The Consideration of Camera Testing for Accurate Color Reproduction Within Museums and Institutions

Rochester Institute of Technology

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2006

The Consideration of Camera Testing for Accurate

Color Reproduction Within Museums and

Institutions

Natalie Russo

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School of Print Media

Rochester Institute of Technology

Rochester, New York

Certificate of Approval

Master's Thesis

This is to certify that the Master's Thesis of

Natalie Russo

has been approved by the Thesis Committee as satisfactory

for the thesis requirement for the Master of Science degree

at the convocation of

May 2006

Thesis Committee:

Franziska Frey

Primary Thesis Advisor

Patti Russotti

Secondary ThesIs Advisor

Illegible Signature

Graduate Thesis Coordinator

Illegible Signature

Graduate Program Coordinator

Illegible Signature

The ConsiderationofCamera

Testing

for Accurate ColorReproduction

Within MuseumsandInstitutions

by

Natalie Russo

Athesis submittedinpartialfulfillmentoftherequirements

forthedegreeofMasterofScience

intheSchoolofPrint Media

intheCollegeof

Imaging

ArtsandSciences

oftheRochester Instituteof

Technology

2006

Primary

ThesisAdvisor: ProfessorDr. Franziska

Frey

Title of Thesis:

Thesis Release Permission Form

Rochester Institute of Technology

School of Print Media

The Consideration of Camera Testing for Accurate Color Reproduction

Within Museums and Institutions

I, Natalie Russo, hereby grant permission to the Wallace Memorial Library of RIT

to reproduce my thesis in whole or in part. Any reproduction will not be for commercial

,

use or profit.

Natalie Russo

Signature of Author

Acknowledgments

My

sincere appreciation goes toTheSchool ofPrint MediaatRochesterInstitute

of

Technology,

toProfessor Dr.Franziska

Frey

forallherassistance and_guidance, andto

Professor Patti Russotti for her never-endingsupport and encouragement. Iwould also

like tothankNitin

Sampat,

Allen

Phillips,

andMichael Hansen for their_ongoingsupport.

Iwouldliketo thank theYamatoya/Numakura Graduate Research

Fellowship

FundandthePrint

Industry

Center fortheirfinancial support giventome while_working

onthisresearch.

I wouldalsoliketoacknowledge andthank_myparentsfor

believing

in meand

for providingme with the tools_necessaryto succeed;to them, Iamforevergrateful.

Lastly,

I am also grateful_{for my}animal companions who make_{my life}complete.

TableofContents

Abstract x

Chapter 1: IntroductionandStatement oftheProblem 1

StatementoftheProblem 1

BackgroundandSignificance 2

Reason for Interest 4

Chapter 2: Literature Review 6

Digital

Imaging

WithintheMuseum Environment 6

RIT

Benchmarking

Conference

Key

Findings 9

RIT

Benchmarking

Conference

Defining

Current Needs 13

Color Reproduction 17

Color Management 29

Photographic Considerations 33

The Digital Scanback Camera 39

Software

Technology

45

StandardsandCamera

Testing

46

Testing

aCamera's

Quality

52

Monitor Calibration Standards 53

Metadata 54

Workflow Guidelines 55

Chapter 4:

Methodology

60

Chapter5:

Finding

63

Camera

Testing

63

The Tests in Greater Detail 69

Metadata 86

Usability

87

Museum

Photography

89

Museum Photographers 92

Chapter 6:

Summary

and Conclusions 96

The Four Selected Tests 96

Should Camera Dealers Do Testing? 99

Testing

Services 99

Museum Conclusions 100

Camera

Testing

Conclusions 101

Chapter 7: Recommendationfor Further Research 102

Bibliography

103

Appendix A: A Case

Study

- The Digital

Imaging

Workflow ofPhotographer Allen PhillipsattheWadsworth Atheneum Museumof

Art,

Hartford,

CT 106

ListofTables

Table 1:The

Benchmarking

AmericanMuseum Project

-Key

Findings 10

Table 2: The

Benchmarking

AmericanMuseumProject Suggested Future Research 14

Table 3a: Wueller's

Mandatory

Testing: AComparisonofCamera Tests 67

Table 3b: Wueller's Recommended Testing: A ComparisonofCamera Tests 68

Table4: Museum

Photography

Variables 91

Table5: Knowledge andSkill Sets Needed for Museum Photographers 94

Table6: Recommended Camera Tests 98

Table BI: List ofstandardsthatare summarizedinthis standardsreview 131

Table BII: Areferencetableforthe_{digital image quality}parameters ofthediscussed

ListofFigures

Figure 1: Management wheelfor digital

imaging

projects 8

Figure 2: The Visual Spectrum ₁₈

Figure 3:

(a)

Theprobable _sensitivitycurvesforthethreetypesof_cones, withthe

threebest R. G. and

B,

lightsforadditive color reproduction,

(b)

Typical spectral

sensitivitycurves foundfrom

bleaching

experimentswiththehumanretina 23

Figure 4: Color matchingfunctionsoftheCIE2standard observer 25

Figure 5: Exampleofnegative color_matching

functions,

_aidingtocolor reproduction

inaccuracies 27

Figure6: Spectral sensitivities ofatypicalscanback camera 28

Figure 7: Spectral sensitivities ofatypical CCDcamera 28

Figure 8: The LAB color space represents

lightness, hue,

and saturation 30

Figure 9: Profile Connection Space as partof an open

loop

workflow 33

Figure 10: Trilinearcolor

imaging

sensor 40

Figure 11: Functional block diagramoftheKodaktrilinear_arraysensor 44

Figure 12: Single-channel block diagramoftheKodaktrilinear_array sensor 44

Figure Al:

Blume,

Peter,

The ItalianStraw

Hat, 1952,

Oilon

board,

The Wadsworth

Atheneum Museumof

Art,

600 Main

Street, Hartford,

CT

06103,

The

Henry

Schnakenberg

Fund,

1955.32 1 10

Figure A2: RearmountedDaylight Infrared filter Ill

Figure A3: RearmountedDaylight Infrared filter 1 1 1

Figure A4: Johnson Magnetic AngleLocator 1 12

Figure A5: BetterLight

Focusing

Aid 112

FigureA7: LaCie Calibrationmenu 1 13

Figure A8: Angle locatorand camera _{1 15}

Figure A9:

Focusing

aids at either corner of_painting 115

Figure A10: BetterLightcamerahard drive 1 16

Figure Al 1:

Inserting

theBetterLight digital back intothe4x5camera 1 16

Figure A12: Theprescannedimage 117

Figure A13:

Focusing

rectangle 117

Figure A14:

Focusing

window 117

Figure A15: Thefinefocus knobonthecamera 1 17

Figure A16: ColortabintheViewFindermaincontrolwindow 118

Figure A17: Spotmeterspot on eachofthefourmiddle_graypatches 118

Figure A18: Neutralized graypatchinformation 1 18

Figure A19: Tonetabin ViewFinderwindow 119

Figure A20:

Saving

thecustom curve 119

FigureA21: Kodak Color Flow Profile Editor 2.1 software 121

Figure A22:

Opening

therespectivefile 121

Figure A23: Selective Color 122

Figure A24: Color

Editing

window 122

Figure A25: Eye droppertool 122

Figure A26:Selectedcolor 122

Figure A27: Color beforeadjustment 123

Figure A29: Colorcorrectedimage witheditlist 124

Figure A30:

Saving

ICCprofile 124

Figure A31: ICCprofile asitappears onthe

desktop

124

Figure A32:

Moving

the profileto theColorSync folder 124

Figure A33:

Moving

theprofiletotheColorSync folder 124

Figure A34: Selectpreferencestofindprofile 125

Figure A35: Selectprofile 125

Figure A36:

Verify

theproper profileisselected 125

Figure A37: Set high-resolution image scan 125

Figure A38:

Scanning

thehigh-resolution digital image 126

Abstract

Theneed forcolor accuratedigitalimagesfor fineart reproductionisa_reality

faced

by

art museums andinstitutionsaroundtheworld. Itisthe goalofmostinstitutions

toreplace old,sometimes used andabused,

transparency

archiveswithdigital image

archives;_{many institutions have already begun}thiswork. Anumber ofdifferent

imaging

systems withvaried_workflows,some

idiosyncratic,

are

being

usedforthiswork. There

is,

therefore, an urgent needforthedevelopmentand_sharingof_workingmethodsthat

will ensure_{efficient, consistent,}and color accurate results. Theseworkflows must use

camera systemstotheirfullestcapability.Itseemsclear,then,thata complete

understandingofthe_qualitymetrics usedto assess_currentlyavailable camera systemsis

the_{necessary first step in}thedevelopmentof_newly suggestedworkflows.

Thegoal ofthisresearchprojectwastoconsider all ofthesteps_{necessary for}

photographerstocreatecolor accurateimages offineart_{for both archiving}and

reproductionpurposes, andtointroducecamera

testing

asthe new_startingpointfor

digital

imaging

workflows.

Introducing

camera

testing

as part of

imaging

workflows will

assist photographers withtheir

imaging

endeavors

by

_enablingthem tounderstandwhat

qualitymetrics shouldbeconsidered when _workingwithdigital

imaging

systems.

Thisresearch wasrestricted to thecurrentlyavailable colorimetric

imaging

devices. Oneofthemain concerns withmostdigital camerasisthat their"spectral

sensitivities are not lineartransformationsof an averagehumanvisual system's spectral

sensitivities. Thisis the_underlyingreason_whycolorinaccuraciesexistin digitalimages"

(Smoyer, Taplin, Berns, 2005,

p. 4). Thisresearchhas been focusedon themost

affective and efficient waysformuseum photographersto accommodateforthis

inaccuracy.

Thisresearch began

by

_consideringa_groupofcameratests_{previously introduced}

tomuseums andinstitutions

during

a research project entitledDirect Digital Image

Capture of Cultural Heritage:

Benchmarking

American Museum Practicesand

Defining

Future Needs. Thegoalofthisresearch wastoconsider all oftheteststhatwere

introduced,

andtonarrowthe

testing

criteriadowntofourorfivetests. This was

completed andthe

key

finding

suggestthat traditional photographic_qualitymetrics

remainthemostimportantparameters totest

for,

inordertoensure _{high quality}

photographic reproductions.

Thus,

the

key

findingsofthisresearchrecommendthat tone

reproduction,spatial

frequency

response, imagenoise, and colorreproduction_accuracy

betestedforasthe_{first step in}afineartreproduction

imaging

workflow.

Alsoconsideredwithinthisresearch weretheinfluential variablesthat_play a part

in digital

imaging

workflows within museums andinstitutions. These have beenoutlined

andtakeninto consideration

during

the selectionoftherecommended cameratests. Itis

the

feeling

oftheresearcherthat

introducing

camera

testing

tophotographers will

help

themto understandtheequipment,technology,andworkflowparameterswith which

they

Chapter1

Introductionand

Statement

oftheProblem

Statementofthe Problem

Digital

imaging

technologieshavetakena strongholdwithin museumsand

institutions,

replacing someoralltraditionalphotographicprocedures. Thisshiftin

technologieshascreated a needforphotographerstoadjusttoandtore-educate

themselves inordertoimplementthesenewtechnologies. Althoughdigital

imaging

technologiesoffera wide range of

benefits,

oftenthe_necessaryworkflowsare not

fully

understood; therefore,thebenefitsare not

fully

realized. Arecent_survey conducted

by

Rochester Instituteof

Technology

(RIT)

scholars points outthat"more thanhalfofthe

respondents reportaperceivedlackofknowledgeabout

digital,

howevera_{majority feels}

comfortablewiththeir_{institutions embracing digital}

photography"

(Berns, Frey, Rosen,

Smoyer,

and

Taplin, 2005,

p. 1).

Theresultsfromthis_survey also pointoutthatthe

top

threereasonsfor digital

imaging

withinmuseumsandinstitutionsare"tomakecollections accessible overthe

Internet,

toinclude digital images in a collection managementsystem,andtoproduce

reproductions"

(Berns,

Frey, Rosen, Smoyer,

&

Taplin,

2005,

p. 1). This is a major

concern

because,

if

imaging

departmentswithinmuseumsandinstitutions are_creating

then_time,money, and man-hours are

being

_lost.Whenan artifactis

imaged,

itshouldbe

done inordertoachievethebestpossible representation ofthepiece.Thiscan allowfor

image_{repurposing for future}needs,such asthe

Internet, databases,

and reproduction. But

ifthegoal when

imaging

is the

Internet, databases,

orreproduction,then it is

likely

that

the_quality ofthearchivalimagewillbecompromisedin orderto fulfillanimmediate

goal

(Berns, Frey, Rosen, Smoyer,

&

Taplin,

2005).

BackgroundandSignificance

RIT scholars_recentlyconducted a research projectfunded

by

theMellon

Foundation,

entitled_{Direct Digital Image Capture of Cultural Heritage:}

Benchmarking

American Museum Practicesand

Defining

Future Needs. Thisresearchprojectwas

carried outinaneffortto

document,

toevaluate, andtounderstand thecurrent practices

of

imaging

departmentswithin museums all overtheUnitedStates.

Oneofthemain componentsofthisproject was a_surveywhichwas available

onlinefora year. Participants frommorethan50museumsfilledoutthe78-question

survey;theresultsestablished aclear_{understanding}ofthecurrentissues faced

by

museum photographers.

Theresearchers _conductingthisproject alsocompletedan in-depth investigation

into fourmuseumsthathadadopted adigitalworkflow_{earlyon, allowing}them

significanttimetoestablishtheirworkflow. The sametwopaintings wereimagedat each

Theresultsofthe _{four varying}workflows andtheinformationgainedfromthe

surveyproject were presented at a conference atRIT in Septemberof2004. Allwho

participated wereinvitedto attendtheconference todiscuss digital

imaging

workflows

andtheavailable_equipment, as well astodefinefuture needs

(Berns, Frey, Rosen,

Smoyer,

&

Taplin,

2005).

The informationgainedfrom boththe_surveyandthe_varyingresultsofthe

in-depthlookatfour different

imaging

workflows provides a clear picture ofthe

idiosyncrasieswhichhave alargeeffect onthe

imaging

practices atvarious museumsand

institutions. One ofthe

key

findingsoftheresearch suggeststhat"it ispossibleto

develop

a single experiential proceduretoevaluate color and spatial

quality"

(Berns,

Frey,

Rosen, Smoyer,

&

Taplin,

2005,

p. 2). This impliesthatifthecameras

being

used

within museums couldbeassessedfor quality_purposes, itwouldthenbepossibleto

construct aworkflowbasedon a conciseknowledgeofthecameras'

performance.

Specifically,

ifa cameraistestedforcolorreproduction_capability

by

_analyzingthe

spectral _sensitivityofthecamera's

imaging

device,

thiswillcreate a _startingpointfor

anticipated deviationfromthe standard observer.Thisisthefirst

discrepancy

inthecolor

reproduction_process; whenthedeviationis

known,

a workflowcanbeconstructed,or

adjustedtoaccountforthedeviation.

Another

key finding

oftheresearchis "there isnot a common workflow_among

thetested institutions"

(Berns, Frey, Rosen, Smoyer,

&

Taplin,

2005,

p. 2). Itwouldbe

beneficial ifa suggestedworkflowwasinplacetoassistphotographerswith"howto"

as_requiringnumerousand often complex equipmenttesting.Ifsimple

testing

could

accessthecondition ofthe

imaging

_equipment,a workflow couldbeconstructed and

implementedtoprovide andtoenable greater_qualityand_{consistency in}theimages

created. Thiswould allowforconsistentimagesforarchival_{purposes, for easy sharing}of

digital

files,

and_{for repurposing}ofimages as needed.

Reasons for Interest

Personal interest inthe topicoffineart reproduction stemsfromtheresearcher7 s

first-handexperience as a photographer_{working in}a museum. Employedatthe

Wadsworth Atheneum Museumof

Art,

Hartford,

CT,

theresearcherexperiencedthe

trials andtribulationsof_{assisting in}thecreation andinitiationof adigital

imaging

department. The departmentwas_{originally funded}withtheintentionof_{producing fine}

art reproductions and catalogues.

However,

onceresearchhad beenconducted andthe

equipmenthad beenpurchased, itwasapparenttothephotographersthatreproduction

was notthesolepurposeofthe

imaging

department.Theresearcherworked with

photographerAllen

Phillips;

together

they

began

digitally imaging

paintings with

archivalgoals. (A descriptionoftheentire workflow canbefound in Appendix

A.)

Each

image created couldberepurposedfor differentapplications,such as_catalogues,

brochures,

advertisements, the

Internet,

and soon.

TheexperiencesattheWadsworthAtheneumMuseumofArtleadtheresearcher

toinvestigatethe topicofdigital

imaging

within museums.

Having

received aBFAin

place at

RIT,

theresearcher came across theDirect_{Digital Image Capture of Cultural}

Heritage:

Benchmarking

American Museum Practicesand

Defining

Future Needs

research project ontheRIT website. Thisprojectbroughttogethera_communityof

photographersfrommuseums aroundthe_countryto addressissues concerning digital

imaging

practices within museums. Thereportsfromthisproject provide a significant

amount ofinformationaboutthecurrent state ofdigital

imaging

within_museums,

enablingtheresearchertocontinuetheinvestigation withsupportfromthedigital

imaging

community atRIT.

Theresearcher would alsoliketohighlightthe

driving

factorsof

imaging

projects

withinmuseums andinstitutions.

Imaging

departmentsoften sufferfromsmall

budgets,

andhavetomakedowiththe

imaging

equipment

they

have.

"Many

systems use

components re-purposedforthis specific application. Asaconsequence,thereisawide

range of_qualityofthese variousimage

archives"

(Berns, Frey, Rosen,

Smoyer,

&

Taplin,

2005,

p. _{1). Not only may}

imaging

departments berestricted

by

asmall

budget,

but

they

may alsoberequiredtoworkquickly,

imaging

certainitemsat specifictimes inspecific

locations. Inaddition,

they

must often caterto thespecificneeds of curatorsor_marketing

personnel. Allofthese factorsand_manymore caninfluencethe_qualityoftheproduced

digital images.

Thus,

theresearcherhasadesiretoassist

imaging

departmentswithin

museumstomakethemost out oftheequipment

they have,

to

help

themassesswhat

equipment

they

_{may actually}need, andtoillustratetheprocedure of_creatingarchival

Chapter2

LiteratureReview

Digital

Imaging

withinthe MuseumEnvironment

Photography

isused within museumsforthepurpose of_reproducingartwork.

Until recently thistypeof_photography was_primarilytraditionalinprocess, usingaview

camera,4x5 colorpositive

film,

andtraditionalprocessing.Themostcommonimages

createdwere4x5 color_{transparencies,} whichcouldbeusedforreproduction, as wellas

toprovide a referenceforreproductionmatching. _{Over time,}museum photographers and

printers_{became very}accustomedto this traditionalworkflow.

However,

withtheadvent

ofdigital photography andtheimplementationofthis

technology

withinthe_photography

industry

as awhole,it isnow imperativethatmuseum_{photography follows} suit.

Themigrationfromtraditional_photographyto _{digital photography}within

museumsshouldbeginwith a wellthought-out, long-termplan whichfocusesonthe

creation andmaintenance of adigital imagearchive. Atransition todigital

imaging

requires new equipment,

technically

skilledphotographers,collections management

personnel, and adatabase for storing digital

images,

metadata,and objectinformation.

Funding

allocatedforthe_upkeepandmaintenanceof an archiveofdigital images isalso

essential. Althoughtheserequirements seemclear,

they

are oftennotthemost prevalent

(Kenney,

Rieger,

2000).

Abby

Smith,

DirectorofProgramsoftheCouncilon

Library

and

Information

Resources,

states"most digitalprojects aredriven atleast inpart

by

strategic

institutionalconsiderationsthatoften conflict withthewell-ordered and_narrowly

confined parameters of anideal digitalconversion

project"

(Kenney, Rieger,

2000,

p. 2).

The digital age andtheInternethavechangedthe_waypeopleacquirealltypesof

information.This changeisseen as a

driving

force behind

digitizing

collections inorder

toprovide a secureInternetpresenceformuseumsorinstitutions. "Cultural institutions

are

investing

in digitization fortworeasons:

First,

they

remain convinced ofthe

continuingvalue of such resourcesfor

learning,

_{teaching,research, scholarship,}

documentation,

and public accountability.

Second,

they

recognizethat_changinguser

behavior may jeopardizetheseresources andtheirstewardship"

(Kenney,

Rieger,

2000,

p. 1). The trendin digital

imaging

is

being

driven

by

the demand for

digitally

accessible

information viatheInternet. This is beneficialtoamuseum or

institution,

as wellasto

theuser,because

by

makingcollectionsavailable overthe

Internet,

imagesofart and

artifacts canbeviewedfromanywhereintheworld. This increasedaccessisone ofthe

most valuable aspectsofdigital

imaging

projects.Digital accessdecreasesphysical

access, which canbe favorabletoan_artifact, aswellasfavorable fortheresearcher who

willsavetimeand_money

by

not

having

to traveltothelocationofthe artifact

(Kenney,

Rieger,

2000).

Digitalimagearchives are now

being

viewed asinstitutional _assets,andthis trend

has asignificantimpactonthevalue ofthe

digitizing

initiative. It isapparentthatimages

timeof conception.The developmentand maintenance of an efficientdigital

imaging

system relies on thedecisionsmade at each stage ofthe

imaging

process. These decisions

affecttheusage oftheimagesnow andinthefuture. Intheir

book, Moving Theory

into

Practice,

Kenny

andRieger

(2000)

present a managementwheelfor digital

imaging

[image:21.564.152.413.210.479.2]

programs. (See Figure 1.)

Figure 1. Managementwheel for digital

imaging

projects. [Source:

Kenney, Rieger, 2000,

p.

6]

Thiswheelillustratesall oftheelements ofadigital

imaging

project. Atthecenter

ofthewheel are users andcollections. Themiddle_ringrepresents theinstitutional

resources made_upof_{the staff,}

funding

andthe

infrastructure,

bothtechnicaland

digital

imaging

program,

including

preservation,access, systems

building,

image

processing,metadatacreation, _qualitycontrol,

digitization,

benchmarking,

selection,and

management. Itis suggestedthattheinstitutionalresource elementswithinthemiddle

ringofthemanagement wheel"willenhance or constrain digitizationprograms"

(Kenney, Rieger, 2000,

p.6).

However,

it is imperativethatall aspectsofthe

managementwheel are considered atthe timeofinitiationand aremaintainedforthe

future inordertoprovide an efficientdigital archival system capable of_servingusersfor

yearstocome

(Kenney,

Rieger,

2000).

RIT

Benchmarking

Conference

Key

Findings

The RIT-basedresearchproject,Direct Digital Image_{Capture of Cultural}

Heritage:

Benchmarking

American Museum Practicesand

Defining

Future

Needs,

establishesthecurrent statusofdigital

imaging

projectswithinAmericanmuseums.

Principle investigators

Roy

Berns andFranziska

Frey,

_alongwithresearchersMitchell

Rosen,

Erin

Smoyer,

andLawrence

Taplin,

completed

(July

2005)

twoyears ofwork

investigating

thetopic. The

key

findingsoftheprojectarelisted in Table 1

[image:23.564.68.499.93.398.2]

Table 1. The

Benchmarking

American Museum Project

key

findings.

Imaging

willbe mainly digital inthenearfuture.

Museum

imaging

was outputdriven(e.g.,printed publications andtheInternet).

Library

and archive

imaging

wasfocusedon_creatingsemantic-basedimagearchives.

Selectioncriteriaforcamera systems were_{determined primarily}

by

subjective_criteria,wordof mouth,andtechnicalsupport ratherthanobjective measures.

Itwaspossibleto

develop

asingleexperimental proceduretoevaluatetheobjective_qualityof a camera system.

The idealphotographerhas 10-15yearsexperience_{photographing}culturalheritageandin-depth knowledge in information_technologyand arthistory.

Workflowsvaried widely.

Proceduresand workflows are notwelldocumented. Colormanagement was notusedtoits fullestcapabilities.

Differences in

lighting

wereone ofthemainfactors

leading

toaestheticdifferencesinarchives. Aestheticswere oftendeemedmoreimportantthan scientific rigor and_{reproducibility}when imaging.

Most institutions includedvisual_{editing in}theirworkflows,adding significantlytothe total timerequiredfrom setupto_archivingadigitalmaster.

Therewas notawell-defineddigitalmasterthatwould enable cross-media_publishingandthat couldalsobeusedinscientificimaging.

Digitalpreservationisstillin its infancy.

Eachofthe

key

findings listed illustratestheneedforguidance and supportsthe

concerns associated withdigital

imaging

projects withinthemuseum environment. For

example, the

key finding

that output,either ontheInternetorinpublication

format,

isthe

driving

force behind digital

imaging

initiativesisagreatconcern,because ifresources are

allocated andutilizedforthecreation ofadigital

image,

thenitis mostbeneficialifthe

image beofarchival quality. Theother

finding

relatedto thisis "therewas not a

well-defined digital masterthat would enablecross-media_publishing andthatcould alsobe

usedinscientific

imaging"

(Berns &

Frey, 2005,

p.l). Althoughtheresearchindicates

has beena considerationforsome timenow.

Kenney

_{& Rieger}

(2000)

givethisexample:

"thereisa_growingsupportfor_{creating digital}mastersthatare rich enoughtobeuseful

overtime and cost effective.This position presumesthatconversion standards are set

higherthantheimmediaterequirements"

(p. 25). The discussion ofthecreation ofdigital masterfiles leads_{to considerations about equipment}

evaluation, characterization,and workflow guidelines.

Thefindings_{from Direct Digital Image Capture of Cultural Heritage:}

Benchmarking

AmericanMuseum Practicesand

Defining

Future Needs indicatethat the

transition _{from analog}todigital_photographyiswellunderway."Over halfofthe_survey

respondentstookat least 90%oftheirphotographs

digitally

in 2003...

[which]

was all

happening

despite aperceivedlackofknowledgeaboutdigital

imaging by

the practitioners"

(Berns &

Frey, 2005,

p. 55).

There is also awiderangeof

imaging

workflows_amongmuseum_{photographers,}

not_manyofwhichhave been documented. The findingsalso suggestthat thereis little

structureto theevaluation ofdigital

imaging

equipmentpriortopurchase (Berns &

Frey,

2005). Thissuggests thattheevaluationof a cameras

imaging

_{quality is}overlookedwhen

purchasing decisions are

taking

place. Although"procedures for

testing

the_qualityof

digital camerashave beenestablishedintherecent

past"

(Smoyer, Taplin,

&

Berns, 2005,

p.

1),

they

haveyettobe implemented intothemainstream museum environment.Thisis becausethe tests"arenot yet suitable andcomprehensive enoughtobeusedinamuseum

setting andhavenotbeen developed specificallyforthedirect digitalcapture of

artwork"

aware ofthe_possibilityof camera_testing, perhaps individualcameraimage quality will

now beconsideredwhen _{purchasingdecisions} are

being

made.

The

key

findingsfromthe_{Direct Digital Image Capture of Cultural Heritage:}

Benchmarking

American Museum Practicesand

Defining

FutureNeedsproject also

indicatethatmost of museums surveyedhavenotfulfilled

benchmarking

proceduresto

thefullestcapacity.

What exactly does

benchmarking

mean?

Kenney

& Rieger

(2000)

statethat

"benchmarking

is primarilya management_tool,designedtoleadtoinformed decision

making about a rangeofchoices andan_{understanding}oftheconsequences ofsuch

decisions"

(p.24).

Considering

thefindings ofDirect Digital Image Capture_ofCultural

Heritage:

Benchmarking

American Museum Practicesand

Defining

Future

Needs,

it is

clearthattheimplementationof

benchmarking

proceduresdoesnot alwayshappen atthe

beginning

ofthe transition_{from analog}todigitalphotography;

however,

itwouldbe

beneficialif it did.

Benchmarking

procedurescanassist a managementteamin

defining

the terms of

itsproject.

According

toAnne R.

Kenney

in

Moving Theory

into

Practice,

thecategories

toconsiderwhen

benchmarking

are: requirements

definition,

measurement, tolerance

values andverification. Requirements definitionconsistsof_{assessing "source documents}

and

identifying

thevariables associated withquality,cost, and/or

performance"

(Kenney

&

Rieger, 2000,

p. 24). Themeasurements_categoryconsistsof_gatheringand

documenting

data,

objectivelycharacterizingmaterial,and

determining

thevariable

tonal range, scanningrequirements

(resolution, bit-depth),

andthe_{corresponding}

imaging

metrics (MTFand signal-to-noise

ratio)"

(Kenney&

Rieger,

2000,

p. 24). Thetolerance

values_categoryrecommendsto"determine howmuch

variance"

willbetolerated

throughout the workflow,as well asfordifferent_qualityrequirementsfor differentimage

usages

(Kenney

&

Rieger,

2000,

p.24). The verification_categoryof

benchmarking

requires approval and modificationtothebenchmarkedproceduresthrough

testing

and

procedure evaluation

(Kenney

&

Rieger,

2000).

Benchmarking

procedures can"reduce bothexperimentation andthe temptation

tooverstate orunderstate

requirements"

(Kenney

&

Rieger,

2000,

p. 24). The

developmentofbenchmarkedprocedures, such as camera

testing

andworkflow

documentationforadigital

imaging

project,allowsfora consistent and controlled

workflow that_{may be}adjustedtoensurehigh imagequality.

RIT

Benchmarking

Conference

Defining

Current Needs

Theoutcome oftheDirect Digital Image CaptureofCultural

Heritage-Benchmarking

AmericanMuseum Practicesand

Defining

Future Needsprojectisan

assessment ofthe currentpracticesofdigital

imaging

withinmuseums and suggestions

forthefuture. Thesuggestionsfor futureresearchanddevelopmentarelisted in Table 2

[image:27.564.79.493.120.400.2]

Table 2. The

Benchmarking

AmericanMuseumProjectsuggested futureresearch.

Suggested Future Research

Establishauser_{group devoted}to

imaging,

_archiving,and_reproducingculturalheritage.

Holdperiodicinformalconferences_{for information gathering}andsharing.

Develop

apracticalcharacterizationtestmethod.

Incorporatecharacterizationdata intoa metadata structure.

Develop

andtestasystemcalibration protocol.

Define qualitycriteriabasedonobjective and subjectivemetrics.

Establisha_testingservice.

Establishaninformal

imaging

inter-comparison.

Researchand

develop

new

imaging

systems.

Theneedforcamera

testing

proceduresis a recurrenttheme_amongthefindings.

To

"develop

apractical characterization testmethod...andtoestablisha

testing

service"

arelistedas areasforsuggestedfuture research(Berns&

Frey, 2005,

p.2).Partofthis

project was anin-depth lookatfour different

imaging

workflows,alongwith objective

characterizationofthefourcamera systems. Thetestsusedtocharacterizethecameras

foreach workflow were complex. Thegoalistobeableto"consolidatethe

characterization procedure

by

reducing thenumber oftargets thatmustbeimaged"

(Berns &

Frey, 2005,

p.59). Targetconsolidationisneededbecause some ofthe targets

Thetargetsare not specifictodigital

imaging

within_museums, and

they

are not

optimized forthistypeof characterization.

Therefore,

it isproposedthat

testing

procedures and targetsbecreated_{specifically fordigital}

imaging

workflows within

museums.

Aconcern also_regardingthecurrent camera

testing

proceduresis the_complexity

ofthedataprocessing. Itis desirabletocreate "anapplicationtoguidea noviceuser

through the

testing

procedure...

[including]

instructionsforcaptureofthe testimagesand

automatic_processingoftheimages data...[aswell asa] metric

report"

(Berns &

Frey,

2005,

p. 59). Theestablishment of a

testing

servicethatcould providea metrics reportis

alsodesirable because itcould serve as"an importanttool for

industry

togaugetheir

measurement_qualitycompared with each otherandwithstandard

instrumentation"

(Berns &

Frey, 2005,

p.61). A

testing

servicecouldalso serve as acatalystfor

improving

imaging

technology

by

allowingphotographerstocommunicatetheirneeds.

Alsolisted for futureresearchis thedevelopmentand

testing

of"asystem

calibration

protocol,"

aswell astheincorporationof"characterizationdata intoa

metadata

structure,"

bothofwhichareexamples of

benchmarking

procedures(Berns &

Frey,

2005,

p.2).The developmentof asystem calibrationprotocol will require a set of

standard conditionsforeach componentofan

imaging

system.These componentsinclude

"lighting,

camera_{setup (exposure time,}dynamicrange,depth of

field,

_{magnification,}

etc.),colormanagement,file formatandencoding, metadata, and

workflow"

(Berns&

Oncea system calibration protocolhas beenestablished, therespective

imaging

system canbeadjustedto_{the protocol,resulting}inanimagearchivethatconforms to the

standard conditions. Asystem calibration protocol would allow standardization of

imaging

procedures, _allowingformultipleimagearchives created underthesame

imaging

conditions,regardless of where the

imaging

tookplace. Thiswould reduce

imageappearancediscrepanciesand allow_{for easy sharing}of archives.

However,

whether or not an

imaging

systemhas beencalibratedtoa set_standard,

the

imaging

procedures withinagiven system areimportanttodocument. The

imaging

procedureisreferredtoasthesystem's characterization. Thecharacterization of_any

imaging

systemis likethe system'sfingerprint. "Characterization datacanbealsobe

usedtodetermine asystem's_{repeatability}and

reproducibility"

(Berns &

Frey, 2005,

p.

60). Ifcharacterizationdata isstoredwith animage'smetadata,it isthenpossibleto

know under whatconditionstheimagewascreated.

"Including

thecharacterizationdata

inanimage'smetadata structureensuresthatanimagewillnotbeseparatedfromthe

imaging

system

characteristics"

(Berns&

Frey,

2005,

p.

60),

thus_{allowing for}the

repeatability and_{reproducibility} of aspecificimage withthespecific

imaging

system

characteristics.

The Direct Digital Image Capture of Cultural Heritage:

Benchmarking

American

Museum Practicesand

Defining

FutureNeedsresearchprojecthasestablished aclear

assessmentofthecurrentstatus andthecurrentneeds ofthe digital

imaging

environment

withinin museumsintheUnited States. It hasprovidedan excellentfoundationforthe

eagertocommunicatetheneedsof photographerswithinthisindustry. Thisresearch

serves as a catalystforchange, _encouragingtheprogressionof

imaging

technologies,

imaging

procedures,and_communitycommitment.

Color Reproduction

Introduction

Thediscussionofdigital

imaging

requires areview of color managementtheory.

In ordertoutilize color management

theory

tothe

fullest,

it is helpfultounderstand color

reproduction,how it

developed,

and whatit is basedon. Itis first helpfultorealizethat

"coloris a_propertyoflight"

(Fraser, Murphy,

&

Bunting, 2005,

p. 5). Without

light,

thereis novisiblecolor.Thenext considerationis that, inordertoexperiencetheevent of

seeingcolor, theremustbeanobserver,alight_source, and anobjector scene. 'Thecolor

eventisasensation evokedintheobserver

by

thewavelengths oflightproduced

by

the

lightsource andmodified

by

the object;if anyofthese three things changes, thecolor

eventis different...we see adifferent

color"

(Fraser, Murphy,

&

Bunting, 2005,

p.5).

The Visual Spectrum

Lightisone onthemain componentsto thevisualcolor experience. Without

light,

thereis nocolortobeseen. "Butalllight isn'tcreated equal: thecharacteristics ofthe

light haveaprofound effect on ourexperienceof

color"

(Fraser, Murphy,

&

Bunting,

2005,

p. 6). Light energy isclassified asphotons.Aphotonis "a fundamentalpacketof

particles, andinother ways photonsbehave likewaves"

(Fraser, Murphy,

&

Bunting,

2005,

p. 548). Eachphoton exists with a specific_energy

level,

which doesnot affectthe

speed at whichittravels (sincethespeed oflight is_{constant); rather,} the_{energy level}

determines how fastthephoton pulsates. Thehigherthe_{energy level}the photon

has,

the

shorterthewavelength at whichthe photon pulsates. "Thespectrum refersto thefull

range of_{energy levels}

(wavelengths)

thatphotonshaveas

they

travel throughspace and

time"

(Fraser, Murphy,

&

Bunting, 2005,

p.7). The visual spectrum referstothepart of

thespectrumtowhichthehumaneyeissensitive. Thevisual spectrumrangesfrom

approximately 380 to700nanometers

(nm)

(Fraser, Murphy,

&

Bunting,

2005). Figure 2

illustratesthevisual spectrum.

Thespectrum

short wavelengths(high energy) longwavelengthsflowenergy)

1n m 1000nm 1mm 1m 1km Ml'"motors 10

'*

Id' 10'

10"

10

|__________|____HBB

viilblstight

Figure 2. Thevisual spectrum.

[Source:

Fraser, Murphy,

&

Bunting,

2005,

p.

7]

The humaneyehas varyingresponsesto thedifferentwavelengths withinthe

as red and_orange), andtheshort wavelengths (400nm)evoke coolcolors(such asblue

and_violet.) Thevisual spectrum rangesfromredtoorangetoyellow togreento

blue,

and

finally,

toviolet.There are also wavelengthsjustoutsidethevisual spectrum which can

indirectly

affecthumanvision.For_example, whenfluorescentagentsare presentin

objects such as paperor

laundry

detergent_{(in clothing}once washed), theseagents will

absorbnon-_{visible photonsinthe}

ultra violet

(UV)

region oftheelectrometricspectrum

and emitvisiblephotons,_causingthephenomenon referredto as

fluorescence,

_creating a

brighterappearance. Infrared

(IR)

photons are also concernsfor digital

imaging

devices.

Mostdigital

imaging

devicesare

highly

sensitive toinfraredand,therefore, must contain

aninfrared filtertoabsorb non-visibleIRphotons priorto them_enteringthe

imaging

device

(Fraser, Murphy,

&

Bunting,

2005).

Understanding

thevisualspectrum oflightis

animportant aspect of_{understanding}colormanagement,aswellas_{understanding}the

humanvisualsystem.

The Human Visual System

Colormanagementin thedigitalage stemsfromcolorscience andfromcolor

appearance models. An_{understanding} ofthehumaneye andhowcolorisreceived and

interpreted

by

thebrain isessentialto_{understanding}all aspectsofcolorreproduction.

"Thesensation of coloris caused

by

electromagnetic radiationof variousfrequencies

being

received

by

theconesinthe

retina"

(Rajala, 1995,

p. 1). Theretina, located inthe

backofthehumaneye,is made_upofathinlayerof photoreceptorcells referredtoas

presentintheopticalimage intochemical and electrical signalsthatcan betransmittedto

the laterstages ofthevisual

system"

(Fairchild, 2005,

p. 4).

Next,

thesignals are

"processed

by

anetwork of cells andtransmittedto thebrainthroughtheoptical

nerve"

(Fairchild, 2005,

p. 4). This is howthehumaneye andbrainworktogethertocreate the

experience of seeing.

Thephotoreceptor cellsin_{the retina, the}rodsand_cones,serve differentvisual

functions. Therods allowforvision atlowluminance

levels,

andthe conesallowfor

visionathigh luminance levels. Rods and conescanfunction separatelyor_together,

depending

ontheluminanceconditions. Thevisual color experienceis dependentonthe

cones withintheretina.

Therearethreetypes ofcones and_onlyonetypeof_rod,makingtherodsunableto

aidthecolor vision experience. Thethree typesof coneswithintheretina_{allowing for}

color visionareknownas

L, M,

andScones.Thesenames are usedtodescribethe

sensitivity functionofthecones,respectively,

long-wavelength,

middle-wavelength, and

short-_{wavelength (sometimesreferredtoas}

RGB,

_red, greenandblue). It is importantto

notetherelativedistributionofthe

L,

M,

andSconesthroughout theretina. "Therelative

populationoftheL:M:S cones are_{approximately} 12:6:1 (withreasonable estimates as

highas

40:20:1)"

(Fairchild, 2005,

p. 10). Thisconedistributionrequires extra

consideration when_{addressing issues} of visual response. That

being

stated,it isthese

L,

M,

andScones withinthehumanretinathatallowfor humancolor_vision,and

they

also

TrichromaticColor Reproduction

"Theretina contains three typesof cones whichfilterthe

incoming

electromagnetic radiation

forming

thebasisforthethreecolor_primary systems usedin

television,_printingand photography.That

is,

_any color canbereproduced

by

_combining

differentamounts of_red,green andblue

primaries"

(Rajala, 1995,

p.

1)

Although any

color canbe visuallycreated

by

_combining

varyingamounts ofthe_primarycolors,

reproducing anycoloris

technically

not so simple.

The investigationoftrichromaticcolorbegan inthe 1700s.In

1722,

Jakob

Christoffel LeBlon iscreditedwith_{printing using}athree-color technique. In

1807,

"Thomas

Young

wasinstrumental in gaining general acceptancefortheviewthatit

istheretina ofthehumaneye thatisresponsibleforthis triplefeatureof

color"

(Hunt,

2004,

p. 9).

In

1861,

JamesClark Maxwellcreatedthefirstcolor photograph_usinga

trichromatic technique. It is Maxwell'swork whichhas hada

lasting

effectonthe

trichromaticcolor process. Maxwellcreatedacolor photograph

by

_capturingthesame

scenethrougha_red,green, andblue

filter,

andfromthenegativeshecreatedthree

positive slides. Heplaced each ofthe three positive slidesintoseparate _projectors, with

respective_{red, green,}andblue filterswith whichtheimagewas projected_through,and at

last,

afullcolorreproduction ofthephotographwas created. "Theprinciple ofhis

(Maxwell's)

method,reproductionof allcolors

by

mixtures, in varyingamounts,of

resourcesthemethod itself (triple projection)can produce resultsof_{very high}

quality"

Hunt

2004,

p. 9).

Trichromatic Limitations

Althoughtrichromatic color reproductionissuccessful at_reproducingmost

colors, it hassomelimitationswhichpreventit from

being

ableto_{colorimetrically}

reproduce all colors.Thespectral response curves ofthe

L,

M,

andScones withinthe

humanretina mustbe determinedfirst inordertoaddresscolor reproduction

(Hunt,

2004). Bothmethodsofdirectmeasurement andindirectmeasurement of"thelight

reflectedbackthrough the pupil oftheeye,havegiven similar

results"

(Hunt

2004,

p.l

1),

400 500 600 7O0

[image:36.564.155.405.79.350.2]

Wavelength(nm)

Figure 3.

(a)

Theprobable_sensitivity curvesforthe three

typesofcones, withthethreebest

R, G,

and

B,

lights for

additive color reproduction,

(b)

Typicalspectral_sensitivity

curvesfound from

bleaching

experiments withthehuman

retina. [Source: Hunt

2004,

p.

12]

Itis clearfrom figure 3that the _sensitivitycurves ofthethreeconetypeswithin

theretina_{overlapconsiderably;}thereisalso adebateaboutthelocationofthecurve

peaks. Some suggestthecurves

L, M,

andSpeakat440_nm, 530_nm,and565 nm,

respectively,while othersindicatethepeaks areat

440,

545,

and580nm. The

overlappingofthe_sensitivitycurvesofthe threeconetypeswithintheretina causesthe

mostconcern. Theusefulness ofthe_sensitivitycurves ofthe threeconetypesis the

abilityto transmitluminance informationatthesamewavelength

(using

suitablefiltersto

the spectrum with whichtheMcones are _solely sensitiveto, "no filtercanbe foundto

transmitlightthatstimulatesthe" Mcones_{only Hunt}

2004,

p. 13). Toaccountfor this,

one must select"a filterthat transmitsanarrowbandoflight inthe green part ofthe

spectrum at a wavelength of about5 1 0nm as shown astheG in...[Figure

3]

...whilethe

bandsoflightpassed

by

thered andbluefilters arethosemarkedR and

B"

(Hunt, 2004,

p. 13).

Hunt

(2004)

furtherstates,

.. .

[it]

isthusclearthatthe

inability

of

any beamsof_red,green, and blue lighttostimulate theretinal cones_{separately introduces}abasic

complicationintothewhole oftrichromatic color reproduction.Ifthe

p andb (L and

S)

curvesdidnot_{overlap in}theblue-green part ofthe spectrum,thengreenlightcouldbefoundthatstimulatedthe y-cones (M_cones)on theirown;

but,

sincethe_pandb (Land

S)

curvesdo overlap

appreciably, they-cones (M_cones) cannotbesimulatedontheirown. Forcolor_vision,this_overlappingprovidesthebasis forgooddetection

of changesin huethroughout thespectrum.

But,

forcolor_{reproduction,} itmeansthatsimpletrichromaticmethods cannot achieve correctcolor

reproductionforall colors. The

difficulty

cannotbeavoided, because it

stemsfromthebasicnatureofhumancolorvision;theresultisunwanted

stimulations inreproductionsystems (p. 13).

The CIE Standard Observer

The International CommissiononIllumination

(CBE)

is a_{well-established,}

internationally

recognizedorganization dedicatedtothe_studyand progression of color

management systems. Tocontinuethediscussionoftrichromaticcolor reproduction

limitations,

considertheCIE2 standard observer.Duetothe_varyingcolor vision

between

individuals,

"the CIEhas come_upwiththeconcept ofthestandard

observer"

(Sharma,

2004a,

p. 83). "The CIE has specifiedthree_primary colors and conducted

thespectrum"

(Sharma,

2004a,

p.83). The results ofthesestudies were publishedin

1931,

and

they

are referredtoastheColor

Matching

Functions

(Sharma,

2004a). Figure 4

isa representation ofthestandard observer color_{matching function}curves.

350 400 450 500 550 600 650 700

Wavelength_(nm)

Figure 4. Color matching functionsoftheCIE2standard observer. [Source:

Sharma,

2004a,

p.

84]

TheCIE's 1931 2

standard observer color_{matching function}providesthe

spectral reflectance_curves, aswell as amathematicalrepresentation oftheaverage

human'scolor vision at a2_viewingangle. The 2

angledescribesthe_viewing angle at

theeyewhenthevieweris

looking

at anobject 18

inches,

orata normal _{viewing distance}

(Sharma,

2004a). "ThisobserverreceivestristimulusvaluesCIE XYZ froma spectral

stimulus_{(pk according}towherex(k), y(k),and_z(k)denote thestandard spectral_matching

functionsandk is determinedfromthenormalization ofY= 1 for_{thespectral stimulus of}

theilluminant"

(Hill, 2002,

p. 1).

However,

duetothephenomenaknown as_metamerism,

"differentspectral stimuli_{cpx may}resultinthe sameXYZvalues,"

appearthesameunderoneilluminantanddifferentunder another

(Hill,

2002,

p.l).This

canbe both beneficialand problematic beneficialbecause itallows forcolor_matching

across awiderange of_media, butproblematicbecause "each image capturing device

exhibitsitsowndevicemetamerism. Colorstimuli

being

metamerictothestandard

observer are nolongermetamericforthe_{image capturing}

device,

andtherearedevice

metameric colors

looking

differenttoan

observer"

(Hill,

2002,

p. 2). Metamerism

considered, it isthegoal of

imaging

devices tomimic thestandardobserver's

trichromaticcolorvision_usingthree channelstomimic the spectral response ofthe

humaneye.

However,

becausetheLand Sconeresponses_overlapconsiderably, Hill

(2002)

statesthatcameras

...wouldruninto severe problemsof electronicnoisewhen separating technicalcolorsignalslike sRGB tocontroloutputdevices...Thisis

becausenoise componentsalwaysadd_upeveniftristimulusvaluesare

subtracted. Butcorrect coloranalysiscanalsoberealized

by

three technicalsensors with spectral responsivitiesderived from any linear transformofthecolor_{matching functions. This}allowsoptimized

separationofspectral responsivities of camerachannels and scanners

with respecttobestelectronic signal-to-noiseratio.

However,

again a

draw backcomes in duetothefactthatresponsivitiesoptimized inthis

wayexhibit negativepartslike allcolor_{matching functionscorresponding}

torealizable_primarycolorsdo. Since ahighsignal-to-noise ratioisthe

moreimportant feature in imagecapturing,all presentcameras and

scannersusean approximationto thepositivepartsofthesecurvesonly.

Asa_result,thereoccurinevitableerrorsofcoloranalysisinallourpresent

&(W

WW

A

//

'I!

if

g

V

t

1

rs(W

V _Wnm

[image:40.564.189.380.79.211.2]

400 500 600 700

Figure 5. Exampleof negative color_matching

functions,

_aidingtocolor

reproductioninaccuracies. [Source:

Hill, 2002,

_p.5]

The Color Reproduction

Inaccuracy

Reality

Foraccurate colorreproduction_usingadigitalcamera,it is ideal ifthedigital

camera's spectral sensitivities matchthespectral sensitivities ofthehuman visual system.

"Strictly,

a camera's spectral sensitivities shouldbealineartransformationofthehuman

visualsystem'sspectral sensitivities. That

is,

throughalinear_{transformation,}the

L, M,

andS sensitivities arewell-estimated.

Thus,

_{CIE color-matching}

functions,

whichdonot

resemble

L, M,

andSsensitivities, meetthis

criterion"

(Berns,

2001,

p. 2).

However,

mostdigitalcamerasdonotmeetthis criterion, andthis isthemain

cause of colorinaccuracieswithindigital images. Forexample,Figure 6 showsthe

difference between humanspectral sensitivities andatypical scanback_{camera, the}type

mostoften used when_{photographing}artwork. Thesolidlinesarethecamera's spectral

beseenin Figure

7;

herethespectral sensitivitiesof aCCD

imaging

deviceareillustrated

in solid

lines,

andthedashedlines illustratehumanspectral sensitivities.

380 430 480 530 580 630 680 730 780

Wavelength,nm

Figure6: Spectralsensitivities

of atypical scanback camera.

[Source:

Berns, 2001,

p.

4]

380 430 480 530 580 630 680 730 780

Wavelength,nm

Figure 7: Spectralsensitivities

of atypicalCCDcamera.

[Source:

Berns, 2001,

p.

7]

Itisclearfromthesecurvesthatthereisalarge difference inthesensitivities

ofhumansand

imaging

devices,

thus_causingalargeproblem when color_{accuracy is}

thegoal. Itis importanttorealize thatcolor_{accuracy is only} one ofthecriteria

consideredwhen

designing

adigitalcamera. "Low-lightsensitivity,imagenoise,

resolution,read-outspeed,manufacturing costs,and soon allmustbeconsidered.

Many

ofthesedesigncriteria are_mutuallyexclusive; thefinal design isalways a

compromise"

Color Management

Device-Dependent Color

Digital_{cameras, scanners,}and monitors operate intheRGB

(red,

green,and

blue)

color space.

Printing

devicesuseCMYK

(cyan,

_{magenta,yellow,} and

black)

inkto

produce prints. Therange of colors capable of

being

produced

by

eachdeviceis referred

to asthedevice gamut.Alldevices behave

differently,

whetherin the_way

they

acquire

color

information,

represent color

information,

orreproducecolorinformation

(Sharma,

Color,

2004). Forexample, ifthesamedigital image file isprinted outwithidentical

RGB values _usingtwodifferentprinters,theprints_{may look}

different,

orwhenthesame

image is scanned_usingtwodifferent scanners,theRGBpixel values_{may be different.}

"Theconclusiontodraw fromthis isthatCMYK (and

RGB)

are

basically

instructions for

adevice anddonotinthemselves tellus whatcolorapixelwillbe"

(Sharma, 2004a,

p. 14).

Therefore,

eachdevice is dependentonitsowngamut and calibration.These

differences createtheneedforcolormanagement.

Device-IndependentColor

Colormanagementsystems are_necessary toreproduce accurate colorinan

all-digitalworkflow. Thequestionsare howto color manageand

by

what standards?

The CIEhas establishedan

internationally

recognizedstandard usedfor

device-independentcoloridentification. "CIE systemsformthebasis forcolor management. The

LAB systemisusedinprofilegeneration and isalso usedin Photoshop. The

Yxy

system

2004a,

p.15).

CIELAB,

_{officially known}asCIE 1976

L*,

a*,

b*,

isthesystem of

numeric coloridentificationthatcorrespondstoa colorbasedonits positionin a3Dcolor

space. Figure 8 illustratestheLAB color space. "ThecoordinateL* standsfor

lightness,

a*

representstheposition ofthecolor on a red-greenaxis, andb* representsthe position

ofthecolor on a yellow-blue

axis"

(Sharma, 2004a,

p. 15).The

Yxy

colorsystemusesa

2-dimensionalgraph calledthe_chromaticity diagramtoplotthex and_ycoordinatesof a

color. CIEcolor specification systems are reliablemeasurement systemsthatincorporate

theuse of_{measuring instruments}to_{colorimetrically} evaluate color.The CIELAB

colorspaceis usedtocreateICCprofiles andisalsousedtocalculate Delta

E,

whichis a

quantitative_wayof_measuringthedifference betweencolors of a printedreproduction

(Sharma,

2003).

Figure8. The LAB colorspacerepresents

lightness,

hue,

and saturation.

Color Profiles

Digitaldevicessuch as_{cameras, scanners, monitors,} and printers produce color

thatis device-dependent.Thismeansthat thesame color_{may look}

different,

depending

on whichdevicereproduces it. The factthatdifferentdevicesreproducethe same color

differently

creates a

big

problem when_performingcolormanagement. Thisis the_primary

reason _whycolor profiles are necessary. Profiles areusedto transformdevice-dependent

hardware into device independentcolor spaces. "Colorspacedefinitions"

(King,

n.d.,p.

2)

are profiles usedto transformdevice-dependenthardware into device-independent

color spaces.

The International Color Consortium

(ICC)

"wasestablishedin 1993 forthe

purpose of_creatingand_promotingthestandardization of an_{open, vendor-neutral,}

cross-platform system_{for managing}

color"

(Romano &

Riordan, 2003,

p.221). The ICC isan

independentorganization whichwasfounded

by

thesecompanies:

"Adobe, Agfa,

Kodak,

Taligent, Microsoft,

Sun,

andSilicon Graphics...

[today]

theICC isopentoallcompanies

who workin fieldsrelatedtocolor

management"

(Sharma,

2004a,

p. 22). The ICC

standardized"colorspacedefinitions"_namingthem"profiles"

(King,

_{n.d., p 2). ICC}color

profiles are created_usingthedevice-independentCIELAB color space

(Sharma,

2003).

Profilesarecommunicationtools_{allowing for}thecorrectinterpretationof color

image

data,

regardless ofthetypeofhardwareor software

being

used. Profilesare

created_using acombination of softwareand

hardware,

such asspectrophotometersand

colorimeters, which are usedtomeasurethecolorsof_monitors,or printedtargets. The

accountedfor. An ICCprofile iscreatedtoadjustforthedifference betweentheknown

targetandthedevicecapabilities. Productssuch asGretagMacbeth'sEye-Oneprofile

solution system andX-Rite's ColorMasterSoftwarecanbeusedtocreateICC profiles

(Bury,

2004).

Profile Connection Space

ICCcolor management provides a standard referencespace, knownastheprofile

connection_space, whichis usedtotransmitprofileinformation

(King,

n.d.). An

all-digitalworkflow canspreadover awide _varietyofmonitors and_printing technologiesin

different locations. These devicesrequireareference point sothat

they

can all represent

accuratecolorwithout

having

tobeconnectedtoone another,asina closed

loop

system.

The difference betweenaclosed

loop

andanopen

loop

color management systemisthe

ability foran open

loop

systemtocommunicate accurate colorinformationto_{any device}

operatingthrough theprofileconnection space.Inan open

loop

_workflow,each device is

independent,

andconsistentcolor_{using ICC}profilesrequires communicationwiththe

profileconnection space. Sharma

(2004a)

furtherstatesthat

...

[every]

devicemusthave a profile.

Thus,

weneed a scanner profile forascanner,amonitor profilefora_monitor, and aprinterprofilefora

printer. Agoldenrule ofcolor managementis 'image +

profile' ...Ina typicalworkflow, images fromascanner_{may be 'brought}into' theprofile

connectionspace_usingthescanner profile. Toprinttheimageitwould

be 'sentout'

fromtheconnectionspaceto aprinter_using a printer profile.

Thus,

color management operations requirea source and adestination

profile-we needtoknow wheretheimage_{is coming from}and whereit is

going. Anothergolden ruleincolor managementis 'profile-PCS-profile'

Proofing

systems have benefitedfromcolor-managementsystemsbasedon ICC

profilingcapabilities. Forexample,"thesetechnologieshave allowedinkjetsimulations

offinalpress

results"

(Felici,

2004,

p. 7). Figure 9 illustrates howan open

loop

workflow

usestheprofile connection space.

/

Profile

connection

V

Figure 9. Profile Connection Spaceas partof an open

loop

workflow. [Source:

Sharma, 2004a,

p.

157]

Photographic Considerations

Filmvs. Digital

Imaging

technologieshave undergoneatransformationinrecentdecades.

equipment,

leading

tocolor management

issues,

aswellastotheneedtounderstandthe new challenges presented

by

thisnew technology.

Digital imagecapture varies_greatlyfromfilmcapture. Fraserand Raw

(2004)

state offerthisexplanation:

Filmmimicstheeye's responseto

light,

whichis

highly

non-linear.. .

Perhapsthe_{biggest difference between shooting film}and_{shooting digital}

isthe _waythe twodifferentmedia respondtolight. Filmrespondstolight thesame_wayour eyes

do,

butsilicondoesnot(p.l).

Forexample, filmcansuffer_reciprocity

failure,

which "meansthat theresponse

(density)

of a photographic material cannotbepredicted _{solely from}the totalexposurea film hasreceived(exceptover a smallrangeofexposure

times)"

(Stroebel, 1990,

p. 121).

In other_words,

during

long

shutter_times,the

build-up

of exposurebeginstoblockthe light

illuminating

onthefilmastimepasses; this slowsdowntheresponsetimeofthe film.

Thus,

alongerthananticipatedexposureisrequiredto_completelyexposethe image.

Filmnot_{only has}anonlinear responseto

light,

italsocomes with anembedded,

predeterminedtonalcurveresponse. Each different filmtypehasaunique_curve, applicable foradesiredresult. Different films have differentqualities_{for varying}

reproductionobjectives.These differencescausethefilmtorespond

differently

to the illuminant intermsofbothtonaland color response. Filmismadetogive a_pleasing

resultinresponsetoanaveragescene. Differenttypesoffilm may beused when

during

imagecapture and alsothroughexposure and

developing

variations

(Kodak,

2005).

Digital

imaging

technology

differsfrom film

technology

_{mostly in}thedigital

imaging

sensors responsetoanilluminate.Theterm"raw"inreferencetofiletypeis

used inconjunction withdigital

imaging

technologies.Arawdigitalfile isadirectrecord

oftheluminancedatacollected

by

thedigital

imaging

sensors. Rawfilesaremadeof

unprocessedlinear datathat are captured

by

the

imaging

sensors, whichcountthe

photons_reachingthesensor.

Wherefilm hasa predetermined embeddedtonalcurve,rawcameradata does not.

Typicaldigital

imaging

sensors thatacquiredata inthis formare referredtoas"mosaic

sensors"

or"colorfilter

array"

cameras. Color filter arraycamerashave a

two-dimensional arraythatismade_upof rowsand columns of photosensitivedetectors. These

detectorsare_usuallymade_upofCCD (charge-coupled

device)

orCMOS

(complementary

metaloxide _{semiconductor)}technology.Thesearethesensorsthatcount

thephotons andthus"produce achargethat's

directly

proportionalto theamount oflight

thatstrikesthem"

(Fraser, 2004b,

p.l).

The initialdatacaptured

by

thesensorsis ingrayscale.

Hence,

thecolorfilter

array isalsoresponsiblefor creatingcolorimages usingthegrayscale data. This is

possiblebecauseeach elementin thecolorfilter array is filteredsothat_{it is only}

responsivetored, green,orblue light. Thecolor receptiveelements are_patterned,

commonly inaBayerpattern,sothateach_sensorycapturestherespectivelight fora

ring,"

whichisstoredinthemetadata ofthe

file,

thegrayscaleinformationforeach pixel

isconvertedtocolorinformationwhenthefileenterstherawconverter. Becausethe

photo sensors are spacedin a_{pattern, the}raw converter also relies onthemetadatato

interpolatethe"missing" colorinformationfortherespectivecolor

by looking

toa

pixel's nearest neighbor.Thisprocess isreferredtoas

demosaicing

(Fraser,

2004b).

The Raw Digital Image

Whena rawdigital fileentersintoa raw converter andgoesthrough the

demosaicing

process,it isthen_readytobe"developed."

Developing

a raw image file

allows forthe

following

imageaspectstobeselected: white

balance,

colorimetric

interpretation,

gamma_correction,and noise reduction.

Whitebalancecanbeseton_camera;

however,

this _{is simply}acquired as

informationwhichis addedto thefile'smetadata. Whenaraw fileentersintotheraw

converter, theconverter will eitherusethe white

balancing

information fromthe

metadata ortheconverterwilldeterminethewhitebalance itself

by

_analyzingtheimage.

However,

whitebalancecan alsobeselected or adjusted

by

the operator.

Colorimetric interpretationof arawfilerequirestherawconvertertoassign

"specificcolor meaningsto thered,green, andbluepixels, usually ina_{colorimetrically}

definedcolor spacesuch asCIE

XYZ"

(Fraser,

2004b,

p.3).

Gammacorrectionisthemostimportantaspect oftheraw_{converter,because} we

rely onthis _steptobeableto "see"anormal

looking

image.Becausedigital

imaging

mustbe appliedtoit inordertoaccountforthe differenceintonalresponsebetweenraw

dataandboth filmandthehumaneye. "Theraw converter applies gammacorrectionto

redistributethe tonalinformation sothatitcorresponds more_closelyto the_wayour eyes

seelightand

shade"

(Fraser, 2004b,

p.3). Thegamma/tonecurvedeterminesthe

light-to-dark_relationshipwithinthevisible

image,

anditcan alsobeadjusted

by

theoperator

inan efforttoenhancethedesiredresults createdthroughtherawconversionresponse.

Thenoise_{reduction, antialiasing,} and_sharpeningsteps

help

toensuretheimage

detailthat_{may be}compromisedthroughthe

demosaicing

process. Digitalrawconverters

are_relatively newto the

imaging

industry;

priorto their

introduction,

raw conversion

most oftentookplace withinthecamera,beforethedigital file wasdownloadedtoa

computer.Theimportantaspectofoff-cameradigitalraw convertersisthat

they

allowfor