Bonnier, Nicolas (2008) Contribution to Spatial Gamut Mapping Algorithms. PhD thesis Signal et Images, Departement Traitement du Signal et de l'Image, ENST p.329.

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Licence: Copyright Nicolas Bonnier

Alternative Locations: http://nicobonnier.free.fr/research/

Abstract

Achieving an accurate print reproduction of a color present in a given image might be impossible when this color is not part of the gamut of colors that the printer can reproduce. Usually the reproduction is then achieved by replacing this color with a color perceived as close in the color gamut of the printer. This mapping to another color is made by a gamut mapping algorithm. In this thesis we describe the work carried out in the development of new spatial and color adaptive gamut mapping algorithms. These algorithms act locally in the image to generate a reproduction perceived as close to the original. Their goal is to preserve both the color values of the pixels and the colorimetric relations between neighbors.

We first propose a mathematical framework encompassing the existing spatial gamut mapping algorithms. Next we introduce two new algorithms within the proposed mathematical framework. In the spatial and color adaptive compression we project each color pixel lying outside the output gamut toward the center, more or less deeply inside the gamut depending on its neighbors. In the spatial and color adaptive clipping the direction of the projection of each color pixel is a variable, set per pixel to better preserve the local energy in the resulting image. We then consider the role of the Modulation Transfer Function of the printing system in the perceived quality of the reproduction.

We design a bias-dependent algorithm to optimally compensate for the MTF of the printing system. Lastly we present the evaluation of the proposed algorithms conducted within a psychophysical experiment, its results demonstrate the improvement in the quality of reproduction.

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Table of content

I Introduction 1

I.1 Context - 1

I.2 Motivation - 1

I.3 Aims - 3

I.4 Thesis Outline - 4

II Elements of Color Science ..7

II.1 Definition of Color Gamut - 7

II.2 Handling Colors - 8

II.2.1 Colorimetry and Perception - 8

II.2.1.1 Color Temperature and Color Rendering Index - 8

II.2.1.2 Standard Illuminants - 9

II.2.1.3 Standard Illuminating and Viewing Geometry - 9

II.2.1.4 Standard Viewing Conditions - 10

II.2.1.5 Color Appearance Attributes - 10

II.2.1.6 Contrast Sensitivity Function - 11

II.2.2 CIE Color Spaces - 12

II.2.2.1 CIE 1931 XYZ (CIE XYZ) - 13

II.2.2.2 Chromaticity Coordinates x y - 13

II.2.2.3 CIE 1976 (L*, a*, b*) color space (CIELAB) - 13

II.2.2.4 CIE 1976 (L*, U*, V*) color space (CIELUV) - 16

II.2.3 Other Color Spaces - 16

II.2.3.1 IPT - 16

II.2.3.2 S-CIELAB - 17

II.2.3.3 Device Color Spaces - 17

II.2.3.4 Standard RGB Color Spaces - 18

II.2.4 Color Appearance Models - 19

II.2.4.1 CIECAM02 - 19

II.2.4.2 iCAM - 20

II.2.5 Perceptual Differences - 20

II.2.5.1 CIELAB ∆E∗ ab - 20

II.2.5.2 CIE94 ∆E∗ 94 - 20

II.2.5.3 CIEDE2000 ∆E00 - 21

II.2.6 Summary - 22

II.3 Determining and Representing Color Gamuts - 22

II.3.1 Device Characterization - 22

II.3.2 Gamut Boundary & Gamut Boundary Descriptors - 24

II.3.3 A Reference Color Gamut - 26

II.3.4 Computing Image Gamut - 27

II.3.5 2-Dimensional versus 3-Dimensional Representations - 27

II.3.6 Representation of a Selection of Gamuts - 27

II.3.7 Summary - 32

II.4 Gamut Mapping Algorithms - 32

II.4.1 Aims of Gamut Mapping Algorithms - 33

II.4.2 Reproduction Strategies - 34

II.4.2.1 Goals and Constraints - 34

II.4.2.2 Gamut Mapping and Color Rendering - 35

II.4.3 Color Space for Gamut Mapping Algorithms - 35

II.4.4 Pointwise Gamut Mapping Algorithms - 37

II.4.4.1 Gamut Clipping Algorithms - 37

II.4.4.2 Gamut Compression Algorithms - 39

II.4.5 Spatial Gamut Mapping Algorithms - 42

II.4.5.1 Compensation Approach - 43

II.4.5.2 Optimization Approach - 45

II.5 ICC Color Management - 45

II.5.1 Color Management - 45

II.5.2 International Color Consortium - 46

II.5.3 ICC Color Management Architecture - 46

II.5.4 Rendering Intents - 47

II.5.5 Recommended Gamut Mapping for the ICC Rendering Intents . . 49

II.5.6 Color Management Module - 50

II.5.6.1 Adaptive Gamut Mapping Algorithm in the ICC Architecture - 50

II.5.7 Selected Workflow in our Implementation - 51

II.5.8 Summary - 51

III Comparisons of Spatial Gamut Mapping in Common Framework 53

III.1 Introduction - 53

III.2 Mathematical Framework - 53

III.2.1 Image Decomposition - 53

III.2.2 Framework - 54

III.3 Compensation Approach - 56

III.3.1 Meyer & Barth 1989 - 56

III.3.1.1 Description - 56

III.3.1.2 Within the Framework - 59

III.3.1.3 Analysis - 59

III.3.2 Kasson 1995 - 59

III.3.2.1 Description - 60

III.3.2.2 Within the Framework - 64

III.3.2.3 Analysis - 64

III.3.3 Morovič & Wang 2003 - 65

III.3.3.1 Description - 65

III.3.3.2 Within the Framework - 67

III.3.3.3 Analysis - 67

III.3.4 Balasubramanian et al. 2000 - 68

III.3.4.1 Description - 68

III.3.4.2 Within the Framework - 70

III.3.4.3 Analysis - 70

III.3.5 Zolliker & Simon 2006 - 70

III.3.5.1 Description - 70

III.3.5.2 Within the Framework - 72

III.3.5.3 Analysis - 72

III.3.6 Farup et al. 2007 - 72

III.3.6.1 Description - 72

III.3.6.2 Within the Framework - 75

III.3.6.3 Analysis - 76

III.3.7 Kolås & Farup 2007 - 76

III.3.7.1 Analysis - 78

III.3.7.2 In the framework - 78

III.4 Optimization Approach - 78

III.4.1 Nakauchi et al. 1995 - 79

III.4.1.1 Description - 79

III.4.1.2 Within the Framework - 82

III.4.1.3 Analysis - 82

III.4.2 McCann 1999 - 82

III.4.2.1 Description - 82

III.4.2.2 Within the Framework - 85

III.4.2.3 Analysis - 85

III.4.3 Kimmel et al. 2005 - 86

III.4.3.1 Description - 86

III.4.3.2 Within the Framework - 89

III.4.3.3 Analysis - 89

III.5 Discussion - 89

III.6 Summary - 91

IV New Approaches for Adaptive Gamut Mapping Algorithms 93

IV.1 Introduction - 93

IV.2 Operators in the Common Framework - 93

IV.2.1 Color Space for Spatial Gamut Mapping Algorithms - 94

IV.3 Image decomposition - 94

IV.3.1 Gaussian Filters - 95

IV.3.2 5D Bilateral Filtering in CIELAB Space - 95

IV.3.3 Decomposition in two bands - 97

IV.3.4 Spatial Filter Size - 98

IV.3.5 Filter Sizes in 5D Bilateral Filter - 102

IV.3.6 Experiment: Impact of σd and σr on image decomposition - 103

IV.3.6.1 Analysis - 103

IV.3.6.2 Selection of σd and σr - 106

IV.4 Function g applied to the Low-pass Band - 106

IV.4.1 Lightness Scaling of Ilow - 106

IV.4.1.1 Choice of color space - 106

IV.4.1.2 Black Point Compensation and Gamut Mapping - 107

IV.4.2 Gamut Clipping - 107

IV.5 Function k applied to the High-pass Band - 110

IV.6 Adaptive Merging and Mapping f of the two Bands - 111

IV.6.1 Merging - 111

IV.6.2 Adaptive Mapping - 111

IV.6.3 Spatial and Color Adaptive Compression (SCACOMP) - 112

IV.6.3.1 Modified Projection in SCACOMP - 113

IV.6.4 Spatial and Color Adaptive Clipping (SCACLIP) - 114

IV.6.4.1 Modified Energy Minimization in SCACLIP - 115

IV.7 Summarizing Proposed Algorithms - 116

IV.7.1 SCAGMAs: Differences and Advantages - 117

IV.7.2 Comparing SCACOMP and SCACLIP - 118

IV.8 Summary - 118

V Compensating the Printer Modulation Transfer Function 129

V.1 Introduction - 129

V.2 Characterizing the Printer MTF - 131

V.2.1 Modulation Transfer Function - 131

V.2.2 Specificity of the MTF of a Printing System - 132

V.2.2.1 Halftoning - 132

V.2.2.2 Resolution - 134

V.2.2.3 Parameters in Characterization - 136

V.2.3 Existing Characterization Technique - 136

V.2.4 Jang and Allebach’s Characterization - 137

V.2.5 Experimental MTF Measure - 139

V.2.6 Comparison with Other Characterization Methods - 141

V.2.7 Summary - 143

V.3 Compensating for the Printer MTF - 144

V.3.1 Deconvolution - 144

V.3.2 Wiener Filter - 145

V.3.3 Unsharp Masking - 145

V.4 Compensation in the Spatial and Color Adaptive Rendering Workflow . . 145

V.4.1 MTF Data - 146

V.4.2 In the Workflow - 146

V.4.3 Locally Adaptive Compensation - 148

V.5 Discussion - 149

V.6 Experimental Results - 150

V.6.1 Objective Results - 150

V.6.2 Results on test images - 150

V.7 Over-compensation - 150

V.8 Summary - 153

VI Evaluation 155

VI.1 Introduction - 155

VI.2 Psychophysical Experiments - 155

VI.2.1 Observers - 156

VI.2.2 Types of Experimental Method - 156

VI.2.3 Reproduction Workflow - 159

VI.2.4 Viewing Conditions for Evaluation - 160

VI.2.5 Test Images - 161

VI.2.6 Gathering and Processing Data - 163

VI.2.6.1 Ranking Experiment - 163

VI.2.6.2 Category Judgment - 165

VI.2.7 Summary - 166

VI.3 Evaluation using Image Quality Metrics - 166

VI.3.1 Image Quality Metrics - 166

VI.3.2 Local Compression Ratios and Contrast Histograms - 167

VI.4 Survey: Evaluation of SGMAs by their Authors - 167

VI.4.1 Summary - 169

VI.5 Experiment 1 - 170

VI.5.1 Setup - 170

VI.5.2 Gathering and Processing Data - 176

VI.5.3 Analyzing the Results of the Experiment - 177

VI.5.3.1 Global results - 177

VI.5.3.2 Evaluation Per Image - 177

VI.5.3.3 Evaluation Per Observer - 183

VI.5.4 Comments - 183

VI.5.5 Summary - 183

VI.6 Experiment 2: Evaluation of the Gain of the MTF Compensation in SCAGMAs - 192

VI.6.1 Setup - 192

VI.6.2 Gathering and Processing Data - 200

VI.6.3 Analyzing the Results of the Experiment - 202

VI.6.4 Comments - 206

VI.6.5 Summary - 206

VII Conclusions 213

VII.1 Overview of Findings - 213

VII.2 Future Work - 215

Bibliography 225

Index 227

A Instruments 227

B Output Devices 229

C Test Images 235

D Pixels Outside the Adobe RGB 98 Gamut in CIELAB/SCID Images 241

E Mask Corresponding to Pixels Inside the Gamut of the Océ TCS 500 in Test Images for Experiment 1 243

F Mask Corresponding to Pixels inside the Gamut of the Océ ColorWave 600 in Test Images for Experiment 2 247

G Raw Data, Experiment 1 249

H Raw Data, Experiment 2 255

I Resulting Images Printed with the Océ ColorWave 600 261

I.1 Results of BPC and HPMin∆ E Clipping - 263

I.2 Results of SCACOMP with MTF Compensation - 269

I.3 Results of SCACOMP with MTF Over-compensation of 25 % - 275

J Legal 281

K Résumé Long 283

K.1 Introduction - 283

K.1.1 Contexte - 283

K.1.2 Motivation - 284

K.1.3 Objectifs - 285

K.1.4 Contenu de la Thèse - 285

K.2 La Gamme de Couleur - 286

K.3 La Mise en Correspondance de Gammes de Couleur - 287

K.3.1 Objectifs des Algorithmes de Mise en Correspondance de Gammes

de Couleur - 287

K.3.2 Les Algorithmes de Mise en Correspondance de Gammes de Couleur 288

K.3.2.1 Les Algorithmes de Mise en Correspondance de Gammes

de Couleur par Projection - 289

K.3.2.2 Les Algorithmes de Mise en Correspondance de Gammes

de Couleur par Compression - 289

K.3.3 Les Algorithmes de Mise en Correspondance de Gammes de Couleur Spatiaux - 291

K.3.3.1 Approche par Compensation - 292

K.3.3.2 L’Approche par Optimisation - 293

K.4 Un Cadre Mathématique pour les Algorithmes de Mise en Correspondance de Gammes de Couleur Spatiaux - 293

K.4.1 Le Cadre Mathématique - 294

K.4.2 Opérateurs dans le Cadre Mathématique Commun - 294

K.4.3 Objéctifs de Développement - 295

K.4.4 Discussion - 295

K.5 Développer de Nouveaux Algorithmes de Mise en Correspondance de Gammes de Couleur Spatiaux - 295

K.5.1 Résumé des Algorithmes Proposés - 296

K.5.2 Algorithmes Spatiaux: Différences et Avantages - 296

K.6 Compenser la Fonction de Transfert de Modulation du Système d’Impression 297

K.6.1 Fonction de Transfert de Modulation - 298

K.6.2 Caractériser la MTF du Système d’Impression - 299

K.6.3 Compenser la MTF du Système d’Impression - 299

K.6.4 Compensation dans le Flux Spatiallement Adaptatif - 299

K.6.5 Sur-compensation - 300

K.7 Evaluation - 300

K.7.1 Expériences Psychophysiques - 301

K.7.2 Première Evaluation - 301

K.7.3 Seconde Evaluation - 302

K.8 Conclusion et Perspectives - 302

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