COLOURENGINEERING

Achieving Device IndependentColour

Edited by

Phil GreenColour Imaging Group, London College of Printing, UK

and

Lindsay MacDonaldColour & Imaging Institute, University of Derby, UK

Innodata0470854138.jpg

COLOURENGINEERING

Wiley-SID Series in Display Technology

Editor:Anthony C. LoweThe Lambent Consultancy, Braishfield, UK

Display Systems:Design and ApplicationsLindsay W. MacDonald and Anthony C. Lowe (Eds)

Electronic Display Measurement:Concepts, Techniques and InstrumentationPeter A. Keller

Projection DisplaysEdward H. Stupp and Matthew S. Brennesholz

Liquid Crystal Displays:Addressing Schemes and Electro-Optical EffectsErnst Lueder

Reflective Liquid Crystal DisplaysShin-Tson Wu and Deng-Ke Yang

Colour Engineering:Achieving Device Independent ColourPhil Green and Lindsay MacDonald (Eds)

Published in Association with theSociety for Information Display

COLOURENGINEERING

Achieving Device IndependentColour

Edited by

Phil GreenColour Imaging Group, London College of Printing, UK

and

Lindsay MacDonaldColour & Imaging Institute, University of Derby, UK

Copyright 2002 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester,West Sussex PO19 8SQ, England

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Contents

Colour plate captions xv

Contributors xvii

Series Preface xxi

Preface xxiii

1 Light and colour 1Arthur Tarrant1.1 Luminous Flux 11.2 Illuminance 41.3 Luminous Intensity 61.4 Luminance 71.5 Units 8

1.5.1 Obsolete units 81.5.2 Radiometric units 8

1.6 Transmission Measurements 91.6.1 Direct transmission 91.6.2 Diffuse transmission 101.6.3 Transmittance 101.6.4 Optical density and absorbance 111.6.5 The Beer–Lambert law 11

1.7 Reflection Measurements 121.7.1 Specular and diffuse reflection 121.7.2 Surface texture 131.7.3 Reflectance 13

1.8 Colour Rendering and Metamerism 141.8.1 Colour rendering 141.8.2 Metamerism 16References 17

2 Instruments and methods for colour measurement 19Danny Rich2.1 Introduction 19

2.1.1 The need for colorimetry 19

vi CONTENTS

2.1.2 The principles of colorimetry 202.1.3 Making the transition from what we ‘see’ to quantifying

how we ‘match’ a colour 202.2 Visual Colorimetry 21

2.2.1 A method to uniquely map the colour of lights and objects 212.2.2 Development of the CIE method of visual colorimetry 222.2.3 Applications of visual colorimetry 252.2.4 Disadvantages of visual colorimetry 27

2.3 Analogue Simulation of Visual Colorimetry 272.3.1 Replacing the human eye with an optoelectronic sensor 272.3.2 Substituting coloured filters to approximate the CIE

colour-matching functions 272.3.3 Assessing the ‘goodness of fit’ of a set of colorimeter filters 302.3.4 Schematic description of analogue filter colorimeters 302.3.5 Disadvantages of analogue filter colorimeters 31

2.4 Digital Simulation of Visual Colorimetry 322.4.1 Replacing the analogue filters with an abridged spectrometer 322.4.2 Assessing the ‘goodness of fit’ of abridged spectrometers 332.4.3 Schematic description of digital spectrocolorimeters 342.4.4 Advantages and disadvantages of digital spectrocolorimeters 34

2.5 Selecting and Using Colorimeters and Spectrocolorimeters 352.5.1 Reading and understanding specifications

and technical literature 352.5.2 Verifying performance specifications 382.5.3 Standards of colour and colour-difference 382.5.4 Sources of error and uncertainty in the measurement

of reflectance, transmittance and radiance 392.6 Geometric Requirements for Colour Measurements 40

2.6.1 Colour measurements from self-luminous objects 402.6.2 Colour measurements from reflecting or transmitting objects 41

2.7 Conclusions and Expectations 452.7.1 Current CIE activities in colour and colour-difference

measurements 452.7.2 Quality management systems and colour measurements 45References 46

3 Colorimetry and colour difference 49Phil Green3.1 Introduction 493.2 Colorimetry 49

3.2.1 Calculation of chromaticity 523.2.2 Calculation of CIE 1976 L∗ a∗ b∗ 533.2.3 Inversion of colorimetric transforms 553.2.4 Implementation issues 56

CONTENTS vii

3.3 Colour Difference 563.3.1 Problems with using UCS colour difference 583.3.2 Acceptability of colour differences 59

3.4 Overcoming the Limitations of UCS Colour Differencewith Advanced Colour Difference Metrics 603.4.1 CMC 603.4.2 BFD 613.4.3 CIE94 633.4.4 CIEDE2000 65

3.5 Advanced Colour Difference Metrics in Colour Imaging 673.5.1 Basis conditions 673.5.2 Colour difference in complex images 683.5.3 Acceptability and perceptibility 693.5.4 Large vs. small differences 70

3.6 Deriving Colour Difference Tolerances 703.6.1 Sample preparation 713.6.2 Psychophysical experiments 713.6.3 Observer variability and experience 713.6.4 Calculating colour tolerances from experimental data 723.6.5 Calculation of discrimination ellipsoids

and tolerance distributions 723.6.6 Calculation of parametric constants in weightings functions 733.6.7 Calculation of acceptability thresholds 74

3.7 Conclusion 74References 75

4 The CIE 1997 colour appearance model: CIECAM97s 79M. Ronnier Luo4.1 Introduction 794.2 CIE 1997 Colour Appearance Model: CIECAM97s 81

4.2.1 Structure of the model 814.2.2 Colour attributes 834.2.3 Colour appearance phenomena predicted by the model 84

4.3 Testing Colour Appearance Models Using Colour AppearanceData Sets 894.3.1 Colour appearance data sets 894.3.2 Psychophysical methods 894.3.3 Testing colour appearance models’ performance 91

4.4 Recent Modifications to CIECAM97s 954.5 Conclusion 95

References 95Appendix 1. Computation Procedures for CIECAM97s 97

Forward CIECAM97s colour appearance model 97

viii CONTENTS

Reverse CIECAM97s colour appearance model 101Worked examples for the CIECAM97s colourappearance model 103

5 Colour notation systems 105Peter A. Rhodes5.1 Introduction 1055.2 Colour Fidelity 1065.3 Colour Notation Systems 106

5.3.1 Introduction and definitions 1075.3.2 Defining colour notation systems 1085.3.3 Device-dependent specification systems 1085.3.4 CIELAB and CIELUV 1105.3.5 TekHVC 1115.3.6 Munsell 1125.3.7 The DIN colour system 1145.3.8 Natural colour system 1155.3.9 The OSA-UCS colour system 1175.3.10 Colour naming systems 1185.3.11 Other colour notation systems 120

5.4 Interrelating Multiple Notation Systems 1205.4.1 Implementing computer-based systems 122

5.5 Summary 124References 125

6 Overview of characterization methods 127Phil Green6.1 Introduction 1276.2 Numerical Models 129

6.2.1 Regression methods used in characterization 1306.2.2 Domain 1326.2.3 Training sets 133

6.3 Look-up Tables with Interpolation 1346.3.1 Interpolation 1356.3.2 LUT implementation 138

6.4 Evaluating Model Accuracy 138References 140

7 Methods for characterizing displays 143Roy Berns and Naoya Katoh7.1 Introduction 1437.2 GOG Model for CRT Displays 143

CONTENTS ix

7.3 The Extended GOGO Model 1457.4 Calibration, Characterisation, and Gamma 151

7.4.1 Determining the colorimetry of each channel 1537.4.2 Determining the OETF of each channel 155

7.5 Numerical Example of A Colorimetric Characterization 1577.5.1 Characterization summary 162

7.6 Conclusions 163References 163

8 Methods for characterizing colour scanners and digital cameras 165Tony Johnson8.1 Introduction 165

8.1.1 Colour management of device-dependentand device-independent systems 166

8.1.2 Principles of scanner characterization 1688.2 Use of the ANSI/ISO Scanner Characterization Target 1698.3 Use of the Target in Scanner Characterization 170

8.3.1 Evaluation of colour digitizers 1718.3.2 Evaluation of ‘gamut compressed’ colour digitizer 1728.3.3 Signal-to-noise ratio 1738.3.4 Scanner repeatability 173

8.4 Characterization Procedures 1748.4.1 Analytical solution to first-order equations 1748.4.2 Least-squares fitting to a first-order model 1758.4.3 Least-squares fitting to a higher-order model 1758.4.4 Non-linear transformation 1768.4.5 Dye modelling algorithms 1768.4.6 Look-up table with interpolation 177

8.5 Summary 177References 178

9 Color processing for digital photography 179Jack Holm, Ingeborg Tastl, Larry Hanlon and Paul Hubel9.1 Introduction 1799.2 Background 180

9.2.1 Color processing approaches: uncalibrated versuscalibrated image data 181

9.2.2 Dealing with calibrated and uncalibrated image datain a common architecture 182

9.2.3 The meaning of device-independent color as appliedto digital photography 183

x CONTENTS

9.3 Open Systems Architecture and Workflow Options 1859.3.1 Digital camera image processing steps related to the three

different workflow options 1869.3.2 Device-independent color via exchange of standard

output-referred image data and application of color rendering 1879.3.3 Device-independent color via deferred processing 193

9.4 Digital Camera Image Processing Choices: ComponentSelection, Characterization Methods, and Processing Algorithms 1949.4.1 Capture of raw image data 1949.4.2 Linearization to focal plane irradiance 1959.4.3 Flare removal 1969.4.4 Adopted white selection 1969.4.5 White balancing 2009.4.6 Demosaicing 2019.4.7 Determination of transformations to scene-referred

colorimetry estimates 2049.4.8 Color rendering to standard output-referred representations 2119.4.9 Encoding, compression, and file formats for exchange 2129.4.10 Color rendering for output 213References 213Appendix 1: Terminology 217

10 Characterizing hard copy printers 221Phil Green10.1 Introduction 221

10.1.1 Halftoning 22210.2 Printer Models 224

10.2.1 Block dye model 22410.3 Physical Models 226

10.3.1 Density 22710.3.2 Dot area models 22710.3.3 Colorant models 232

10.4 Numerical Models and Look-up Tables 23610.5 Black Printer 236

10.5.1 Spectral grey-component replacement 23710.6 Black Generation Algorithm 23810.7 The Reverse Model 24210.8 RGB Printers 242

References 243

11 Color management and transformation through ICC profiles 247Dawn Wallner11.1 The Need for Color Management 24711.2 The PCS 24911.3 The ICC Profile 250

CONTENTS xi

11.3.1 ICC profile types 25111.3.2 Use of ICC profiles 252

11.4 The ICC Profile Structure 25311.4.1 ICC profile creation 25411.4.2 ICC device models 25511.4.3 ICC rendering intents 25711.4.4 Required processing tags 25711.4.5 Private tags 258

11.5 Profile Use 25811.5.1 Color management on the web and ICC profiles 25811.5.2 Sample ICC profile interfaces 25911.5.3 ICC profile validation software 26111.5.4 What to look for in the future at the ICC website 261

11.6 Conclusion 261References 261

12 Colour gamut determination 263Marc Mahy12.1 Introduction 26312.2 Overview of Gamut Calculations 26412.3 Terminology 26512.4 A General Printer Model 266

12.4.1 The Neugebauer model 26612.4.2 A localised Neugebauer model 268

12.5 Gamut Calculations 26912.5.1 Introduction 26912.5.2 Boundaries of a 3-ink process 26912.5.3 Boundaries of a 4-ink process 27512.5.4 Analytical gamut calculation: well-behaved Neugebauer

model without ink limitations 27712.5.5 Gamut of a well-behaved localised Neugebauer model 27912.5.6 Gamut of a less well-behaved model 280

12.6 Gamut Representations 28012.7 Conclusions 282

References 283Appendix 1. Neugebauer Equations 285

Neugebauer equations for a 1-ink process 285Neugebauer equations for a 2-ink process 285

Appendix 2. Analytical Gamut Calculation: Well-Behaved NeugebauerModel with Linear Ink Limitation 286

13 Colour gamut mapping 297Ján Morovic13.1 Introduction 297

13.1.1 Gamut mapping aims 299

xii CONTENTS

13.2 Terminology 29913.3 Factors Affecting Gamut Mapping 300

13.3.1 Colour reproduction system 30113.3.2 Gamut boundary calculation 30213.3.3 Colour space 30313.3.4 Media 30313.3.5 Images 304

13.4 Parameters of Gamut Mapping 30613.4.1 Type of mapping 30613.4.2 Choice of original gamut 306

13.5 Gamut Mapping and Colour Rendering Intents 30713.5.1 Colour rendering intents 30713.5.2 Relationship between gamut mapping and rendering intents 308

13.6 Overview of Gamut Mapping Algorithms 30913.7 Gamut Mapping Algorithms and Colour Management Frameworks 311

13.7.1 Recommended gamut clipping algorithm 31113.7.2 Recommended gamut compression algorithm 312

13.8 Summary 31413.9 Acknowledgements 314

References 314

14 Implementation of device-independent color at Kodak 319Kevin Spaulding and Edward Giorgianni14.1 Introduction 31914.2 Image States 32014.3 Standard Image State Color Encodings 323

14.3.1 Criteria for selection of RIMM/ROMM RGB colorencodings 325

14.3.2 ROMM RGB color encoding 32814.3.3 ROMM RGB conversion matrix 33114.3.4 Nonlinear encoding of ROMM RGB 33114.3.5 RIMM RGB color encoding 33114.3.6 RIMM RGB conversion matrix 33214.3.7 Nonlinear encoding of RIMM RGB 33314.3.8 ERIMM RGB color encoding 33314.3.9 Nonlinear encoding of ERIMM RGB 334

14.4 Image States in a Color-Managed Architecture 33514.5 Implementation with JPEG 2000 338

References 339

15 Engineering color at Adobe 341James King15.1 Introduction 34115.2 The Early Black and White POSTSCRIPT Days 341

CONTENTS xiii

15.3 A Little Bit about POSTSCRIPT 34315.4 POSTSCRIPT Procedures 34515.5 Typical Workflow for Making Black and White Film 34615.6 POSTSCRIPT Level 2 34815.7 Handling Black in CMYK Workflows 35515.8 Destination Profiles in POSTSCRIPT 35715.9 Differences Between ICC Processing and POSTSCRIPT Processing 36115.10 ‘The Tail Wagging the Dog’ 36215.11 The Formation of the International Color Consortium 36315.12 Portable Document Format (PDF) 36515.13 Color Management in Other Adobe Products 36615.14 When Things Go Wrong 36715.15 The Future 36815.16 Acknowledgements 369

References 369

16 Colour management in digital film post-production 371Wolfgang Lempp and Leonardo Noriega16.1 Introduction 37116.2 Traditional Film Post-production 372

16.2.1 The film process chain 37216.2.2 The tools of the cinematographer 373

16.3 Digital Visual Effects 37416.3.1 Digitising the negative 37416.3.2 Scanner encoding scheme 37516.3.3 Matching the original negative 37616.3.4 Colour management: a potential solution 376

16.4 The Digital Studio 37816.4.1 Telecine processing 37816.4.2 The cinematic experience 37916.4.3 Colour appearance models 38016.4.4 Gamut mapping 381

16.5 Digital Cinema 38216.5.1 A standard for digital cinema distribution 38316.5.2 An architecture for film colour management 383

16.6 Conclusion 383References 384

17 Managing color in digital image libraries 385Sabine Süsstrunk17.1 Image Representations in Digital Image Libraries 38517.2 Digital Image Color Workflow 387

17.2.1 Sensor encoding 39017.2.2 Input-referred image encoding 392