understanding color 2010

SC076 Understanding Color

Giordano Beretta

HP Labs Palo Alto

Alexandria, someday 2010

http://www.inventoland.net/imaging/uc/slides.pdf

Giordano Beretta (HP Labs Palo Alto) SC076 Understanding Color Alexandria, someday 2010 1 / 207

Broad outline

1 Introduction

2 Color theories

3 Terminology

4 Objective colorterms

5 Illumination

6 Measuring color

7 Spectral color

8 Color reproduction

9 Milestones in colorprinting

10 Color imagecommunication

11 Color appearancemodeling

12 Cognitive color

13 Conclusions

14 Bibliography


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Course objectives

Develop a systematic understanding of the principles of colorperception and encodingUnderstand the differences between the various methods for colorimaging and communicationGain a more realistic expectation from color reproductionDevelop an intuition for

I trade-offs in color reproduction systemsI interpreting the result of a color measurement


What is color?

Color is an illusionColorimetry: the art to predict an illusion from a physicalmeasurementExperience is much more important than knowing facts or theoriesThe physiology of color vision is understood only to a very smalldegree

I Physiology: physical stimulus→ physiological responseI Psychophysics: physical stimulus→ behavioral response

What is essential is invisible to the eyeAntoine de Saint-Exupéry (The Little Prince)


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Section Outline

2 Color theoriesChronologyColor vision is not based on a bitmapColor vision physiologyLimited knowledge


Color theories over the Millennia

Particle theory ca.945–715 B.C.E.:sun god Horakthyemits light as a flux ofcolored lilies


Color theories

92,000 B.C.E. — Qafzeh Cave, color symbolism800 B.C.E. — Indian Upanishads

I there are relations among colors400 B.C.E. — Hellenic philosophers

I Democritus: sensations are elicited by atomsI Plato: light or fire rays emanate from the eyesI Epicurus: replicas of objects enter the eyes

100–170 C.E. — Alexandria’s natural philosophersI Claudius Ptolemæus describes additive color based on wheel in

section 96 of the second book of OpticsFirst Millennium — Arab school, pure science

I Abu Ali al-Hasan ibn al-Haytham a.k.a. Alhazen:F invents scientific process (observation–hypothesis–experiment)F disproves Plato’s emanation theoryF image is formed within the eye like in a camera obscuraF describes additive color based on top


Opponent colors15th century — Renaissance, technology

I Leonardo da VinciF color perceptionF color order systemF black & white are colorsF 3 pairs of opponent colors (black–white, red–green, yellow–blue)F simultaneous contrastF used color filters to determine color mixtures

Note: rendered with chiaro-scuro techniqueGiordano Beretta (HP Labs Palo Alto) SC076 Understanding Color Alexandria, someday 2010 10 / 207

Color theories (cont.)

18th century — Enlightenment, physics & chemistryI Isaac Newton:

F spectral dispersion, white can be dispersed in a spectrum by a prismF colors of objects relate to their spectral reflectanceF light is not colored and color perception is elicited in the human visual

system

19th century — scientific discoveryI Thomas Young: trichromatic theoryI Hermann von Helmholtz: spectral sensitivity curvesI Ewald Hering:

F opponent color theory (can explain hues, saturation, and why there isno reddish green or yellowish blue)

F black and dark gray are not produced by the absence of light but by alighter surround


Color theories (cont.)

20th century — advanced scientific instrumentsI Johannes A. von Kries: chromatic adaptation

F why is white balance necessary?I Georg Elias Müller & Erwin Schrödinger: zone theoryI physiological evidence for inhibitory mechanisms becomes

available in the 1950sI molecular biologyI functional MRI techniquesI see http://webvision.med.utah.edu/ for the latest progress


http://webvision.med.utah.edu/

Section Outline



Color vision is not based on a bitmapVision is based on contrastVision is not hierarchical. The simple modeldistal event proximal stimulus brain event

is very questionable. It is believed that feedback loops existbetween all 26 known areas of visual processingIn fact, it has been proved that a necessary condition of someactivity in even the primary visual cortex is input from “higher”areasLike the other sensory systems, vision is narcissisticMany sensory signals are non-correlational — a given signal doesnot always indicate the same property or event in the world

The “inner eye’s” function is not to understand what the sensory statesindicate

Examplesee Science 17 March 2006: Vol. 311. no. 5767, pp. 1606 – 1609


Cognitive model for color appearance

amber

stimulus detectors early mechanisms pictorial register

context parameters

Color lexicon

color nameaction

edgescontourmotiondepth

color

…

internallightness

hue

chromaetc.

lightness

hue

chromaetc.

apparent colorrepresentation

color space

Reliable color discrimination: 1 weekColor-opponent channels: 3 monthsColor constancy: 4 monthsInternal color spaceColor names


Memory colors

Vision is not hierarchicalDelk & Fillenbaum experiment (1965)

We tend to see colors of familiar objects as we expect them to be

Surround

Sky

Adaptingfield

Vegetation

2º

10º

Complexion


Section Outline



Color vision physiologyThe retina has a layer of photoreceptors, which grow like hair(10µm per day). They are of two kinds: rods and conesThe cones are of three kinds, depending on the pigments theycontain. One pigment absorbs reddish light, one absorbs greenishlight, and one absorbs bluish lightThis leads to the method of trichromatic color reproduction, inwhich we try to stimulate independently the three kinds of cones

optic nerve fib

ers

retinal g

anglion ce

lls

amacrine ce

lls

bipolar cells

horizontal ce

lls

rod & cone ce

lls

rods & co

nes

pigment epith

elium

stimulus


Photoreceptors

Credit: Carlos Rozas (CanalWeb, Chile)http://webvision.med.utah.edu/movies/3Drod.mov


http://webvision.med.utah.edu/movies/3Drod.mov

PhotoreceptorsOuter segment

Credit: Helga Kolbhttp://webvision.med.utah.edu/movies/discs.movhttp://webvision.med.utah.edu/movies/phago4.mov


http://webvision.med.utah.edu/movies/discs.mov

http://webvision.med.utah.edu/movies/phago4.mov

The aging retina

Comparative diagrams of 3- and 80-year-old retinal pigment epithelial(RPE) cells in the eye. As the eye ages, the RPE cells deteriorate,making it harder for the brain to receive and register light, leading toblindness. Credit: David Williams, University of Rochester.


EvolutionFrom the difference in the amino-acid sequences for the variousphotoreceptor genes it is clear that the human visual system did not evolveaccording to a single design

Finding Rod and S Mechanisms L and M Mechanisms

Anatomy Distribution perifoveal foveal

Bipolar circuitry one class (only on) two classes (on and off)

Psychophysics Spatial resolution low high

Temporal resolution low high

Weber fraction high low

Wavelength sensitivity short medium

Electrophysiology Response function saturates does not saturate

Latencies long short

ERG-off-effect negative positive

Ganglion cell response afterpotential no afterpotential

Receptive field large small

Vulnerability high low

Genetics autosomal sex-linked

Source: Eberhart Zrenner, 1983


Catching photons

Retinal pigments: rhodopsin, cyanolabe, chlorolabe, erythrolabeI lysine attaches chromophore to a protein backboneI electronic excitation (two-photon catch) initiates a large shift in

electron density in less than 10−15 secondsI shift activates rotation around two double-bonded carbon atoms in

the backboneI entire photocycle lasts less than a picosecond (10−12sec.)I photoisomerization induces shift in positive charge perpendicular to

membrane sheets containing the proteinI this generates a photoelectric signal with a less than 5psec. rise timeI forward reaction is completed in ∼ 50µsec.(10−6sec.)

Quantum efficiency: measure of the probability that the reactionwill take place after the absorption of a photon of light4 pigments sensitized to photons at 4 energy levels (wavelength):L, M, S, and rods


Phototransduction

Credit: Helga Kolb,http://webvision.med.utah.edu/movies/trasduc.mov


http://webvision.med.utah.edu/movies/trasduc.mov

Catch probabilitiesQuantum energy of a photon: hνFor each pigment, there is a probability distribution for a reaction,depending on the photon’s wavelengthw(λ)dλWhat counts is not the energy of a single photon, but the averageFor a spectral power distribution Pλ: S =

∫Pλw(λ)dλ

0.0

0.2

0.4

0.6

0.8

1.0 S-cone

M-cone

L-cone

Rod

650600550500450400

absorbance

nm

Dartnall, H. J. A., Bowmaker, J. K., & Mollon, J. D. (1983). Human visual pigments: microspectrophotometric results from the

eyes of seven persons. Proceedings of the Royal Society of London, B 220, 115–130


Retinal mechanisms

Center

Surround

Surround

ReceptorBipolarcell

Horizontalcell

Amacrinecell

Retinalganglion

cell

Receptors in retina are not like pixels in a CCD sensorReceptive field: area of visual field that activates a retinal ganglion(H.K. Hartline, 1938)Center-surround fields allow for adaptive coding (transmit contrastinstead of absolute values)Horizontal cells presumed to inhibit either its bipolar cell or thereceptors: opponent response in red–green and yellow–bluepotentials (G. Svaetichin, 1956)Balance of red–green channel might be determined by yellowRetinal ganglion can be tonic or phasic: pathway may also beorganized by information density or bandwidth


Parvocellular and magnocellular pathwaysP– M–

Originating retinal gan-glion cells

Tonic Phasic

Temporal resolution Slow (sustained responses, low conductionvelocity)

Fast (mostly transient responses, some sus-tained, high conduction velocity)

Modulation dominance Chromatic LuminanceAdaptation occurs at high frequencies Adaptation occurs at all frequencies

Color Receives mostly opponent type input fromcones sensitive to short and long wavelengths

Receives mostly combined (broadband) inputfrom M and L cones, both from the center andfrom the surround of receptive fields

Contrast sensitivity Low (threshold > 10%) High (threshold < 2%)

LGN cell saturation Linear up to about 64% contrast At 10%

Spatial resolution High (small cells) Low (large cells)

Spatio-temporal resolu-tion

When fixation is strictly foveal, extraction ofhigh spatial frequency information (test grat-ings), reflecting small color receptive fields

Responds to flicker

Long integration time Short integration time

Relation to channels Could be a site for both a lightness channelas for opponent-color channels. The role de-pends on the spatio-temporal content of thetarget used in the experiment

Might be a site for achromatic channels be-cause the spectral sensitivity is similar to Vλ,it is more sensitive to flicker, and has only aweak opponent color component

Possible main role in thevisual system

Sustain the perception of color, texture, shape,and fine stereopsis

Sustain the detection of movement, depth,and flicker; reading of text


Color constancy

Optictract Lateral

geniculatebody Optic

radiations

Primaryvisualcortex

Blob

Axons of retinal ganglion cells in optical nerve terminate at LGNand synapse with neurons radiating to striate cortexLGN might generate masking effects; combination with saccadicmotion of eyeBlobs in area 17 consist mainly of double opponent cellsMay be site for color constancyRequires input from V4 (Zeki)

Why is white balancing necessary in color reproduction?Giordano Beretta (HP Labs Palo Alto) SC076 Understanding Color Alexandria, someday 2010 28 / 207

Section Outline



Limited knowledge

Reaction time at rhodopsin level: femtosecondsReaction time at perceptual level: secondsFrom photon catches to constant color names

We do not know exactly what happens in-between

Examplesimultaneous contrastchromatic induction


1 color appears as 2


Appearance mode

Three flat objects or picture of a white cube illuminated from the topand right?


Our goal

We would like to be able to predict the color of a sample bymaking a measurementHumans can distinguish about 7 to 10 million different colors —just name them and build an instrument that identifies themTask: find good correlates to the subjective color terms


Basis for colorimetry

Too many unknowns in physiology and cognitive processesCannot yet build accurate color vision modelUnlike auditory system, visual system is not spectral butintegrative

I Advantage of integrative system: metamerismBasis of colorimetry:

1 Instead of a physiological model, build a psychophysical modelF Physiology: physical stimulus physiological responseF Psychophysics: physical stimulus behavioral response

2 Assume additivity3 Keep the viewing conditions constant


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Section Outline

3 TerminologyBasicsSubjective color termsObjective color termsColor matchingMetamerismChromaticity diagramsCIE 1931 standard colorimetric observerTristimulus normalization


The CIE

The International Commission on Illumination — also known asthe CIE from its French title, the Commission Internationale del’Éclairage — is devoted to worldwide cooperation and theexchange of information on all matters relating to the science andart of light and lighting, colour and vision, and image technologyWith strong technical, scientific and cultural foundations, the CIEis an independent, non-profit organisation that serves membercountries on a voluntary basisSince its inception in 1913, the CIE has become a professionalorganization and has been accepted as representing the bestauthority on the subject and as such is recognized by ISO as aninternational standardization body


CIE definition 845-02-18: (perceived) colorDefinition (Color)

Attribute of a visual perception consisting of any combination ofchromatic and achromatic content. This attribute can be describedby chromatic color names such as yellow, orange, brown, red,pink, green, blue, purple, etc., or by achromatic color names suchas white, gray, black, etc., and qualified by bright, dim, light, darketc., or by combinations of such namesPerceived color depends on the spectral distribution of the colorstimulus, on the size, shape, structure and surround of thestimulus area, on the state of adaptation of the observer’s visualsystem, and on the observer’s experience of the prevailing andsimilar situations of observationPerceived color may appear in several modes of appearance. Thenames for various modes of appearance are intended todistinguish among qualitative and geometric differences of colorperceptions


Colorimetry

Definition (Colorimetry)Colorimetry is the branch of color science concerned with specifyingnumerically the color of a physically defined visual stimulus in such amanner that:

1 when viewed by an observer with normal color vision, under thesame observing conditions, stimuli with the same specificationlook alike,

2 stimuli that look alike have the same specification, and3 the numbers comprising the specification are functions of the

physical parameters defining the spectral radiant powerdistribution of the stimulus


Grassmann’s laws of additive color mixture

Definition (Trichromatic generalization)Over a wide range of conditions of observation, many color stimuli canbe matched in color completely by additive mixtures of three fixedprimary stimuli whose radiant powers have been suitably adjusted(proportionality)In addition, the color stimuli combine linearly, symmetrically, andtransitively


Color term categories

Definition (Subjective color term)A word used to describe a color attribute perceived by a human.Example: the colorfulness of a flower

Definition (Objective color term)A word used to describe a physical quantity related to color that can bemeasured. Example: the energy radiated by a source


Section Outline



Subjective color terms — Hue

Definition (Hue)The attribute of a color perception denoted by blue, green, yellow, red,purple, and so on

Definition (Unique hue)A hue that cannot be further described by use of the hue names otherthan its own. There are four unique hues, each of which shows noperceptual similarity to any of the others: red, green, yellow, and blue


Brightness

Definition (Brightness)The attribute of a visual sensation according to which a given visualstimulus appears to be more or less intense, or according to which thevisual stimulus appears to emit more or less light

Objective term: luminance (L)

Brightness scales


Lightness

Definition (Lightness)The attribute of a visual sensation according to which the area in whichthe visual stimulus is presented appears to emit more or less light inproportion to that emitted by a similarly illuminated area perceived as a“white” stimulus

Objective terms: luminance factor (β), CIE lightness (L∗)

FactBrightness is absolute, lightness is relative to an area perceived aswhite


Colorfulness

Definition (Chromaticness or Colorfulness)The attribute of a visual sensation according to which an area appearsto exhibit more or less of its hue. In short: the extent to which a hue isapparent

Objective term: CIECAM02 M


Colorfulness — Chroma

Definition (Chroma)The attribute of a visual sensation which permits a judgement to bemade of the degree to which a chromatic stimulus differs from anachromatic stimulus of the same brightness

In other words, chroma is an attribute orthogonal to brightness:absolute colorfulness; we perceive a color correctly independently ofthe illumination levelObjective term: CIE chroma (C∗uv , C∗ab)


Colorfulness — SaturationDefinition (Saturation)The attribute of a visual sensation which permits a judgement to bemade of the degree to which a chromatic stimulus differs from anachromatic stimulus regardless of their brightness

In other words, it is the colorfulness of an area judged in proportion toits brightness: relative colorfulness; we can judge the uniformity of anobject’s color in the presence of shadows and independently of theincident light’s angleObjective term: purity (p), CIE saturation (Suv )

FactColorfulness is absolute, chroma is relative to a white area andabsolute w.r.t. brightness, saturation is in proportion to brightness


Section Outline



Spectral curvesQuantities we can measure

Definition (spectral power curve)The spectral power curve gives at each wavelength the power (inwatts), i.e., the rate at which energy is received from the light source

Definition (spectral reflectance curve)The spectral reflectance curve gives at each wavelength thepercentage of incident light that is reflected

refle

ctan

ce

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

700650600550500450400 nm Human complexion


Spectral color reproductionDefinition (spectral color reproduction)By spectral color reproduction we intend the physically correctreproduction of color, i.e., the duplication of the original object’sspectrum

The general reproduction methods (micro-dispersion andLippmann) are too impractical for normal useFor some special applications like painting restoration or illuminantreconstruction, the spectrum may be sampled at a small numberof intervals and combined with principal component analysisFortunately, spectral color reproduction is required only in rarecases, such as paint swatches in catalogs, and in this cases it isoften possible to use identical dyes

Our aim is to achieve a close effect for a normal viewer under averageviewing conditionsMathematically: build a simple model of color visionGiordano Beretta (HP Labs Palo Alto) SC076 Understanding Color Alexandria, someday 2010 51 / 207

Section Outline



Completing a wardrobe

Some observations:I If you want to buy a skirt or a pair of slacks to match a jacket, you

cannot match the color by memory — you have to take the jacketwith you

I Just matching in the store light is insufficient, you have to matchalso under the incandescent light in the dressing room and outdoors

I You always get the opinion of your companion or the store clerkThree fundamental components of measuring color:

I light sourcesI samples illuminated by themI observers


Color matching

Colors are assessed by matching them with reference colors on asmall-field bipartite screen


Color-matching functions I

Given a monochromatic stimulus Qλ of wavelength λ, it can be writtenas

Qλ = RλR + GλG + BλB

where Rλ, Gλ, and Bλ are the spectral tristimulus values of Qλ


Color-matching functions II

Assume an equal-energy stimulus E whose mono-chromaticconstituents are Eλ (equal-energy means Eλ ≡ 1)The equation for a color match involving a mono-chromatic constituentEλ of E is

Eλ = r(λ)R + g(λ)G + b(λ)B

where r(λ), g(λ), and b(λ), are the spectral tristimulus values of Eλ

Definition (color-matching functions)

The sets of such values r(λ), g(λ), and b(λ) are called color-matchingfunctions (CMF)


Color-matching functions III

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

b(λ)

g(λ)

r(λ)

700 600 500 400

nm

Stiles-Burch (1955;1959)


Section Outline



Metameric stimuli

Consider two color stimuli

Q1 = R1R + G1G + B1BQ2 = R2R + G2G + B2B

Definition (metameric stimuli)If Q1 and Q2 have different spectral radiant power distributions, butR1 = R2 and G1 = G2 and B1 = B2, the two stimuli are calledmetameric stimuli

FactColor reproduction works because of metamerism


Metameric stimuliMetamerism kit

0.0

0.1

0.2

0.3

0.4

0.5

0.6

D

C

B

A

700600500400

refle

ctan

ce

nm


Metameric stimuliKinds of metamerism

Illuminant metamerismI example: daylight and a D65 simulation fluorescent lamp

Object metamerismI example: metameric inks (see metamerism kit)

Sensor metamerismI example: scanner and human visual system

Observer metamerismI example: you and your neighbor

Complex metamerismI example: two inks metameric under two illuminants


Section Outline



Chromaticity diagramsWe can normalize the color-matching functions and thus obtain newquantities

r(λ) = r(λ)/[r(λ) + g(λ) + b(λ)]

g(λ) = g(λ)/[r(λ) + g(λ) + b(λ)]

b(λ) = b(λ)/[r(λ) + g(λ) + b(λ)]

with r(λ) + g(λ) + b(λ) = 1

Definition (spectrum locus)The locus of chromaticity points for monochromatic colors sodetermined is called the spectrum locus in the (r ,g)-chromaticitydiagram


(r ,g)-chromaticity diagram

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

-0.5

0.0

0.5

1.0

1.5

2.0

2° pilot groupStiles-Burch (1955)

r(m)

g(m)


Imaginary color stimuli

The fact that the color-matching functions and the chromaticitycoordinates can be negative presents a problem when thetristimulus values are computed from a spectral radiant powerdistributionBecause the color-matching space is linear, a lineartransformation can be applied to the primary stimuli to obtain newimaginary stimuli that lie outside the chromaticity region boundedby the spectrum locusThis ensures that the chromaticity coordinates are never negative


(x , y)-chromaticity diagram

nm0.0

0.5

1.0

1.5

2.0

z2(λ)y2(λ)x2(λ)

800700600500400

A: ~2856˚K

D65: ~6504˚K

∞

Planckian locus

spectrum locus


Section Outline



CIE 1931 standard colorimetric observerWe want to build an instrument delivering results valid for the group ofnormal trichromats (95% of population); since

R = k∫

Pλr(λ)dλ

G = k∫

Pλg(λ)dλ

B = k∫

Pλb(λ)dλ

an ideal observer can be defined by specifying values for thecolor-matching functions

Definition (CIE 1931 standard colorimetric observer)

The Commission Internationale de l’Éclairage (CIE) has recommendedsuch tables containing x(λ), y(λ), z(λ) for λ ∈ [360nm,830nm] in 1nmsteps


CIE 1931 Observer (cont.)

In addition to the color-matching properties, the CIE 1931Standard Observer is such that it has also the heterochromaticbrightness-matching properties. The latter is achieved bychoosing y(λ) to coincide with the photopic luminous efficiencyfunctionX and Z are on the alychne, which in the chromaticity diagram is astraight line on which are located the chromaticity points of allstimuli having zero luminanceThe data is based averaging the results

1 on color matching in a 2◦ field of 17 observers and2 the relative luminances of the colors of the spectrum, averaged for

about 100 observers


Section Outline



Tristimulus normalizationX ,Y , and Z are defined up to a common normalization factor.This factor is different for objects and for emissive sourcesThe perfect reflecting diffuser is an ideal isotropic diffuser with areflectance equal to unityThe perfect reflecting diffuser is completely matt and is entirelyfree from any gloss or sheen. The reflectance is equal to unity atall wavelengthsWhen the tristimulus values are measured with an instrument, YLrepresents a photometric measure, such as luminance. For objectsurfaces it is customary to scale X ,Y ,Z , so that Y = 100 for theperfect diffuser

I In practice a working standard such as a BaSO4 plate or a ceramictile is used in lieu of the perfect diffuser

For emissive sources there is no illuminant and therefore theperfect diffuser is not relevant. So it is customary to use thephotometric measures


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Objective color termsQuantities we can measure

Definition (Dominant wavelength)Wavelength of the monochromatic stimulus that, when additively mixedin suitable proportions with a specified achromatic stimulus, matchesthe color stimulus considered[In disuse, replaced by chromaticity]

y

x

0.8

0.2

0.4

0.6

0 0.2 0.4 0.6

700

450

460470

480

490

500

510

520530

540

550

560

570

580

590

600

610620

630

A: ~2856˚K

D65: ~6504˚K

∞

Planckian locus


Luminance

Definition (Luminance)The luminous intensity in a given direction per unit projected area

Lv = Km

∫λ

Le,λV (λ)dλ

where Km is the maximum photopic luminous efficacy (683lm ·W−1),Le,λ the radiance, and V (λ) the photopic efficiency

Definition (Luminance factor)The ratio of the luminance of a color to that of a perfectly reflecting ortransmitting diffuser identically illuminatedSymbol: β


Section Outline

4 Objective color termsY and chromaticityUniformityColor spaces sliced and diced


Y

Definition (Y stimulus)In the XYZ system the luminance depends entirely on the Y stimulus.The Y values of any two colors are proportional to their luminances.Therefore, Y gives the percentage reflection or transmission directly,where a perfectly reflecting diffuser or transmitting color has a value ofY = 100

Y = V

where V is the luminance of the stimulus computed in accordance withthe luminous efficiency function V (λ)

Called luminosity in some literatureApplication: conversion of a color image to black and white


Excitation purityDefinition (Excitation purity)A measure of the proportions of the amounts of the monochromaticstimulus and of the specified achromatic stimulus that, when additivelymixed, match the color stimulus considered

pc =x − xw

xb − xwor pc =

y − yw

yb − yw

where w denotes the achromatic stimulus and b the boundary colorstimulus

In disuse, replaced by chromaticityy

x

0.8

0.2

0.4

0.6

0 0.2 0.4 0.6

700

450

460470

480

490

500

510

520530

540

550

560

570

580

590

600

610620

630

A: ~2856˚K

D65: ~6504˚K

∞

Planckian locus


Chromaticity

Definition (Chromaticity)Proportions of the amounts of three color-matching stimuli needed tomatch a color

Relationship between chromaticity coordinates r(λ),g(λ),b(λ) andx(λ), y(λ), z(λ) of a given spectral stimulus of wavelength λ areexpressed by the projective transformation

x(λ) =0.49000r(λ) + 0.31000g(λ) + 0.20000b(λ)

0.66697r(λ) + 1.32240g(λ) + 1.20063b(λ)

y(λ) =0.17697r(λ) + 0.81240g(λ) + 0.01063b(λ)

0.66697r(λ) + 1.32240g(λ) + 1.20063b(λ)

z(λ) =0.00000r(λ) + 0.01000g(λ) + 0.99000b(λ)

0.66697r(λ) + 1.32240g(λ) + 1.20063b(λ)


Section Outline



UniformityThe X ,Y ,Z tristimuluscoordinates allow us to decideif two colors match in a givencontext. If there is no match, itdoes not tell us how large theperceptual mismatch is.Consequently, the CIE 1931chromaticity diagram is not aperceptually uniformchromaticity space from whichthe perception of chromaticitycan be derived.

x = X/(X + Y + Z )

y = Y/(X + Y + Z )

1 = x + y + z

Stiles Line ElementEllipses plotted 3 x

y

x

0.8

0.2

0.4

0.6

0 0.2 0.4 0.6

700

450

460470

480

490

500

510

520530

540

550

560

570

580

590

600

610620

630


Uniform chromaticity diagramThe CIE 1976 UCS (Uniform Chromaticity Scale) chromaticity diagramis perceptually uniform

u′ = 4X/(X + 15Y + 3Z ) = 4x/(−2x + 12y + 3)

v ′ = 9Y/(X + 15Y + 3Z ) = 9y/(−2x + 12y + 3)

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80.0

0.1

0.2

0.3

0.4

0.5

0.6

u'

v'

0.1 0.2 0.3 0.4 0.5 0.6 0.70

x0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

y

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

u0

0.1

0.2

0.3

0.4

0.5 v

Original MacAdam data, 10×


CIELAB1976 CIE L?a?b? color space

CIE 1976 lightness L?

A non-linear function to provide a measure that correlates withlightness more uniformlySimilar lightness distribution to Munsell Value scale

L? = 116 · 3√

Y/Yn − 16

Tangential near origin — when Y/Yn < 0.001:

L?m = 903.3YYn

forYYn6 0.008856


CIELAB (cont.)1976 CIE L?a?b? color space

Two color opponent channels a?,b?

a? = 500 ·{

3√

X/Xn − 3√

Y/Yn

}b? = 200 ·

{3√

Y/Yn − 3√

Z/Zn

}Tangential near origin — when X/Xn,Y/Yn,Z/Zn < 0.001Xn,Yn,Zn : reference white

D50 : (96.422,100.000,82.521)

D65 : (95.047,100.000,108.883)

von Kries type adaptation


Color difference formulæThe CIE has defined two uniform color spaces, 1976 CIE L?u?v?

and 1976 CIE L?a?b? in which the difference of two color stimulican be measuredu? and v? (but not a? and b?) are coordinates on a uniformchromaticity diagram. The third dimension is the psychometriclightness

C?ab =

√a?2 + b?2 hab = arctan(b?/a?)

∆E?94 =

√(∆L?

kL · SL

)2

+

(∆C?

abkC · SC

)2

+

(∆H?

abkH · SH

)2

SL = 1SC = 1 + 0.045 · C?

ab

SH = 1 + 0.015 · C?ab

kL = kC = kH = 1


Section Outline



Color spacescolor model operators

Device dependent spacesI counts received from or sent to a deviceI typically RGB counts or CMYK percentages

Device independent spacesI human visual system relatedI counts for an idealized device

Colorimetric spacesI analytically derived from the CIE colorimetry system

Uniform spacesI Euclidean, with a distance metric

Visually scaled spacesSpaces defined by an atlas


Colorimetric spaces

XYZ + basis for all other CIE color spaces– non-uniform

RGB + can be produced by additive devices+ linear transformation of XYZ– non-uniformexample:R

GB

=

0.019710 −0.005494 −0.002974−0.009537 0.019363 −0.0002740.000638 −0.001295 0.009816

XYZ

matrix elements are the primary colors

sRGB + contains non-linearity typical for PC CRTs+ easy to implement– non-uniform and non-linear


Colorimetric spaces (cont.)CIELAB + most uniform CIE space

+ widely used in the printing industry– cubic transformation

CIELUV + simple transformation of XYZ+ uniform+ related to YUV (PAL, SECAM)– less uniform than CIELAB

YIQ + used for NTSC encoding+ black and white compatible– contains gamma correction– non-uniform

YES, YCC + linear transformations of XYZ+ black and white compatible+ opponent color models– less uniform than CIELAB and CIELUV– YCC contains gamma correction– private standards


Colorimetric spaces (cont.)L?C?hab + has perceptual correlates

+ good for gamut mapping+ perceptually uniform– cylindrical– not uniform for compression

xvYCC + large gamut for HDTV with LED BLU (backlight unit)+ backwards compatible to sRGB

16

235254

16 240 254

Y

Cb, Cr1

65.0+5.0+5.0-

128

-0.57 Black

Over White

0 < R’,G’,B’ < 1

1< R’,G’,B’

R’,G’,B’< 0

(Gamut of BT.709-5)

Gamut of xvYCC

Extended

Extended Region

Extended Region

R’,G’,B’< 0

1< R’,G’,B’

Extended

BT.709-5(sRGB)

sYCC

xvYCC

0.0

1.0

Luma

Chroma

(sRGB)

1


Uniform color spaces

MunsellI perceptually uniformI based on atlas

CIELABI colorimetric

CIELUVI colorimetric

OSAI perceptually uniformI based on atlas

ColoroidI æstetically uniformI based on atlas


Visually scaled color spaces

MunsellI perceptually uniformI based on atlas

OSAI perceptually uniformI based on atlas

ColoroidI æstetically uniformI based on atlas

NCSI atlas with uniform coordinatesI not perceptually uniform


Color spaces defined by an atlas

MunsellOSAColoroidNCS

I Scandinavian, popular in EuropeRAL

I German, popular in EuropePantone

I popular in the U.S.A.

Many atlases defined by government agencies, industrialassociations, companies


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


IlluminationThe spectral power distribution of the light reflected to the eye by anobject is the product, at each wavelength, of the object’s spectralreflectance value by the spectral power distribution of the light source

500 700600400 500 700600400 500 700600400

500 700600400500 700600400 500 700600400

Incident SPD Reflected SPDReflectance curvex =

CWF

DeluxeCWF

Complexion

Complexion


Light sources of interest

At the beginning of color perception there is radiant energyTreatment in color science is slightly different from what welearned in high school physics — it can be limited to the visibledomainThe spectral power distribution of a tungsten filament lampdepends primarily on the temperature at which the filament isoperatedTypical average daylight has a color temperature of 6504◦K, whichcan be achieved also by Artificial Daylight fluorescent lamps,a.k.a. North-light or Color Matching lamps


CIE standard illuminantsDefinition (Illuminant A)CIE standard illuminant Arepresents light from a full (orblackbody) radiator at 2854◦K

Definition (Illuminant D65)CIE standard illuminant D65represents a phase of naturaldaylight with a correlated colortemperature of 6504◦K 0

50

100

150

200

250

300

D65

A

800 750 700 650 600 550 500 450 400 350 300

wavelength [nm]

rela

tive

radi

ant

pow

er

Fact (Illuminants B,C)CIE standard illuminants B and C were intended to represent directsunlight with a correlated color temperature of 4874◦K resp. 6774◦K.They are being dropped because they are seriously deficient in the UVregion (important for fluorescent materials)Giordano Beretta (HP Labs Palo Alto) SC076 Understanding Color Alexandria, someday 2010 96 / 207

CIE standard sourcesDefinition (Illuminant)Illuminant refers to a specific spectral radiant power distributionincident to the object viewed by the observer

Definition (Source)Source refers to a physical emitter of radiant power, such as a lamp orthe sun and sky

CIE illuminant A is realized by a gas-filled coiled-tungsten filamentlamp operating at a correlated color temperature of 2856◦KThere are no artificial sources for illuminant D65, due to the jaggedspectral power distribution. However, some sources qualify asdaylight simulators for colorimetryFor more information seehttp://www.mostlycolor.ch/2007/06/hot-body-excited-particles-and-north.html


http://www.mostlycolor.ch/2007/06/hot-body-excited-particles-and-north.html

http://www.mostlycolor.ch/2007/06/hot-body-excited-particles-and-north.html

Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Measuring color

There are no filters that approximate well the color matchingfunctionsThere are no artificial sources for the popular illuminants D65 andD50

Today’s hardware situation has changed dramaticallyI Embedded processors are inexpensiveI Holographic gratings are inexpensiveI Light sources are highly efficientI CCD sensors have much less dark noise

It is better to perform spectral measurements and let theinstrument do the colorimetrySpectroradiometer: determine the reflected SPDSpectrophotometer: determine the reflectance curveBecause they are a closed system, spectrophotometers are veryreliable


Trusting your instrumentSooner or later all users enter a deep trust crisis in their instruments.Some survival tips:

Illuminate your work area with a source simulating your targetilluminant

I see what the instrument “sees”Compact spectrophotometers have a very small geometry;perpendicularity between optical axis and sample, as well asdistance to the sample are critical

I maintain an uncluttered work spaceThe instrument’s light source generates heat, which increasesdark current noise in the CCD and causes geometric deformationsin the grating

I wait between measurementsI recalibrate

F at each session startF after each pauseF after a long series of measurements,F when the ambient temperature has changed by more than 5◦C


CalibrationWhite calibration: adjusts computational parameters so the calculatedtile’s reflectance curve is the same as the absolute reflectance curve

do it oftenAbsolute certification: verifies that the measured color of the tile iswithin the tolerance (e.g. 0.6∆E units) from the tile’s absolute color

important for agreement between laboratoriesRelative certification: verifies if the measured color of the tile is withinthe tolerance (e.g. 0.3∆E units) from the initial color of the tile with thesame instruments

important for reproducibilityCollaborative testing: verifies that the entire color measurementprocedure is in agreement with outside laboratories

Collaborative Testing Services Inc, 21331 Gentry Drive, Sterling,VA 20166, 571–434–1925http://www.collaborativetesting.com/


http://www.collaborativetesting.com/

Effect of variabilityA measurement is never perfectThe effect of variability of color measurement is reduced by usingmultiple measurementsHow many measurements should I make and average?Rule of thumb: 10× for each variability parameter

I instrument’s variability: measure each spot — 10×I sample uniformity: repeat at several locations — 100×I sample variability: repeat for several samples — 1000×I . . .

Follow ASTM standard practice E 1345 – 90 to determine howmany measurements are necessary in each case

I ASTM International, 100 Barr Harbor Drive, West Conshohocken,PA 19428-2959, 610–832–9585, http://www.astm.org

Improve all process aspects to minimize the required number ofmeasurementsISO 9001


http://www.astm.org

Geometries of illumination and viewingOn a glossy surface there are mirror-like (specular) reflectionsThere are more reflections in the case of diffuse light sourcesSince the color of the illuminant is white, specular reflections addwhite, with the effect of desaturating the colorNon-metallic glossy surfaces look more saturated in directionalthan in diffuse illuminationMatte surfaces scatter the light diffusely — matte surfaces usuallylook less saturated than glossy surfacesMost surfaces are between glossy and matteDiffuse illumination is provided by integrating spheres

I usually they are provided with gloss trapsInstruments with 45◦/0◦ and 0◦/45◦ geometry are less criticalASTM recommendation for partly glossy samples:

I use the geometry that minimizes surface effects (usually the onethat gives lowest Y and highest excitation purity)

45◦/0◦ geometry gives rise to polarization problems


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Section Outline

7 Spectral colorComputational colorMetamerism and Matrix RThe LabPQR interim connection space


Motivation

Examples when spectral color methods are required:MetamerismFluorescenceMedia and ink characterizationReproduction across illuminantsMapping from one device to anotherMore than 3 colorant hues (e.g., CMYKOGV)Scanner and camera characterization


Repetition of Standard Observer

R = k∫

Pλ · r(λ)dλ

means that the red color coordinate is obtained by integrating the SPDusing the red CMF for the measure, where

Pλ = E(λ) · S(λ)

is the product of the SPD of an illuminant E with the object spectrum S.Usually we are interested in the coordinates of various objects under afixed illuminant for a standard observer, so we reorder to

R = k∫

r(λ)E(λ) · S(λ)dλ


Discretization

In practice, the CMF are given as a table with 1nm steps, andinstruments measure at steps of 1,4,10,20nm etc., so in reality this isa summation [for red R]:

R = k∫

r(λ)E(λ)S(λ)dλ ≈ k∑

r(λi)E(λi)S(λi)∆λ

The integration resp. summation is over the visible range [380,780]nm,but in practice it is often over [380,730]nm for n = 36 samples

Instead of doing color science with measure theory, we can do itwith simple linear algebraIn 1991 H. Joel Trussell has made available a comprehensiveMatLab library and several key papers for color scientistsSince then, spectral color science is mostly done with linearalgebra


Formalism

We use the vector-space notationWLOG, let k = 1R = (R>E)S, G = (G>E)S, B = (B>E)SInstead of doing this for each of R,G,B or X ,Y ,Z , using linearalgebra we can write it as a single equation by combining the CMFin an n × 3 matrix A with the CMFs data in the columns:

Υ = (A>E)S

Sometimes we are interested in the color of a fixed object underdifferent illuminants, then we write

Υ = A>(ES) = A>η

η corresponds to the Pλ from earlier


Matlab, etc.

abcd


Section Outline



Fundamental and residual

How can we reconcile metamerism and color reproductiontechnology?In 1953 Günter Wyszecki pointed out that the SPD of stimuliconsists of a fundamental color-stimulus function η(λ) intrinsicallyassociated with the tristimulus values, and a residual called themetameric black function κ(λ)

κ(λ) is orthogonal to the space of the CMF


Matrix R theory

How does this translate to the discrete case?In 1982 Jozef Cohen with William Kappauf developed the matrix RtheoryUse an orthogonal projector to decompose stimuli in fundamentaland residualThe fundamental is a linear combination of the CMF AThe metameric black is the difference between the stimulus andthe fundamentalFor a set of metamers η1(λ), η2(λ), . . . , ηm(λ):

A>η1 = A>η2 = · · · = A>ηm = Υ


Development of matrix R

R is defined as the symmetric n × n matrix

Definition (matrix R)

R := A(A>A)−1A>

Matrix R is an orthogonal projectionA(A>A)−1 =: Mf , so R = Mf A> (remember: Υ = A>η)Because A has 3 independent columns, R has rank 3It decomposes the stimulus spectrum into fundamental η(λ) andthe metameric black κ:

η = Rηi

κ = ηi − η = ηi − Rηi = (I − R)ηi


Corollaries

Metameric black has tristimulus value zero

A>κ = [0,0,0]>

η = Rηi means that any group of metamers has a commonfundamental η, but different residuals κInversely, a stimulus spectrum can be expressed as

ηi = η + κ = Rηi + (I − R)ηi

i.e., the stimulus spectrum can be reconstructed if thefundamental metamer and metameric black are knownWhy is this useful?


Section Outline



Reducing the data

Storing a multidimensional vector for each pixel is expensiveCan we project on a lower-dimensional vector space?Yes, because the spectra are relatively smoothPopular technique: principal component analysisDue to the usually smooth spectra, the dimension can be quitelow: between 5 and 8

We have known how to deal with this for decades, it just requireslinearly more processing


The hard problem

We would like to use an ICC type workflow also for spectralimagingColorimetric workflow:

profile connection space 3-hue printerimage

The killer is the LUT used in the PCS:bands in bands out levels per band size [bytes]

3 6 17 30K

6 6 17 145M

9 6 17 700G

31 6 17 8 · 1027G


Interim Connection Space

Proposal by Mitchell Rosen et al. at RITIntroduce a lower-dimensional Interim Connection Space ICS

PCS to ICS

multi-hue printerscene

ICS to counts via low-dim. LUT


Choosing the basis vectors

Can we deviate from the usual PCA method of choosing thelargest eigenvectors and build on some other useful basis?When defining the basis vectors for XYZ, the new basis waschosen so that one vector coincides with luminous efficiency V (λ) compatibility of colorimetry with photometry1995 proposal by Bernhard Hill et al. at RWTH Aachen:incorporate three colorimetric dimensions compatibility of spectral technology with colorimetryhttp://www.ite.rwth-aachen.de/Inhalt/Documents/Hill/AachenMultispecHistory.pdf


http://www.ite.rwth-aachen.de/Inhalt/Documents/Hill/AachenMultispecHistory.pdf

http://www.ite.rwth-aachen.de/Inhalt/Documents/Hill/AachenMultispecHistory.pdf

The RWTH Aachen approach

X Y Z V 4 ............. V 16

S1 S2 S3 .............. S64

L* a* b*

L* a* b* V* 4 ............ V* 16

L bit abit b bit V 4,bit .......... V 16,bit

nonlinear transform

spectral scan values

conventionalthree channel

display or printersystem interface

S1 S2 S3 .............. S16

multichanneldisplay orprinting

smoothing inverse

basis functions

quantization

spectral reconstruction

multispectral values

nonlinear representationencoding

decoding


The RIT LabPQR approach

Mitchell Rosen et al. at RIT1 Calculate operator similar to matrix R using regression analysis

on a specific printer (unconstrained), or matrix R directly(constrained)

2 Calculate residual using principal component analysis3 Calculate tristimulus values XYZ4 Calculate PQR from residual (3 largest EV)5 Calculate LabPQR from XYZPQR using CIE equations


LabPQR notationReconstructed spectrum (LabPQR transform): P = TNc + VNp

I T : colorimetric transformationI Nc : tristimulus vector ΥI V : basis vectors in PQRI Np: residual

Constrained: Tck = A(A>A)−1 = MfI Remember: matrix R = Mf A>

Unconstrained: Tu = RN>c (NcN>c )−1 via least squares analysisover a number of tristimulus vectors for spectra R = ηi

Calculation of V : first 3 eigenvectors in metameric black κ viaprincipal component analysis

Conventional notation:

η = Rηi (= Mf Υ)

κ = ηi − η = ηi −Rηi = (I −R)ηi


LabPQR gamut


Using LabPQR

The diagram in the previous slide indicates how the algorithm isverifiedNote in particular the meaning of gamut mapping in PQR

The usage is to print a color chart and measure it spectrallyThe resulting table from device coordinates to spectra is then

1 converted to LabPQR2 inverted

The inverted table is used to interpolate LabPQR values to obtainthe device coordinates to reproduce a requested spectrum


Canon i9900 dye-based inks

G

K


Caveats

Green and black dyes tend to have an increasing reflectance inthe far redPaper brighteners act in the blue rangeRIT work: [400,700]nm for n = 31 samplesMost real world data: [380,730]nm for n = 36 samplesVisible range: [380,780]nm

The range has a strong effect on the principal components


PQR


Quality metric

objective function = minimize (CIEDE2000 + k ·∆PQR)


Accuracy of matrix R vs. unconstrained

What price in loss of accuracy do we pay for compatibilityconventional metamerism theory?

I Constrained model depends only on CMFI Unconstrained model additionally depends on device

Based on simulations (no LUT),I the constrained model is more accurate in generalI for a single fixed printer, the unconstrained method allows the use

of less principal components: LabPQ

Short spectral range [400,700]nm caused problems with green ink


Summary

1 Conventional ICC workflow is based on colorimetry2 A spectral workflow can can solve many more problems

I proof printingI fluorescenceI metamerismI . . .

3 LabPQR is low-dimensional and compatible with colorimetry


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Color reproduction

In most cases, color reproduction is simple and inexpensive becauseof metamerismSpectral color reproduction: equality of spectral reflectance or SPD

rarely neededpaint samples, metamerism assessment

Colorimetric reproduction: equality of chromaticities and relativeluminances

useful when viewing conditions are the same and light source isthe same

Exact reproduction: equality of chromaticities, absolute & relativeluminances

useful when viewing conditions are identical


Reproduction modes (cont.)

Equivalent reproduction: same appearance of chromaticities, absolute& relative luminances

useful when the luminance level is the sameCorresponding reproduction: same appearance of chromaticities andrelative luminances when the luminance levels are the same

current focus of research in color reproduction; CIECAMPreferred reproduction: achieve more pleasing reproduction ofmemory colors by departing from equality of appearance


Section Outline

8 Color reproductionAdditive and subtractive colorHalftoningScan — think — print


Additive and subtractive colorMixing of colored lights vs. mixing of colorants

Additive color: start with black and add primariesI red green blue (RGB)

Subtractive color: start with white and substract complements ofprimaries

I cyan magenta yellow (CMY)


The additive method

0.0

0.2

0.4

0.6

0.8

1.0 S-cone

M-cone

L-cone

Rod

650600550500450400

absorbance

nm

Probable sensitivity curves of the human eye and the three bestlights for additive color reproductionNote the strong overlap in the orange–yellow intervalThis means that correct color reproduction cannot be achievedwith simple trichromatic methods, because there are alwaysunwanted stimulationsHence, the trivial idea of stimulating the cones independently doesnot work with a simple approach


The subtractive methodThe additive method has two major disadvantages when theset-up is not light-emissive:

I the required filters significantly reduce the brightness of the imageI the reproduction of a mosaic can be tricky

It is easier to generate colors from a beam of white light andvarying the proportions of reddish, green, and bluish partsOn top to the unwanted stimulations, there is a problem withunwanted absorptions, making the subtractive method evenharder to master than the additive method

0.0

0.2

0.4

0.6

0.8

1.0

100%

50%

10%

700650600550500450400

0.0

0.2

0.4

0.6

0.8

1.0

1.2

100%

50%

10%

700650600550500450400

0.0

0.2

0.4

0.6

0.8

1.0

100%50%

10%

700650600550500450400


Dithering

Color is a usually represented with at least 8 bits per channel, for256 levelsSome devices can display less levels

I mobile LCD displays often have only 6 bits per channelI most printers have only 1 bit per channel

Displays: temporal ditheringPrinters: spatial dithering, a.k.a. halftoning


Section Outline



Scan — think — print

Because of the unwanted stimulations and absorptions, it ispractically impossible to engineer a color reproduction systembased on light and lenses producing satisfactory image qualityBecause of the large amount of data and lengthy computations,digital systems are possible only slowlyInitially, closed proprietary solutionsLater, open solutions based on standards and a colormanagement system

I SWOP inksI ICC profiles


Managed color reproduction

PhotoCDscanner

digitalcamera

graphic artsscanner

businessTIJ printer

digitalproof printer

platemaker ordirect press

graphic artsTIJ printer

workstationand archive

display andsoftcopy

spectro-photometer

raster imageprocessor

repository(database)

profile

ICC

profile

ICC

profile

ICC

profile

ICC

profile

ICC

profile

ICC

profile

ICC

ICC profile

ICC profile

profile maker

color rendering dictionaryICC profile

YCC

AdobeRGB

RGB

sRGB

RGB

CMYK

CMYK

CIELAB

RGB

anyCIELAB+sRGB

negative

positive

Internet


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Milestones in color printing

30,000 BCE hand is commonly used as a stencil by holding it againsta cave wall and blowing powder on it

1457 Fust and Schöffer use colored metal plates to print thePsalterium with colored initial letters. They had todiscover and solve the problems of color trapping andregistrationBreakthrough: mass-production of illuminated books

1580–1644 during the Ming dynasty, techniques are perfected for themass-production of multicolored book illustrations

ca. 1700 invention of the katagami stencil. The stencil’s looseelements are connected with silk wires fine enough thatink can flow around them, enabling the mass-productionof fine illustrations. Ukiyo-e — pictures of the floatingworld


Color printing milestones (cont.)1719 Le Blon receives British patent 423 for inventing the

trichromatic printing principle. Yellow, red, blue plus blackfor better gray balance and clean blacks

1797 Senefelder invents lithography, enabling the inclusion of alarge number of illustrations in very long run books likethe Encyclopédie

1816 Engelmann invents chromolithography; 6 to 19 partialcolors, sometimes even 24 and 30

1816 Young invents color filters, which will allow to separatecolor images

1852 Fox Talbot invents concept of halftone screening1879 Swan invents line screen1888 Meisenbach invents crossline screen1910 invention of the panchromatic film emulsion, allowing the

use of Maxwell’s filters


Color printing milestones (cont.)

From here on all effort goes into color correction (masking)1937 Neugebauer proposes an eight-color analytical method

based on colorimetry1948 Hardy and Wurzburg invent the scanner — electronic

circuitry is used to determine the color correction in onesingle step

FactThe 1941 Murray and Morse scanner just tried to simulate maskingHardy and Wurzburg’s solved the Neugebauer equations

1957 Patent 2,790,844 — early effort towards gamut mapping1977 Ichiro Endo receives U.S. patent 4,723,129 for thermal ink

jet technology1987 Canon launches CLC-1 color copier


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Color image communication

Application

Protocol

Format

Compression

Color image


Section Outline

10 Color image communicationColor imagingCompression methodsMixed Raster ContentInternet faxInternet printingDocument ecosystems


Color imagingApplication

Protocol

Format

Compression

Color image

Requirement for digital color imaging

The total size of a page should be such it can be transferredquicklyTherefore, the color space must compress well


Luma-chroma spaces

LC1C2

= A ·

fR(R)fG(G)fB(B)

XYZ NTSCRGB

EBURGB

SMPTERGB

CCIR709 sRGB

CIELAB

YIQ YUV

YES PhotoYCC

YC1C2


RGB separations

R

BG

Allow quick display — no processing necessaryUnsuitable for color image communication — separations notde-correlated


CIELAB separations

*b*a

L*


Chroma subsampling

L*

a*b*


Uniform discretization errors

Cartesian coordinatese.g., CIELAB:

Cylindrical coordinatese.g., L?C?hab:


Section Outline



Compression methods

Lossless codingPalette colorPerceptually lossless codingMixed contents


Lossless codingHuffman codingArithmetic codingLZ codingLZW coding (USP 4,558,302)Flate and deflate (IETF RFC 1951)Binary image compression

I Group 3 1-d (MH) and 2-d (MR)F ITU-T Rec. T.4

I Group 4 (MMR)F ITU-T Rec. T.6

I JBIG — progressive bi-level image compressionF ISO 11544 / ITU-T Rec. T.82F ITU-T Rec. T.85 — application profile for faxF ITU-T Rec. T.43 — bit-plane coding for color fax images using JBIG

I JBIG2 — lossy/lossless coding for bi-level imagesF ISO 14492 / ITU-T Rec. T.88F text halftone, and generic modes

Lossless JPEGLossless JPEG 2000


Palette colorCounting colors

24-bit pixels can represent 16 millioncolorsHumans can distinguish 10 millioncolorsA 2× 3K image contains 6 millionpixelsA 512× 512 image contains 250thousand pixelsA “typical” 5122 image has 26thousand colorsOne byte can represent 256 colors


Color palettes (mapped color)Represent original colors by indices into a map with reduced setof colors (paint by numbers)

I choose N colors (palette)F image dependent (adaptive) or image independent (fixed)F e.g., median cut

I quantize (map) original to palette colorsI use look-up table to map index to palette colorI may use dither in palettized image

quantizeQ xednilanigiro


JPEG

No color space specificationBaseline JPEG: 4 or less color components

I colorimetric color representation is possibleFull JPEG: 256 or less color components

I discrete spectral color representation is possible

Compression can be improved with chroma sub-sampling

JPEG 2000

Wavelet-based follow-on to JPEGI same committee, different contributors

Single compression architectureI continuous-tone and binary compressionI lossy, lossless, and lossy-to-lossless codingI progressive rendering

1–256 color (spectral) components


Section Outline



Mixed Raster ContentBackground

in1in2

out

black-and-whitetext and linediagrams

in1in2

out


black-and-whitetext, halftones,stipples, line art,and so on

PSTN

T.4

MH

T.6

MMR

T.85

JBIG

T.42

JPEGCIELAB

T.43

JBIGCIELAB

Multiple, independent compression methods—each optimized for one kind of image content


Mixed Raster ContentSolution

black-and-whitetext & digramsas before,

interchange

T.44

MixedRaster

Content

MRC is a method for using multiple compression methods in raster documents that contain multiple kinds of content

color text andgraphics

black-and-whitetext, halftones,stipples, line art,and so onin1

in2out


coloredtexttoo


Mixed Raster ContentOverview

MRC = Mixed Raster ContentI multi-layer model for representing compound imagesI described in ITU-T Recommendation T.44

F originally proposed in joint Xerox/HP contributionI efficient processing, interchange and archiving of raster-oriented

pages with a mixture of multilevel and bilevel imagesTechnical approach

I segmentation of an image into multiple layers (planes), by imagecontent

I use spatial resolution, color representation and compressionmethod matched to the content of each layer

Compound image architectureI framework for using compression methods

PerformanceI can achieve compression ratios of several 100 to 1 on typical

documents


Mixed Raster ContentModel

black-and-white

text & digrams

colored text

blackred

black-and-whitetext & digrams

colored text

Image3-layer model

ForegroundI multilevel, e.g., text colorI JBIG 12 bpp, 100 dpi

MaskI bilevel, e.g., text shapeI MMR 1 bpp, 400 dpi

BackgroundI multilevel, e.g., contone im.I JPEG 24 bpp, 200 dpi

Image = M · FG + M ′ · BG


Section Outline



Internet faxWhat is it ?

Store-and-forward Internet faxI scanned document transmission using e-mail attachmentsI ITU-T standards and IETF protocolsI uses ESMTP with delivery confirmation and capabilities exchange

ITU-T Recommendation T.37 — approved September 1999I references IETF standards

F requires use of TIFF-FXI Simple Mode — TIFF-FX Profile S: April 1999I minimal b&w with no delivery confirmation or capability exchangeI Full Mode — TIFF-FX all profiles: September 1999

F range of b&w and color with delivery confirmation and capabilityexchange

Best write-up: RFC 2542 by Larry Masinter (see bibliography)


Internet faxConfigurations

Internet

PSTN

workstation

all-in-one

faxon/off ramp


Internet faxAdditional information

Latest version of file format: RFC 3949 (February 2005)http://www.ietf.org/rfc/rfc3949.txt

2 International Telecommunication Union (ITU) standardsI ITU-T T.37 fax via emailhttp://en.wikipedia.org/wiki/T.37_(ITU-T_recommendation)

I ITU-T T.38 real time fax over IPhttp://en.wikipedia.org/wiki/T.38


http://www.ietf.org/rfc/rfc3949.txt

http://en.wikipedia.org/wiki/T.37_(ITU-T_recommendation)

http://en.wikipedia.org/wiki/T.37_(ITU-T_recommendation)

http://en.wikipedia.org/wiki/T.38

Section Outline



IPP — Internet Printing ProtocolWhat is it ?

Firewall problemIETF standard developed with help from the Printer WorkingGroupClient-server protocol for distributed printing on the Internet

I intended to replace LPR/LPDUses HTTP 1.1 POST application protocol

I Internet media type: application/ipp


IPP — Internet Printing ProtocolSample configurations

Client to printerclient IPP object

IPP

Client to server

IPP

client IPP object


Section Outline



Document ecosystemsSeamless office imaging

Scanners, copiers, connected to Ethernet instead of computerDocuments distributed via e-mail, fax servers, remote printers, orISV applications

HP 9100C WindowsServer

ImagingApplication

Digital Sender

HP 9100CService

TCP/IP

ApplicationServer

SharedDisk

write read

NOTIFY.DAT

image +metadata


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Color appearance modeling

Recommended model: CIECAM02Do not use an appearance model when

I stimulus specification is simple (CIELAB, sRGB, . . . )I simple color tolerances (CIE94)I only one viewing conditionI it is not clear it will help

What they allow you to doI map from measurements to color namesI predict color matches across viewing conditions

F render color across mediaI gain a deeper understanding of colorI no metric for color differences


Cognitive context

amber


context parameters

Color lexicon

color nameaction


color

…

internallightness

hue

chromaetc.

lightness

hue

chromaetc.


color space


CIECAM02Conditions modeled

I adaptationI discounting the illuminantI surround effects

Predictions missing from the modelI rod contributionsI color difference metricI constant hue linesI Helson-Judd effectI Helmholtz-Kohlrausch effect

Graphical representationI CIECAM02 is represented in cylindrical coordinates

F lightness JF chroma CF hue h

I trigonometric transformation necessary for plots

Includes the 5 years of revisions since CIECAM97s


The color selection problem

Surround

Background

AdaptingProximal field

2º

10º

Color

field

considered

This user interface problem cannot be solved without colorappearance modelCurrently users converge towards their intended rendering by trialand error


The gamut mapping problem

Monitor

Printer

CG Image

a*

b*

Gamut compression

Modify appearance (L*C*hab)

Compute colorant quantities

Measure original

Compute appearance


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Local vs. global color differences

Stiles Line ElementEllipses plotted 3 x

y

x

0.8

0.2

0.4

0.6

0 0.2 0.4 0.6

700

450

460470

480

490

500

510

520530

540

550

560

570

580

590

600

610620

630

.55

.45

.35

.25

.15.05 .15 .25 .35 .45

v’

u’

David L. Post, 1988

green

white yelloworange

red

peachaqua

graypink

bluepurple

1. 1. 1.

1..98 .92 .65 .53

.99 .98 .97 .88 .62.97 .91 .94 .84 .66 .5.71 .71 .68 .7 .44

.33

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.39

.45

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.51

.61.53.53 .47.47

.47.52.41.45

.64

.56 .43.56

.56

.53

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.39

.45.45

.47.48.28

.37.38

.36

.35

.53.64 .5

.62.75 .53

.74.82.53

.9.84 .48.92.89.53

.98 .9.52

.98 .9 .53

.97 .9

.96.91 .6

.97.92.63

.97.94

.97

.32.46.56.68.76 .74.75.58 .35.53 .7 .8 .85.87.82.74.52.74.81.87.91.89.86

.33.57.69.83.92.88.87.57 .7 .82.86.88.56.68.72.74

.51.63

.5

.48.44

.56.56.47.54.67.77.59.48.7 .83 .8 .72.65.82.92 .9 .75.69.83.93.89.81.83.94.92

.52.91.95

.84B = CIE Standard Illuminant B

E = equal-energy pointD = CIE Standard Illuminant D65C = CIE Standard Illuminant C

A = CIE Standard Illuminant A

A

BED

C


Where global differences are used

avionics and car navigationreadability of colored text on colored backgroundvariable data publishinggamut mapping, especially from xvYCC and HDR sourcescolor communication“graphic artist in a box” applications“good enough” coloronline print quality control. . .


Bumps in color spaceDefinition (Categorical Perception)A categorical perception effect occurs when

1 A set of stimuli ranging along a physical continuum is given onelabel on one side of a category boundary and another label on theother side and

2 The subject can discriminate smaller physical differences betweenpairs of stimuli that straddle boundary than between pairs that areentirely within one category or the other

.55

.45

.35

.25

.15.05 .15 .25 .35 .45

v’

u’

David L. Post, 1988

green

white yelloworange

red

peachaqua

graypink

bluepurple

1. 1. 1.

1..98 .92 .65 .53

.99 .98 .97 .88 .62.97 .91 .94 .84 .66 .5.71 .71 .68 .7 .44

.33

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.39

.45

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.51

.61.53.53 .47.47

.47.52.41.45

.64

.56 .43.56

.56

.53

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.39

.45.45

.47.48.28

.37.38

.36

.35

.53.64 .5

.62.75 .53

.74.82.53

.9.84 .48.92.89.53

.98 .9.52

.98 .9 .53

.97 .9

.96.91 .6

.97.92.63

.97.94

.97

.32.46.56.68.76 .74.75.58 .35.53 .7 .8 .85.87.82.74.52.74.81.87.91.89.86

.33.57.69.83.92.88.87.57 .7 .82.86.88.56.68.72.74

.51.63

.5

.48.44

.56.56.47.54.67.77.59.48.7 .83 .8 .72.65.82.92 .9 .75.69.83.93.89.81.83.94.92

.52.91.95




A

BED

C.55

.45

.35

.25

.15.05 .15 .25 .35 .45

v’

u’

David L. Post, 1988

green

white yelloworange

red

peachaqua

graypink

bluepurple

1. 1. 1.

1..98 .92 .65 .53

.99 .98 .97 .88 .62.97 .91 .94 .84 .66 .5.71 .71 .68 .7 .44

.33

.73 87.91.87 .53.71.59.73.74.57.33.46.44.31

.75.88.96.98.58.54.63.59 .5

.39

.45

.49.63.78.81.63.47.33.35.56.61.61.52

.98.96.93.52

.51

.61.53.53 .47.47

.47.52.41.45

.64

.56 .43.56

.56

.53

.48 .3

.57.56.44

.77.63.38

.82.65.36

.69.42

.39

.45.45

.47.48.28

.37.38

.36

.35

.53.64 .5

.62.75 .53

.74.82.53

.9.84 .48.92.89.53

.98 .9.52

.98 .9 .53

.97 .9

.96.91 .6

.97.92.63

.97.94

.97

.32.46.56.68.76 .74.75.58 .35.53 .7 .8 .85.87.82.74.52.74.81.87.91.89.86

.33.57.69.83.92.88.87.57 .7 .82.86.88.56.68.72.74

.51.63

.5

.48.44

.56.56.47.54.67.77.59.48.7 .83 .8 .72.65.82.92 .9 .75.69.83.93.89.81.83.94.92

.52.91.95




A

BED

C


Previous attempts: ISCC–NBSMunsell Value

Munsell C

hroma

black dark gray medium gray light gray white

–ish black dark –ish gray –ish gray light –ish gray –ish white

blackish darkgrayish grayish

paleor

light grayishvery pale

very dark dark moderate light very light

very deep deep strong brilliant

vivid


Previous attempts: Coloroid by Nemcsics

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

T

VA = 20

Anatolian brown

cement greybroken warm white

Roman ochre

brown beige

Arsigont

Pompeian yellow

orange ochreIndian orange


Previous attempts: World Color Survey

yellow

red

greenyellow

green

blue brownwhiteandblack

orangeand/orpinkand/orpurpleand/orgray

I II III IV VI VIIV


Previous attempts: Zollinger

60%

20%

40%

Häufigkeit

5R 5RP5P5PB5B5BG5G5GY5Y5YR

rotgelb

orange

braun

grünblau violett

rot

rosa

purpur

ETH-Zürich


Previous attempts: Boynton & Olsong

j

green

yellow

orange

brown

pinkred

blue

purple

whitegrey


New attempt: crowd-sourcingGood news: viewing conditions are not critical

I Boynton, “Insights gained from naming the OSA colors,” 1997I Olkkonen, Hansen, Gegenfurtner, “Categorical color constancy for

simulated surfaces,” Journal of Vision, 9(12):6, 1-18, 2009

Bad news: the categories that arise in the left hemisphere are notconstrained by the pre-linguistic categories that are in the righthemisphere (Franklin et al., 2008)

adultswithin-categorybetween-category

visual fieldleft right

initia

tion t

ime

[ms]

550

250

350

450

infantswithin-categorybetween-category

visual fieldleft right

initia

tion t

ime

[ms]

900

500

600

700

800


Zollinger in Tokyo

60%

20%

40%

Häufigkeit


rotgelb

orange

braun

grünblau violett

rot

rosa

purpur

ETH-Zürich

60%

20%

40%

かいすう


80%

オレンジ

TKD-東京

朱あか黄き

茶ちゃ

橙ダイダイ

緑みどり青あお

水色

みずいろ

紫むらさき

ピンク

桃色

ももいろ


Multilingual color naming

gelb

rosa

blau

grün

rot

grau

あおあか

はいいろむらさき

みどり

き


Use a common standard

#007CB0#EF4123

#848688

#BF1E74

#F89F6D

#008F4C

#F499B8

#007CB0


Munsell Sheets of Color are a better standard

Eliciting color names from actual samples provides a commonstandard, which is not available when compiling abstract lists

Color names must be anchoredEverybody should use the same atlas

. . . but the complete gamut should be used, not just the surface!


Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Color image communication synopsis

Web Browsers Image TransferApplication

Protocol

Format

Compression

Color Space

HTTP *TP

HTML

GIF PNG JFIF

LZW flate JPEG

palette

RGB sRGB YCbCrICCprofile

other formatsvia plug-ins

e.g., PDF,TIFF, SVG

IIP *TP

FlashPix JP2

JPEG JPEG 2000

sRGB Photo-YCC

sRGB,Gray

simpleICC profile

InternetFax

InternetPrinting

ESMTP IPP

TIFF-FX

JPEG

MRC

binary

supporteddocumentformats

ProfileC

ProfileM

CIELABApplication

Protocol

Format

Compression

Color image


Color space supportby file format

LZW flate JPEG

GIF device RGB n/a n/a

PNG n/a device RGB, sRGB n/a

JFIF n/a n/a YCbCr

FlashPix n/a n/a PhotoYCC, sRGB

TIFF-FX Profile C n/a n/a CIELAB

PDF dev. RGB, dev. CMYK, cal. RGB, CIELAB, XYZ, ICC profiles

LZW, flate for text, graphics, and indexed imagesJPEG for images


Conclusions

What you should take home from this course:I a more realistic expectation from color reproductionI color is more an art than a science

F practice, practice, practiceF develop your intuition

I color reproduction algorithms could not be patented before the late80s

F prior art is in the literature, not in the USPTOF algorithms often wrapped in an apparatus

I how to interpret the result of a color measurementI how to trust your instrument

Acknowledgements:I collaboration with Robert R. Buckley, Xerox CorporationI metamerism test kits donated by X-Rite

www.hpl.hp.com/personal/Giordano_Beretta/


www.hpl.hp.com/personal/Giordano_Beretta/

Outline

1 Introduction

2 Color theories

3 Terminology


5 Illumination

6 Measuring color

7 Spectral color





12 Cognitive color

13 Conclusions

14 Bibliography


Bibliography

R.S. Berns. Billmeyer and Saltzman’s Principles of ColorTechnology. 3rd edition, John Wiley & Sons, New York, 2000CIE Publ. No. 17.4. International Lighting Vocabulary. 1987J. Davidoff. Cognition through Color. The MIT Press, Cambridge,1991M.D. Fairchild. Color Appearance Models. 2nd edition, John Wiley& Sons, Hoboken, 2005J.-P. Homann. Digital Color Management: Principles andStrategies for the Standardized Print Production. Springer-Verlag,2009, ISBN: 978-3-540-67119-0G.A. Gescheider. Psychophysics. Lawrence Erlbaum, Hillsdale,1985E.J. Giorgianni and Th.E. Madden. Digital Color Management.Prentice Hall PTR, 1998, ISBN: 0201634260


Bibliography (cont.)

R.W.G. Hunt. Measuring Colour. 3rd edition, Fountain Press,Kingston-upon-Thames, 1998R.W.G. Hunt. The Reproduction of Colour in Photography, Printing& Television. 6th edition, John Wiley & Sons, Hoboken, 2004,ISBN: 0-470-02425-9R.S. Hunter and R.W. Harold. The Measurement of Appearance.2nd edition, John Wiley & Sons, New York, 1987H.R. Kang. Color Technology for Electronic Imaging Devices.SPIE, Bellingham, 1997H.R. Kang. Digital Color Halftoning. SPIE, Bellingham, 1999H.R. Kang. Computational Color Technology. SPIE, Bellingham,2006, ISBN: 0-8194-6119-9



Helga Kolb et al. Webvision—The Organization of the Retina andVisual System. http://webvision.med.utah.edu/R.G. Kuehni. Color: An Introduction to Practice and Principles.John Wiley & Sons, Chichester, 2000L. Masinter. RFC 2542. Terminology and Goals for Internet Fax.1999, http://www.ietf.org/rfc/rfc2542.txtJ. Morovic. Color Gamut Mapping. Wiley IS&T Series in ImagingScience and Technology. John Wiley & Sons, Chichester (WestSussex), 2008.G. Sharma, Editor. Digital Color Imaging. CRC Press, BocaRaton, 2003, ISBN: 0-8493-0900-XR.L. van Renesse. Optical Document Security. Artech House,Boston, 2005


http://webvision.med.utah.edu/

http://www.ietf.org/rfc/rfc2542.txt


G. Wyszecki and W.S. Stiles. Color Science: Concepts andMethods, Quantitative Data and Formulæ. 2nd edition, John Wiley& Sons, New York, 2000, ISBN: 0-471-39918-3Y.-J. Zhang. Image Engineering. Processing, Analysis, andUnderstanding. Cengage Learning Asia, Singapore, 2009, ISBN:978-981-4239-63-9H. Zollinger. Color: A Multidisciplinary Approach. HelveticaChimica Acta, Zurich, 1999, ISBN: 3-906390-18-7


Quiz

Why is white balancing necessary in color reproduction?

Optictract Lateral

geniculatebody Optic

radiations

Primaryvisualcortex

Blob

amber


context parameters

Color lexicon

color nameaction


color

…

internallightness

hue

chromaetc.

lightness

hue

chromaetc.


color space


Questions and Discussionmailto:[email protected]://www.hpl.hp.com/personal/Giordano_Beretta/http://www.mostlycolor.ch/
