

method
Method
  • YES / NO
  • Only a qualitativemeasurefor colour renderingbasedonacceptability of apparent object colour.

Investigatecolour acceptanceboundaries of familiarobjects

  • Test subjectsrateacceptability(YES/NO) of a set of apparent object colours:

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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method1
Method
  • Similarity ratings on a 5 point scale as a function of chromatic distance to memory colour.
  • Quantitativemeasurefor colour rendering.
  • Test subjectsratesimilarity (on a 5 point scale) of a set of apparent object colours to theirmemory colour of the object:

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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from similarity ratings to an alternative colour rendering index
Fromsimilarity ratings to analternative colour rendering index
  • Model pooledsimilarity ratings by a bivariateGaussiansurface R(x,y):
      • Chromaticity of apparent object colour: X
      • Centre Xc = chromaticity of memory colour
      • Mahalanobisdistance d(x,y) ~ degree of similarity S(x,y)
      • aiare fitting parameters:
        • a1 & a2 are used to account forinterobservervariability
        • a3 to a4 describe the similarityfunction S(x,y).

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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from similarity ratings to an alternative colour rendering index1
Fromsimilarity ratings to analternative colour rendering index
  • Alternative colour rendering index Sa:
    • Calculateforeach object i:
      • chromaticity of object renderedby the test source
        • byusing the spectralreflectance factors of the object.
      • Mahalanobisdistancedi to chromaticity of memory colour
      • Special colour rendering index:S’i = exp(-0.5* di)
      • General colour rendering index:S’a= mean(Si).

5. (Rescale indices to CRI levels: Ra(F1-F12)= a*Sa(F1-F12)+b)

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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slide9

Practical

Kevin Smet – An alternative colour rendering index

Kevin Smet – Analternative colour rendering index

real versus simulated objects
Real versus simulatedobjects?
  • Naturalness has influenceonsimilarity ratings

(Yendrikhovskii, Blommaert & De Ridder, 1999)

  • Colour constancy is greaterforsurfacecolours and lights in realscenesthan in simulatedscenes.

(Berns & Gorzynski, 1991; Brainard, 1998)

Experimentswithrealobjects !

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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illumination box to change apparent object colours
Illumination box to changeapparent object colours

Create the illusion of the object changing colour!

  • Special design to maskanyilluminantclues!

a. RGBA LEDs

        • changeapparent object colour

b. Diffusing tunnel:

        • uniform illumination of objects
        • avoidspecularreflectionon object

c. Transparent support forobjects:

        • hidesurfacereflections

revealingillumination colour

d. Back panel lighting:

        • constant adaptation white with CCT = 6000K.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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spectral measurements
SpectralMeasurements
  • Tele-spectroradiometrically:
    • radiometric measuring head
    • 74055 MS260i spectrograph
    • iDUS DV420A-BU2 CCD camera
  • Setupidentical to viewingconditions

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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chromaticities colour spaces
Chromaticities / colour spaces
  • CIE 10 degreeobserver
  • Measuredchromaticities are transformed to correspondingcolours:
    • Chromatic AdaptationTransform: CAT02
    • Equal Energy Illuminant
  • Colour spaces:
    • SVF (Seim & Valberg, 1986): ~perceptually uniform colour space
    • CIECAM02 is unfortunatelynotapplicable:
      • Yb of background cannotbedefined, becauseit is more luminous (=selfluminous) than the reflected white of the front panel (Yb wouldbe > 100).

Personalcommunicationwith Mark Fairchild.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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test object selection
Test Object Selection
  • High degree of familiarity: NATURAL OBJECTS (e.g. food)
  • Object chromaticitydistributions are preferablyunimodalor a single mode shouldbeunambiguouslyspecifiable; i.e. the natural colour varianceshouldnotbetoo high:

e.g.:

    • green apple, ripebanana, …
    • but NOT: blue grape, because the chromaticity of blue grapesvariesfrom a black/dark blue to wine red.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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test object selection1
Test Object Selection
  • Object chromaticities spread uniformlyaround the huecircle:
    • Primaryregions of choice: green, yellow, red, purple & blue.

BUT somedifficulties:

        • Illumination box cannotchange the apparent colour of REDnaturalobjectssufficientlydue to theirhighly chromatic nature.

- ORANGE is taken as a compromise.

        • Number of bluewell-knownunambiguouslyspecifiableobjects is extremelylimited.

- a SMURF figurine was selected, becausewhenquestioned most people report having a goodideasmurf blue.

  • Extra experiments are plannedwithsomeless chromatic objects as well: strawberry yoghurt (red), cheese , caucascian skin, …

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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experiment setup
Experiment setup:
  • Calibrateillumination box: map XYZ to RGBA DAC input values.
  • Calculate object colour solid.
  • Select luminanceplanewith optimum chromaticitygamut.
  • Calculate RGBA DAC input valuesresulting in a uniform gridin the colour spaceselected.
  • Add double gridpoints to illuminationsequence.
  • Randomizesequence.
  • Add a set of 20 training pointsto familiarizeobserverswith the scale.
  • Measure the object spectralradiance.
  • Calculatechromaticities of sequence of apparent object colours
  • Visual assessmentof sequenceby test subjects:
    • Similarity ratings on a 5 point scale
      • 1: very bad; 2: bad; 3: neutral; 4: good; 5: verygood

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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slide17

Experimentalresults

Kevin Smet – Analternative colour rendering index

visual assessment results
Visual assessmentresults
  • Intra & Interobservervariabilitywith PF/3 performance factor:
    • Results are good, consideringthat a PF/3 of 30 is common in colour discrimination studies.

(Guan & Luo, 1999a,b; Xu, Yaguchi & Shiori, 2001)

    • Data can be pooled and average observer can be postulated.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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visual assessment results1
Visual assessmentresults
  • Average observer reliability (real world validity) analysed with ICC(2,n) according to method described by Shrout & Fleiss (1979).
    • Good results:
      • validate the use of pooled rating data as basis for colour rendering evaluation.
    • ICC(2,n) shows a high inverse correlation (ρ = -0.94) with the PF/3.
  • Observers agreed best on what a ripe banana looks like and least on what dried lavender should look like.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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model results
Model results
  • Pooled rating distribution was fitted

using MATLAB lsqcurvefitfunction.

  • Goodness-of-fit to mean ratings of pooled

data analysedwithPearsonρ and RMSE:

    • Confirmation of Yendrikhovskij et al. (1999):
      • Similarityjudgementscanbedescribed

by a bivariateGaussian distribution in a

perceptually uniform colour space.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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slide21

Colour renderingevaluationbasedonmemorycolours.

- Results -

Kevin Smet – Analternative colour rendering index

rescaling the similarity functions
Rescaling the similarityfunctions

Fluorescent sources F1-F12

  • Similarityfunctions are rescaled to the CIE CRI level by a lineartransformationcalculatedusing a least squares approach:
  • Advantage: Colour rendering scores foroldsources are keptnearly the same.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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alternative cri for 90 lamps
Alternative CRI for 90 lamps
  • Lamp spectralradiances: seeexcel file.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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correlation with visual assessments
Correlationwithvisualassessments
  • All visualscaling data was kindlyprovidedby Sophie Jots-Boissard
    • The Pearsoncorrelationcoefficientbetween the Thurstonescalingsforattractiveness, of nine3000K (Boissard, 2009) & eight 4000K lampsobtained in a series of visualexperimentsperformedby Sophie Jost-Boissard, and the Memory Colour based index is high: ρ3000K = 0.95 (p<0.05) & ρ4000K = 0.87 (p<0.05).

As to not obscure anypossiblecorrelationbetween the visualassessments and the Memory Colour based index, only the special colour rendering indices (apple, banana and orange) have been used in thiscalculation, since a lightsourcehaving terrible colour renderingcapabilities in the blue to purpleregionmightobscurthiscorrelation.

    • The Pearsoncorrelation has also been calculatedbetween the Thurstonescalingsforattractiveness and the CRI and CQS. The Pearsoncorrelationcoefficientswere:

ρCRI,3000K = 0.26 (p=0.50) & ρCRI,4000K = 0.07 (p=0.86)

ρCQS,3000K = 0.48 (p=0.20) & ρCQS,4000K = 0.44 (p=0.28)

For the samereasondescibedabove, special colour rendering indices 2,3,4,10,11,13 & 14 have been usedfor the CRI, whilefor the CQS special indices 7-13 wereselected.

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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planned experiments
Plannedexperiments
  • Extend special indices into the red region: strawberry yoghurt, ham, bacon,…
  • Addsomelesssaturatedobjects.
  • Try and get a similarityfunctionforCaucasian skin.
  • Extendnumber of observersoneach object.
  • Performvalidationexperimentswith a set of test lampshaving low CRI but high visualappreciation and withan object set that is notlimited to the green-redregion!
  • Change to an even more perceptually uniform colour space: eg. IPT?

Kevin Smet – Analternative colour rendering index

Kevin.Smet@kahosl.be

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references
References
  • Bartleson, C. J. (1960). Memory colors of familiar objects. J. Opt. Soc. Am., 50, 73–77.
  • Berns, R.S. & Gorzynski, M.E. (1991). Simulating surface colors on CRT displays: the importance of cognitive clues. Proc. AIC Colour and Light, 91, 21-24.
  • Brainard D.H., (1998). Color constancy in the nearly natural image. J. Opt. Soc. Am. A, 15, 307-325.
  • Guan, S. & Luo, M.R. (1999a). Investigation of parametric effects using small colour differences pairs. Col. Res. Appl., 24, 331–343.
  • Guan, S. & Luo, M.R. (1999b). A colour-difference formula for assessing large colour-differences. Col. Res. Appl., 24, 344-355.
  • Jost-Boissard, S., Fontoynont, M., & Blanc-Gonnet, M., (2009). COLOUR RENDERING OF LED SOURCES: VISUAL EXPERIMENT ON DIFFERENCE, FIDELITY AND PREFERENCE. Proc. CIE Light and Lighting conference. Budapest. Hungary. 27-29 may 2009.
  • Seim, T. & Valberg, A. (1986). Towards a uniform color space. A better formula to describe the Munsell and OSA color scales. Col Res. Appl.,11, 11–24.
  • Xu, H., Yaguchi, H. & Shioiri S. (2001). Estimation of Color-Difference Formulae at Color Discrimination Threshold Using CRT-Generated Stimuli. Optical Review, 8 (2), 142-147.
  • Yendrikhovskij, S. N., Blommaert, F. J. J., De Ridder, H. (1999). Representation of memory prototype for an object colour. Col. Res. Appl., 24 (6), 393–410.

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