Fundamentals and Applications of Colour Engineering E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Series in Display Technology
• Sprache: Englisch

Seitenzahl: 1015

Veröffentlichungsjahr: 2023

Beschreibung

FUNDAMENTALS AND APPLICATIONS OF COLOUR ENGINEERING EXPERT OVERVIEW OF THE WORLD OF COLOUR ENGINEERING IN THE 21ST CENTURY, WITH NEW, UPDATED TECHNOLOGIES AND A MATLAB TOOLBOX Fundamentals and Applications of Colour Engineering provides important coverage on topics that hold the power to extend our knowledge of colour reproduction, such as colour measurement and appearance and the methods used, with additional discussion of the technologies responsible for reproducing colour across a wide range of devices, together with the colour management systems that are used to connect devices and exchange information. Composed of 20 chapters, the Editor and his team of expert contributors consider the new ICC.2 architecture, an approach that introduces an evolutionary step in colour engineering, ensuring wider possibilities for technology. The text also considers the emerging applications for advanced colour management, such as processing spectral data, handling HDR images, and the capture and reproduction of material appearance. The text is supported by a MATLAB toolbox of relevant functions and data. Fundamentals and Applications of Colour Engineering is a useful reference for anyone involved in the reproduction of colour and a strong supplementary course potential for master???s degrees with a colour science component. Fundamentals and Applications of Colour Engineering includes information on: * Instruments and methods of colour measurement, colorimetry, and colour difference, and colour appearance * Colour spaces and colour encodings, and characterizing input devices, displays and printers * Colour gamut communication and imaging standards, high dynamic range imaging, and HDR * Sensor adjustment in colour management, open source tools for colour engineering, and colour transform evaluation Supporting active learning with the inclusion of a toolbox of relevant functions and data, Fundamentals and Applications of Colour Engineering is an essential resource for students in relevant programs of study, and for professionals within colour engineering and reproduction looking to maximize their skill set and keep their skills updated.

Leseprobe

Wiley – SID Series in Display Technology

Series Editor: Dr. Ian Sage

Advisory Board: Paul Drzaic, Ioannis (John) Kymissis, Ray Ma, Ian Underwood, Michael Wittek, Qun (Frank) Yan

Fundamentals and Applications of Colour EngineeringPhil Green

E-Paper DisplaysBo-Ru Yang

Liquid Crystal Displays - Addressing Schemes and Electro-Optical Effects, Third EditionErnst Lueder, Peter Knoll, and Seung Hee Lee

Flexible Flat Panel Displays, Second EditionDarran R. Cairns, Dirk J. Broer, and Gregory P. Crawford

Amorphous Oxide Semiconductors: IGZO and Related Materials for Display and MemoryHideo Hosono, Hideya Kumomi

Introduction to Flat Panel Displays, Second EditionJiun-Haw Lee, I-Chun Cheng, Hong Hua, and Shin-Tson Wu

Flat Panel Display ManufacturingJun Souk, Shinji Morozumi, Fang-Chen Luo, and Ion Bita

Physics and Technology of Crystalline Oxide Semiconductor CAAC-IGZO: Application to DisplaysShunpei Yamazaki, Tetsuo Tsutsui

OLED Displays: Fundamentals and Applications, Second EditionTakatoshi Tsujimura

Physics and Technology of Crystalline Oxide Semiconductor CAAC-IGZO: FundamentalsNoboru Kimizuka, Shunpei Yamazaki

Physics and Technology of Crystalline Oxide Semiconductor CAAC-IGZO: Application to LSIShunpei Yamazaki, Masahiro Fujita

Interactive Displays: Natural Human-Interface TechniquesAchintya K. Bhowmik

Addressing Techniques of Liquid Crystal DisplaysTemkar N. Ruckmongathan

Modeling and Optimization of LCD Optical PerformanceDmitry A. Yakovlev, Vladimir G. Chigrinov, and Hoi-Sing Kwok

Fundamentals of Liquid Crystal Devices, Second EditionDeng-Ke Yang and Shin-Tson Wu

3D DisplaysErnst Lueder

Illumination, Color and Imaging: Evaluation and Optimization of Visual DisplaysP. Bodrogi, T. Q. Khan

Liquid Crystal Displays: Fundamental Physics and TechnologyRobert H. Chen

Transflective Liquid Crystal DisplaysZhibing Ge and Shin-Tson Wu

LCD BacklightsShunsuke Kobayashi, Shigeo Mikoshiba, and Sungkyoo Lim (Eds.)

Mobile Displays: Technology and ApplicationsAchintya K. Bhowmik, Zili Li, and Philip Bos (Eds.)

Photoalignment of Liquid Crystalline Materials: Physics and ApplicationsVladimir G. Chigrinov, Vladimir M. Kozenkov, and Hoi-Sing Kwok

Projection Displays, Second EditionMathew S. Brennesholtz and Edward H. Stupp

Introduction to MicrodisplaysDavid Armitage, Ian Underwood, and Shin-Tson Wu

Polarization Engineering for LCD ProjectionMichael G. Robinson, Jianmin Chen, and Gary D. Sharp

Digital Image Display: Algorithms and ImplementationGheorghe Berbecel

Color Engineering: Achieving Device Independent ColorPhil Green and Lindsay MacDonald (Eds.)

Display Interfaces: Fundamentals and StandardsRobert L. Myers

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

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

Fundamentals and Applications of Colour Engineering

Edited By

Phil Green

This edition first published 2024

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Contents

Cover

Series Page

Title Page

Copyright Page

Series Editor’s Foreword

Preface

Introductory Notes

1 Instruments and Methods for the Colour Measurements Required in Colour Engineering

1.1 Introduction

1.1.1 The Need for Colorimetry

1.1.2 The Principles of Colorimetry

1.1.3 Making the Transition from What We “See” to Quantifying How We “Match” a Colour

1.2 Visual Colorimetry

1.2.1 A Method to Uniquely Map the Colour of Lights and Objects

1.2.2 Development of the CIE Method of Visual Colorimetry

1.2.3 Applications of Visual Colorimetry

1.2.4 Disadvantages of Visual Colorimetry

1.3 Analogue Simulation of Visual Colorimetry

1.3.1 Replacing the Human Eye with an Optoelectronic Sensor

1.3.2 Substituting Coloured Filters to Approximate the CIE Colour-Matching Functions

1.3.3 Assessing the “Goodness of Fit” of a Set of Colorimeter Filters

1.3.4 Schematic Description of Analogue Filter Colorimeters

1.3.5 Disadvantages of Analogue Filter Colorimeters

1.4 Digital Simulation of Visual Colorimetry

1.4.1 Replacing the Analogue Filters with an Abridged Spectrometer

1.4.2 Assessing the “Goodness of Fit” of Abridged Spectrometers

1.4.3 Schematic Description of Digital Spectrocolorimeters

1.4.4 Advantages and Disadvantages of Digital Spectrocolorimeters

1.5 Selecting and Using Colorimeters and Spectrocolorimeters

1.5.1 Reading and Understanding Specifications and Technical Literature

1.5.2 Verifying Performance Specifications

1.5.3 Standards of Colour and Colour-difference

1.5.4 Sources of Error and Uncertainty in the Measurement of Reflectance, Transmittance and Radiance

1.6 Geometric Requirements for Colour Measurements

1.6.1 Colour Measurements from Self-Luminous Objects

1.6.2 Colour Measurements from Reflecting or Transmitting Objects

1.7 Conclusions and Expectations

1.7.1 Current CIE and ISO Activities in Colour and Colour-difference Measurements

1.7.2 Quality Management Systems and Colour Measurements

References

2 Colorimetry and Colour Difference

2.1 Introduction

2.2 Colorimetry

2.3 Normalization

2.4 Colour Matching Functions

2.5 Illuminants

2.6 Data for Observers and Illuminants

2.7 Range and Interval

2.8 Calculation of Chromaticity

2.9 Calculation of CIE 1976 Uniform Colour Spaces

2.10 Inversion of CIELAB Equations

2.11 Colour Difference

2.12 Problems with Using UCS Colour Difference

2.13 Uniformity of the Components of Colour Difference

2.13.1 Chroma

2.13.2 Hue

2.13.3 Lightness

2.14 Viewing Conditions

2.15 Surface Characteristics

2.16 Acceptability of Colour Differences

2.17 Overcoming the Limitations of UCS Colour Difference with Advanced Colour Difference Metrics

2.18 CIE94

2.19 CIEDE2000

2.20 Progress on Colour Difference Metrics since CIEDE2000

2.21 3D Colour Difference

2.22 Colour Difference in High Luminance Conditions

2.23 Colour Difference Formulas Based on Colour Appearance Models

2.24 Limitations in the Use of Advanced Colour Difference Metrics in Colour Imaging

2.25 Basis Conditions

2.25.1 Illuminant

2.25.2 Illuminance

2.25.3 Sample Separation

2.25.4 Sample Size and Image Structure

2.26 Colour Difference in Complex Images

2.27 Acceptability and Perceptibility

2.28 Large vs Small Differences

2.29 Deriving Colour Difference Tolerances

2.30 Sample Preparation

2.31 Psychophysical Experiments

2.31.1 Observer Variability and Experience

2.32 Colour Difference Judgements by Observers with a Colour Vision Deficiency

2.33 Calculating Colour Tolerances from Experimental Data

2.34 Calculation of Discrimination Ellipsoids and Tolerance Distributions

2.34.1 Calculation of Parametric Constants in Weightings Functions

2.35 Calculation of Acceptability Thresholds

2.36 Evaluating Colour Difference Metrics

2.37 Conclusion

References

3 Fundamentals of Device Characterization

3.1 Introduction

3.1.1 Objectives

3.2 Characterization Methods

3.2.1 Test Charts

3.2.2 Calibration

3.2.2.1 Matching Aim Values

3.2.2.2 Optimizing Performance

3.2.2.3 Perceptual Uniformity of Device Values

3.2.2.4 Optimization for Machine Vision

3.2.3 Linearization

3.3 Numerical Models

3.3.1 Regression Methods Used in Characterization

3.3.1.1 First Order Model

3.3.1.2 Higher Order Models

3.3.1.3 Choosing the Polynomial Order

3.3.1.4 Spline Methods

3.3.1.5 Weighted Regression

3.3.2 Domain

3.3.3 Optimization

3.3.4 Noisy and Discontinuous Data

3.3.5 Machine Learning

3.4 Look-Up Tables with Interpolation

3.4.1 Packing

3.4.2 Extraction

3.4.3 Interpolation

3.4.4 LUT Implementation

3.4.4.1 LUT implementation in ICC profiles

3.5 Evaluating Accuracy – Training and Test Data

References

4 Characterization of Input Devices

4.1 Input Channels

4.2 Characterization Goals

4.3 Transform Encoding

4.4 Dynamic Range

4.5 Input Characterization Methods

4.5.1 Scanners

4.6 Targets

4.7 Modelling

4.7.1 Digital Cameras

4.8 Target-Based Characterization

4.9 Targets

4.10 Modelling

4.10.1 Spectral Sensitivity-based Methods

4.10.2 Machine Learning Methods

4.10.3 Spectral Characterization of Input Devices

References

5 Color Processing for Digital Cameras

5.1 Introduction

5.2 Basics of a Camera Sensor

5.3 The Camera Pipeline

5.3.1 Defective Pixel Correction

5.3.2 Black-Level Correction and Normalization

5.3.3 Lens Shading Correction

5.3.4 Autofocus, Autoexposure, Auto White Balance

5.3.4.1 Autoexposure

5.3.4.2 Autofocus

5.3.5 White Balance and Auto White Balance

5.3.5.1 White Balance

5.3.5.2 Manual and Auto White Balance

5.3.6 Demosaicing

5.3.7 Noise Reduction

5.3.8 Color Space Transform to Device-Independent Color Space

5.3.9 Photo-Finishing/Rendering

5.3.9.1 General and Selective Color Manipulation

5.3.9.2 Global and Local Tone-Mapping

5.3.9.3 Sharpening/Noise and Grain

5.3.9.4 Image Resizing/Super-Resolution

5.3.10 Color Mapping to Final Image Encoding Color Space

5.3.11 Compression and Save to Storage

5.3.12 RAW Image Capture

5.4 Multi-Frame Processing

5.4.1 HDR Imaging

5.4.2 Low-Light/Night-Mode Imaging

5.5 Towards the Neural ISP

5.6 Concluding Remarks

Acknowledgment

References

6 Display Calibration

6.1 Introduction

6.2 From CRT to Contemporary Display Technologies

6.3 The Display Never Sleeps… Merging Television and Computer Display Standards

6.4 The Evolution of Display Calibration Capabilities

6.4.1 Gamut Mapping

6.4.2 Manual Calibration

6.4.3 One Dimensional Lookup

6.3.4 The Matrix Shaper Architecture

6.4.5 Single 3-Dimensional LUT

6.4.5.1 3DLUT Considerations

6.4.6 Hybrid Matrix Shaper Utilizing 3DLUT Followed by a 1DLUT

6.5 Measurement Set Requirements

6.5.1 Pattern Generation

6.5.2 How Many Measurements are Needed?

6.5.3 Methods to Mitigate Drift in Display Measurements

6.6 Calibration Validation Methodologies

6.6.1 Numerical Scales

6.6.2 Visual Evaluation Targets and Methods

6.7 Low Blue Light Developments

6.8 Conclusions

References

7 Characterizing Hard Copy Printers

7.1 Introduction

7.2 Properties of Hard Copy Printers

7.3 Substrates and Inks

7.3.1 Fluorescent Whitening Agents

7.3.2 Inks

7.4 Colour Gamut

7.5 Halftoning

7.6 Mechanical Printing Systems

7.7 Printing Conditions

7.8 Digital Systems

7.9 RGB Printers

7.10 Test Charts

7.11 Printer Models

7.12 Block Dye Model

7.13 Physical Models

7.13.1 Density

7.13.2 Dot Area Models

7.13.2.1 Murray-Davies

7.13.2.2 Yule-Nielsen

7.13.2.3 Clapper-Yule

7.13.2.4 Additivity Failure

7.13.3 Neugebauer

7.13.3.1 Modified and Extended Neugebauer Equations

7.13.3.2 N-Modified Neugebauer Equations

7.13.4 Vector-Corrected Neugebauer Equations

7.13.4.1 Cellular Extensions

7.13.4.2 Spectral Extensions

7.13.4.3 Evaluation of Different Forms of the Neugebauer Equations

7.13.5 Colorant Models

7.13.5.1 Masking Equations

7.13.6 Beer-Bouguer

7.13.7 Kubelka-Munk

7.13.8 Extensions

7.14 Numerical Models and Look-up Tables

7.14.1 Black Printer

7.14.1.1 Spectral Grey-Component Replacement

7.14.1.2 Black Generation Algorithm

7.15 Inverting the Model

7.16 Multi-Colour and Spot Colour Characterization

7.17 Spectral Characterization

7.18 White Ink

7.19 Reducing the Frequency of Characterization

7.20 Conclusions

References

8 Colour Encodings

8.1 Introduction

8.2 Colour Encoding Components

8.3 Colour Spaces

8.4 Device and Colour Space Encodings

8.5 Colorimetric Interpretation

8.6 Image State

8.7 Standard 3-Component Colour Space Encodings

8.8 Colour Gamut

8.8.1 Extended Colour Gamut

8.9 Precision and Range

8.9.1 High Dynamic Range

8.9.2 Negative Values

8.10 Luminance/Chrominance Encodings

8.11 Conversion to Colorimetry

8.12 Implementation Issues

8.13 File Formats

References

9 Colour Gamut Communication

9.1 Introduction

9.1.1 Device Colour Gamut and the Usable Colour Gamut

9.1.2 Colour Space

9.1.3 Factors Affecting Colour Gamut

9.1.4 Gamut of an Image

9.2 How to Describe Colour Gamuts

9.2.1 Convex Hull

9.2.2 Alpha-Shapes

9.2.3 The Segment Maxima Method

9.2.4 Gamut Based on a Printer Model

9.2.5 Gamulyt Method

9.2.6 The Mountain Range Method

9.2.7 Defining Gamut Boundaries with a Test Target

9.3 How to Obtain a Colour Gamut of a Printing System

9.4 How to Obtain a Colour Gamut of a Display

9.5 How to Calculate Gamut Volume

9.6 How to Analyse Colour Gamuts

9.6.1 Metrics for Comparing Colour Gamuts

9.6.2 Gamut Analysis of an N-Colour Printing Process

9.7 How to Visualize Colour Gamuts

9.7.1 Venn Diagram

9.7.2 Gamut Rings

9.8 How to Communicate Colour Gamuts

9.8.1 How to Encode a Colour Gamut Description

9.8.1.1 Encoding Based on CxF

9.8.1.2 Encoding Based on an ICC Profile

9.8.1.3 Encoding Based on Tab-Delimited Text Files

9.9 Summary

References

10 The ICC Colour Management Architecture

10.1 Origins of the ICC

10.2 Fundamentals of the ICC Architecture: The PCS, the ICC Profile, Transforms and the CMM

10.2.1 Range and Precision

10.2.2 Tags and Types

10.2.3 Media-Relative Colorimetry

10.2.4 Image State

10.2.5 Rendering Intents

10.2.6 Profile Classes

10.2.7 Features of ICC v4

10.2.8 Making Profiles

10.2.9 Embedding Profiles

10.2.10 CMMs

10.3 Other CMM Operations

10.3.1 Function Inversion

10.3.2 Black Point Compensation

10.3.3 Channel Preservation

10.3.4 Gamut Mapping

10.3.5 Copyright and Security

10.4 Workflows

10.5 Current Status of ICC.1

10.5.1 Limitations of ICC.1

10.6 ICC.2

10.6.1 PCS

10.6.2 Data Types

10.6.3 Multiprocess Elements

10.6.4 Calculator

10.6.5 Workflows

References

11 iccMAX Color Management – Philosophy, Overview, and Basics

11.1 Background and Philosophy Leading to iccMAX

11.2 Overview

11.2.1 Making Connections

11.2.2 Transform Connection and Application

11.2.3 Encoding Transforms

11.2.4 MultiProcessing Element Transforms

11.2.4.1 Matrix Elements

11.2.4.2 Curve Elements

11.2.4.3 Tint Array Elements

11.2.4.4 CLUT Elements

11.2.4.5 Tone Mapping Element

11.2.4.6 Calculator Element

11.2.4.7 Emissive Elements

11.2.4.8 Emission Observer Element

11.2.4.9 Reflectance CLUT Element

11.2.4.10 Reflectance Observer Element

11.2.4.11 CAM Elements

11.2.5 Profile Structure Generalization

11.3 Creating Transforms

11.4 Specification Subsets via ICSs

11.5 Domain Specific Examples

11.5.1 Photography

11.5.2 Packaging

11.5.3 Medical Imaging

11.5.4 Fine Art

11.5.5 Critical Color on Wide Gamut Displays

11.6 Getting Started with iccMAX (Where Color Engineering Comes to Play)

11.7 Conclusion

References

12 Sensor Adjustment

12.1 Introduction

12.2 Aims of Sensor Adjustment

12.3 Luminance Adjustment

12.4 Chromatic Adaptation

12.4.1 Chromatic Adaptation in Colour Management

12.4.2 Chromatic Adaptation in ICC.2

12.5 Material-Equivalent Adjustment

12.6 Local Adaptation

12.7 Incomplete Adaptation

References

13 Evaluating Colour Transforms

13.1 Introduction

13.2 Accuracy

13.2.1 Metamerism

13.2.2 Smoothness

13.2.3 Spatial Artefacts

13.2.4 Spectral Accuracy

13.2.5 Acceptability

13.2.6 Sources of Error in Colour Transforms

13.2.7 Procedures for Colorimetric Transform Evaluation

13.2.8 Media-Relative Colour Transforms

13.3 Cost

13.4 Subjective Preference

13.4.1 Test Data

13.4.2 Reporting Evaluation Results

13.4.2.1 Visualisation of Results

References

14 Appearance Beyond Colour: Gloss and Translucency Perception

14.1 Introduction

14.2 Gloss Perception

14.2.1 Perceptual Dimensions of Gloss

14.2.2 Image Cues and Partial Models

14.2.3 Factors Impacting Perceived Gloss

14.2.3.1 Shape

14.2.3.2 Illumination

14.2.3.3 Motion

14.2.3.4 Observer

14.2.4 Summary and Open Questions

14.3 Translucency Perception

14.3.1 Transparency and Translucency

14.3.2 Image Cues and Partial Models

14.3.3 Factors Impacting Perceived Translucency

14.3.4 Summary and Open Questions

14.4 Interaction among Appearance Attributes

14.4.1 Impact of Colour on Gloss and Translucency

14.4.2 Interaction between Gloss and Translucency

14.5 Impact on Colour Technologies

14.6 Conclusion

References

15 Colour Management of Material Appearance

15.1 Introduction

15.2 Material Appearance Modelling

15.2.1 Blinn‒Phong Model

15.2.2 Ward Model

15.2.3 Cook‒Torrance Model

15.3 Appearance Support in Colour Management

15.4 A Colour Management Workflow for Material Appearance

15.5 Conclusion

References

16 Color on the Web

16.1 Early History

16.2 Color on the Legacy Web

16.2.1 RGB Representations

16.2.2 Color Names

16.2.3 Color with Alpha

16.2.4 Hue-Wheel Systems

16.2.5 Gradients

16.3 Wide Color Gamut (WCG) Comes to the Web

16.3.1 The Importance of Display P3

16.3.2 WCG Raster Images, with ICC Profiles

16.3.3 Development of WCG Upgrades to Web Specifications

16.3.4 Limitations of CIELAB: Introducing OK Lab

16.4 Color on the Wide Gamut Web

16.4.1 Predefined RGB Color Spaces

16.4.2 Device-independent Color Spaces

16.4.3 WCG Gradients

16.4.4 Manipulating and Mixing Colors

16.5 HDR Comes to the Web

16.5.1 Introducing HDR

16.5.2 HDR in Canvas

16.5.3 HDR in WebGL and WebGPU

16.5.4 HDR in CSS

References

17 High Dynamic Range Imaging

17.1 Introduction and Background

17.1.1 The Human Visual System

17.1.2 Color Imaging

17.2 High Dynamic Range Imaging

17.2.1 HDR Acquisition

17.2.1.1 Single Exposure HDR Acquisition

17.2.1.2 Multi-Exposure HDR Acquisition

17.2.1.3 HDR Image Synthesis

17.2.2 HDR Image Storage

17.2.2.1 HDR Image Formats and Encoding

17.2.3 HDR Rendering

17.2.3.1 Tone Mapping

17.2.3.2 Reverse Tone Mapping

17.3 Conclusion

References

18 HDR and Wide Color Gamut Display Technologies and Considerations

18.1 Introduction

18.2 Early HDR Display Systems

18.3 Transmissive Displays

18.3.1 Liquid Crystal Display Technology

18.3.2 Global Modulation

18.3.3 Dual Modulation

18.3.4 Dual LCD Displays

18.4 Emissive Displays

18.4.1 Organic Light Emitting Diodes

18.4.2 Direct LED Displays

18.5 Projection Systems

18.5.1 Projection-LCD Dual Modulation

18.5.2 Screen Projection

18.6 Reflective Displays

18.7 Achieving Wide Color Gamuts

18.7.1 Designing Narrow Primaries

18.7.2 Multi Spectral or Multi-Primary Displays

18.7.3 Metameric Error

18.8 Spatial Display Properties

18.9 Temporal Display Properties

18.10 Signaling

18.10.1 Signal, Display and Content Properties

18.10.2 Signal Reference vs. Display Preference Modes

18.10.3 Professional vs. Consumer Displays

18.11 Characterization and Calibration

18.12 Ambient Effects

18.13 Conclusion

References

19 Colour in AR and VR

19.1 Introduction

19.2 Colour Synthesis in AR and VR Displays

19.2.1 GOG Display Model

19.2.2 Idealized Display Models

19.2.3 Spatial and Temporal Independence

19.2.4 HMD Optics

19.2.5 Measuring AR and VR Displays

19.2.6 Example: Measuring and Characterizing an AR Display

19.3 Colour Appearance in AR and VR

19.3.1 Limitations of CAMs for AR and VR

19.3.2 Chromatic Adaptation

19.3.2.1 Chromatic Adaptation in VR

19.3.2.2 Chromatic Adaptation in AR

19.3.3 Scission and Transparency in AR

19.3.3.1 Experimental Evidence for Scission in OST-AR

19.3.3.2 Interpretation of Transparency and Related Visual Effects

19.3.4 Example: Modelling an OST-AR Display and Colour Matching Results

19.4 Colour Imaging and Graphics in AR and VR

19.4.1 Colour Reproduction

19.4.2 Virtual Colour Reproduction

19.5 Conclusion

19.5.1 Open Questions in AR and VR

Acknowledgements

References

20 Colour Engineering Toolbox and Other Open Source Tools

20.1 Colour Engineering Toolbox 2.0

20.1.1 Colorimetry

20.2 Polar Calculations

20.3 Media-Relative and PCS Scaling

20.3.1 Adaptation

20.3.2 Difference

20.3.3 Characterization

20.3.4 Gamut

20.3.5 Utility Functions

20.3.6 Psychophysics

20.3.7 Documentation

20.3.8 Licensing and Use

20.4 DemoIccMax

20.5 Color.js

20.6 Little CMS

20.7 Argyll

20.8 Colour

References

Index

End User License Agreement

List of Tables

CHAPTER 03

Table 3.1 Characterization errors for an unweighted...

Table 3.2 Performance of a third order polynomial...

Table 3.3 Performance of a regression model on two...

CHAPTER 06

Table 6.1 EDID definition.

Table 6.2 Video formats.

CHAPTER 07

Table 7.1 Performance of different regression...

Table 7.2 Performance of original Neugebauer...

CHAPTER 08

Table 8.1 Standard colour encodings.

Table 8.2 File formats supporting HDR....

Table 8.3 File formats supporting ICC...

CHAPTER 09

Table 9.1 Evaluation of the printer...

Table 9.2 Information to be provided...

CHAPTER 12

Table 12.1 Comparison of material...

CHAPTER 13

Table 13.1 Transform errors for...

Table 13.2 Comparison of errors...

CHAPTER 17

Table 17.1 Summary of new standard...

CHAPTER 19

Table 19.1 Example data. A selection...

Table 19.2 AR matching data. CIE 1931...

List of Illustrations

CHAPTER 01

Figure 1.1 Typical visual colorimeter...

Figure 1.2 Colour mixture curves for...

Figure 1.3 CIE 1931 standard observer’s...

Figure 1.4 CIE Standard illuminants A and D65...

Figure 1.5 Donaldson’s six-filter...

Figure 1.6 Filter fit for a 3-filter,...

Figure 1.7 (a) Filter fits using four...

Figure 1.8 The tristimulus filter colorimeter...

Figure 1.9 The spectrocolorimeter...

Figure 1.10 Statistical distribution...

Figure 1.11 Geometry of intensity,...

Figure 1.12 CIE recommended geometries...

CHAPTER 02

Figure 2.1 Spectral locus in x,y...

Figure 2.2 Colour discrimination...

Figure 2.3 ΔC*94 varying with...

Figure 2.4 A uniform difference of...

Figure 2.5 A uniform difference of...

CHAPTER 03

Figure 3.1 Colour transform workflow.

Figure 3.2 Partitioning a colour cube...

CHAPTER 05

Figure 5.1 (a) A typical CMOS...

Figure 5.2 The typical processing...

Figure 5.3 This figure illustrates...

Figure 5.4 The white-balance procedure...

Figure 5.5 The demosaicing procedure...

Figure 5.6 Because the spectral...

Figure 5.7 Common routines used...

Figure 5.8 Two different approaches...

CHAPTER 06

Figure 6.1 Perceptual Quantizer...

Figure 6.2 Hybrid Log Gamma (HLG)...

Figure 6.3 Capabilities block diagram.

Figure 6.4 Display calibration evolution.

Figure 6.5 Multiple wide gamut...

Figure 6.6 Colorspace specifications.

Figure 6.7 An example of a “hull”...

Figure 6.8 Effects of manual calibration...

Figure 6.9 Example 1DLUT correction table...

Figure 6.10 Example signal processing...

Figure 6.11 Example 3DLUT and how its...

Figure 6.12 Example display drift tracking...

Figure 6.13 The blue shaded region represents...

Figure 6.14 A sample white spectrum showing...

Figure 6.15 Visual of Method 2. The dark blue...

CHAPTER 07

Figure 7.1 Block dye model...

Figure 7.2 Halftone dots of the...

Figure 7.3 Cellular extensions.

Figure 7.4 Kubelka-Munk model...

Figure 7.5 Convergence of Dcmy...

CHAPTER 09

Figure 9.1 An example of a 3D colour...

Figure 9.2 CMYK gamut boundary target...

Figure 9.3 Individual tetrahedron...

Figure 9.4 Projection of the...

Figure 9.5 Example of 2D visualization...

Figure 9.6 A 3D colour gamut of sRGB...

Figure 9.7 Comparison of two colour...

Figure 9.8 Gamut rings for Rec. 2020...

CHAPTER 10

Figure 10.1 Connection via the PCS.

Figure 10.2 Elements of a LUT-based...

Figure 10.3 Perceptual reference...

CHAPTER 11

Figure 11.1 ICC.2 spectral and...

Figure 11.2 Multiplex identification...

Figure 11.3 Example MCS connection...

CHAPTER 12

Figure 12.1 ICC.1 chromatic adaptation...

Figure 12.2 Chromatic adaptation in...

Figure 12.3 Simultaneous contrast...

CHAPTER 13

Figure 13.1 Colorimetric transform evaluation.

Figure 13.2 Colorimetric differences...

Figure 13.3 Colorimetric differences...

CHAPTER 14

Figure 14.1 (a) Appearance can be split...

Figure 14.2 (a) Higher contrast makes...

Figure 14.3 (a) Metelli’s episcotister...

Figure 14.4 (a) Although mean saturation is...

Figure 14.5 (a) It is challenging to measure...

CHAPTER 15

Figure 15.1 Geometric representation...

Figure 15.2 Representation of specular...

Figure 15.3 Representation of half...

Figure 15.4 A workflow of connecting...

Figure 15.5 BRDF workflow using multiplex...

Figure 15.6 TIFF output of iccMAX BRDF...

CHAPTER 16

Figure 16.1 Oklab gamut diagram with...

Figure 16.2 Gamut mapping by Chroma...

CHAPTER 17

Figure 17.1 Dynamic range illustration...

Figure 17.2 Visualization of Camera...

Figure 17.3 The corresponding camera...

Figure 17.4 Global tone mapping curves...

Figure 17.5 Global tone mapping...

Figure 17.6 Example illustration...

Figure 17.7 The general architecture...

CHAPTER 18

Figure 18.1 The Camera Obscura is a...

Figure 18.2 Contrast ratios achievable...

Figure 18.3 Main modulation components...

Figure 18.4 Behavior of a Full Area...

Figure 18.5 Dual modulation signal...

Figure 18.6 Comparison between RGB...

Figure 18.7 Example and concept of...

Figure 18.8 ITU-R Rec. BT.709, P3...

Figure 18.9 Comparison of the content...

Figure 18.10 Examples of HDR content...

Figure 18.11 Spectral power distribution...

Figure 18.12 White LEDs are the most...

Figure 18.13 RGBW or RGB+White displays...

Figure 18.14 Small feature rendering...

Figure 18.15 Difference between the...

Figure 18.16 The key impacts on display...

Figure 18.17 Displays with different...

CHAPTER 19

Figure 19.1 AR and VR Examples. Image...

Figure 19.2 Desktop OST-AR setup...

Figure 19.3 OST-AR experiment...

Figure 19.4 Induction and transparency...

Figure 19.5 AR matching results...

Guide

Cover

Series Page

Title Page

Copyright

Table of Contents

Series Editor’s Foreword

Preface

Introductory Notes

Begin Reading

Index

End User License Agreement

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Series Editor’s Foreword

The central questions of colour engineering: “Are these two objects the same colour?” and “If not, are they close enough to be acceptable?” have an apparent beguiling simplicity based on the familiarity with colour that most of us share in everyday life. Display scientists and engineers know different. Comparison of reference objects with those made using different colourants, or with their representations rendered through different imaging devices, data pipelines, display technologies and hard copy devices, under different conditions of illumination and view becomes challenging, and compensating for the different behaviour of each device can be exquisitely complex. Fortunately, we do not have to walk this difficult path alone. International bodies such as CIE, ISO, ICC and SID have produced a multitude of standards and recommendations to guide best practice.

Against this background, Professor Green has provided an authoritative guide since the publication of his first book on the subject, Colour Engineering: Achieving Device-independent Colour almost 20 years ago. In this new book, Professor Green, aided by expert authors on specialist topics, brings us a thoroughly updated account of his subject, which covers the latest developments in the field. Here the reader will find guidance, formulae and best practice relating to all aspects of the colour recording, manipulation and reproduction pipeline with specialist chapters on such diverse topics as HDR rendering, AR and VR applications, web colour management and the impact of surface texture on colour perception and rendering. The text is logically and progressively presented, with sections covering the fundamentals of colorimetry, characterization and calibration of input and output devices, colour transformation and management protocols, followed by specialist topics. Explicit formulae and guidance are provided throughout the text, with copious references to the underlying adopted standards and recommendation documents in addition to research papers.

Colour reproduction is a topic of supreme importance, not only in display technology but also in manufacturing, graphic arts, publishing, broadcast and software development. This book will provide an invaluable reference to practitioners in all these disciplines and also serve as a guide to advanced students and those beginning their journey in colour engineering and deserves a place on the bookshelf of all whose concern is a faithful – or enhanced – rendering of colour.

Ian SageMalvern, Worcestershire

Preface

Colour engineering, as presented in this book, represents the totality of disciplines involved in the acquisition, processing, synthesis and reproduction of colour images, using a wide range of devices. These colour imaging systems have become ubiquitous both in everyday life and in specialist, highly technical and high-volume applications.

Since the early days of digital colour imaging there has been a close collaboration between academics and industry-based scientists and engineers, who meet regularly in international scientific conferences and technical committees. This text aims to support the colour engineers of tomorrow, who are likely to be working in colour in web-based applications, in phones and in HDR displays, perhaps more so than in the more established industries of cameras, printers and SDR displays. The understanding of the relationship between device signals and the human vision system, and the colour gamut of an imaging device, are fundamental building blocks to all these application areas. Expectations of colour fidelity are no longer limited to 2D, planar diffuse colorimetry but are extending to spectral reproduction and total material appearance in 3D. International standards, developed by technical committees in ISO, IEC and CIE, play an important role in the interoperability of these technologies and their applications. The science and engineering of matching colour across different devices and platforms is defined in the well-established ICC.1 colour management architecture, while a key development in support of the colour engineering of tomorrow is the more flexible and more advanced ICC.2 (iccMAX) architecture, which has a chapter to itself.

I have been extremely fortunate that leading figures in all these cutting-edge areas of research and development agreed to contribute chapters to this volume. I have also added a few chapters myself to round out the range of subjects covered. No claim is made to be comprehensive, as it would take many volumes to do full justice to the state of the art in this field. A small number of the chapters include material that was previously published in Colour Engineering: Achieving Device-Independent Colour (2002) but have been comprehensively updated.

Series Editor Ian Sage provided valuable insights which helped to structure the content in some of the early chapters, and Wiley staff Sandra Grayson, Becky Cowan, Katherine Wong, Martin Tribe, Dilip Varma and Durgadevi Shanmugasundram all made important contributions at different stages in the development of the book.

I should very much like to thank all those who contributed to my own journey in the field of colour, although I fear that to do so would not leave much room in this book for the content. Instead, I will mention just a few and hope not to give too much offence to everyone else. Ronnier Luo and Tony Johnson supervised my PhD, and, with others at University of Leeds and the London College of Communication, provided much colourful inspiration; my friends and colleagues at the Norwegian University of Science and Technology provide a wonderful collegiate environment dedicated to colour and imaging; the members of the International Color Consortium have shared their wide knowledge and experience; Pei-li Sun and his colleagues at National Taiwan University of Science and Technology allowed me to work with their excellent students; and my wonderful postgraduate students at LCC and NTNU and elsewhere over the years, have all added their own pieces to the puzzle. I should also particularly like to thank Eric Walowit, Peter Nussbaum and Aditya Sole, who kindly reviewed chapters in the book; and of course, to Ruth and Rosalie, my partner and daughter, who have always given their support.

The highly regarded colour scientist Danny Rich, who made an enormous contribution to colour science and standardisation activities over many decades, and wrote Chapter 1 in this book, tragically passed away in July 2022. I am grateful to Phyllis and Amanda Rich for their gracious support for the continuation of Danny’s work. The royalties from the book will be dedicated to a scholarship fund in his name (details are on the ICC web site www.color.org).

Phil GreenNTNU

1 Instruments and Methods for the Colour Measurements Required in Colour Engineering

Danny Rich

Sun Chemical

1.1 INTRODUCTION

1.1.1 The Need for Colorimetry

In the reproduction of colour and coloured images, the assessment of the colour appearance or colour match has historically been the job of experienced, trained artisans. They were schooled and apprenticed by a master colourist or artist who taught them how to hold the specimen or image, how to describe the appearance of the colour or colour difference and how to select the correct set of dyes, pigments or inks to create a workable recipe for the desired colour match. They also taught them their bias for certain colours and their methods for making any recipe uniquely their secret.

The master colourist could match any colour within the gamut of their known primaries and could find the most visually acceptable or pleasing near-match to any colour that was outside of their gamut. This approach worked well when the coloured product, be it an illuminated manuscript, a fine textile or the paint to decorate and protect the woodwork of a stately house, was conceived, reproduced and sold locally. In today’s fast moving, global marketplace, more objective and rigorous methods are required for creating, matching and reproducing coloured materials and images. This is especially true in the application of electronic imaging where the coloration process occurs in milliseconds rather than hours. Colorimetry is the technology that attempts to capture the essence of the visual sensation of the light reflected from or transmitted through coloured images or emitted by self-luminous images. Colorimetry then converts that essence into an objective nomenclature to communicate the colour or colour-differences to someone in a different place and time and still obtain the same level of fidelity and aesthetics. The remaining chapters in this book describe the engineering approaches to the creation and manipulation of coloured images and to many of the issues raised here, especially electronically communicating and reproducing coloured images, but all applications of colour engineering must begin with a basis in colorimetry.

1.1.2 The Principles of Colorimetry

Colorimetry, at its purest and most basic level, is quite simple. The technology was developed to answer one question, “Does this test colour match this reference colour?” Basic colorimetry can provide nothing more than the answer to that question. To obtain further information is to extend the technology from the measurement of colour into the measurement of colour appearance. Topics like colour difference, colour constancy or lack of constancy, corresponding colour and answers to the question, “How does this image or image element appear?” require additional assumptions and technologies beyond the scope of colorimetry. If there is one principle that needs to be kept in mind while becoming proficient in colour engineering, it is that “Colorimetry does not describe what a person sees!” Colorimetry is fully enveloped by the technology of colour matching. The details in colorimetry are found in how the colour match is created and reported. There are many excellent introductory textbooks on colorimetry that provide the history and background of colour measurements. The one by Berns [1] is particularly useful as it introduces both object mode colorimetry and imaging colorimetry.

1.1.3 Making the Transition from What We “See” to Quantifying How We “Match” a Colour

It can sometimes be difficult for the novice colour engineer of the future to be completely comfortable with this limited definition of colorimetry. As pointed out in the earlier parts of this book, colour is what you see, and the human visual system is highly adapted to the visual perception of colour. Indeed, this is true, but words are often pregnant with deeper meanings and implications. So it is with the concept of perception. The perception of colour involves many factors, most of which require the interaction of neural processes and physical phenomena that are not yet fully understood. Sensation involves fewer processes and occurs at much earlier stages in the visual system. The assessment of a colour match involves the sensation of identity or difference in the two sources of colour. Colorimetry is still a visual measurement even though we may utilise a colorimeter to make the measurements more precise. In the following sections the various ways to construct a colorimeter will be described, as well as how to validate that the colorimeter is consistent with its design intent and how that colorimeter can be used to answer the question of what reference colour is matched by the test colour. From an understanding of visual colorimetry, the development of an international standard of colorimetry can be understood and finally its application to automatic, electronic colorimeters. In the years since the publication of the first edition of this book, many new international standards have been published providing guidance on the best practices for colour measurements.

Colorimetry was first commercialised for the characterisation of materials, mainly textiles and later for decorative coatings, commonly known as paints. Plastics and inks used for image reproduction by printing were added much later. Because the application was most analytical in terms of the content of colouring media in the materials the objects took on a central role. The traditional description of colorimetry was in terms of a triad of contributors, usually abbreviated as: “Source + Object + Observer”. This paradigm is not useful when describing the measurements of the colours in image reproduction. The image may be in the form of a reflective object, or a transmitting object or it may be completely self-luminous. Many times the desire is to have two or more of these viewing modes produce matching colours. For image reproduction, the more fundamental paradigm of a colour stimulus function and an observer function is more appropriate. This paradigm will be used in the following sections.

The colour stimulus function, Φ(λ), is the true complement to the observer functions or spectral tristimulus values, X(λ), Y(λ), Z(λ). The tristimulus values are then computed from the spectral product, wavelength by wavelength, of the colour stimulus function and the observer function. In most cases, the actual functional dependence is not known so the functions are approximated by tabulations at discrete points. The exact number of points depends on the spectral range and the required accuracy. The CIE has determined that the visible spectral range extends from 360nm to 830nm and recommends for highest accuracy, a table with 471 values or intervals of 1nm.

(1.1)

When performing colorimetry on self-luminous objects, displays, projections, the colour stimulus function is measured directly. When performing colorimetry on objects, the colour stimulus function is derived from the measurement of the incident light flux and the transmittance or reflectance of the image or image element, as a decimal not as a percentage.

(1.2)

1.2 VISUAL COLORIMETRY

1.2.1 A Method to Uniquely Map the Colour of Lights and Objects

Visual colorimetry is the most direct and accurate method to objectively quantify colour. It is also the most difficult. Visual colorimetry requires the colourist to mix lights of different colours until a match between the test colour and the mixture is obtained. There are several common components found in all colorimeters that include a source of light, a source of primary or mixing colours that together produce the colour stimulus function and the viewing optics that transfer the colour stimulus function to the observer. Figure 1.1 shows a block diagram of a visual colorimeter. The principle of visual colorimetry is very simple and very familiar to most people today. It is the same principle that is used to set the mood on the stage in a theatre, and to generate colour signals on television screens, computer displays and slide or motion picture film projectors. This approach to colorimetry is also appropriate to teach primary school children to mix tempera paints, dye our clothing or hair and is familiar to artists and technologists who mix colorants to match the colours of natural objects in a paint medium. The physics of how the primaries interact to form the final, coloured stimulus is different but the process and result is completely analogous.

Figure 1.1 Typical visual colorimeter with three primary filters.

The earliest commercial colorimeters were thus visual colorimeters. Donaldson [2] produced several different models of visual colorimeter. The light source was a stable, ribbon filament incandescent lamp which illuminated both halves of a bipartite visual field. A transparent, coloured specimen could be placed in one half of the visual field and located between the coloured primaries opened full and the viewing optics. In the other half, the mixture of the coloured primaries is adjusted by opening or closing shutters over the primaries allowing more or less of the light to pass through the primary filters. Opening the shutters together made the image brighter while closing them together made the image dimmer. The shutters on the test field could also be adjusted so that the light seen through the viewing optics, usually a telescope imaging the two light mixing chambers side by side into the observer’s eye, could be matched for both colour and brightness. When the observer was satisfied with the quality of the match the positions of each of the shutters would be recorded, fixing exactly the state of that colour mixture on that colorimeter. Essentially, this quantified the equality of the two colour stimulus functions. Those numbers could be communicated to anyone else in the world with the same model Donaldson colorimeter and the match could be visualised by setting the shutters to the same positions. But if the second laboratory did not have the same model of colorimeter, the shutter settings would be of little value. Visual colorimetry as practiced in this way was thus a rather limited tool, good for evaluation of repeated specimens of the same colour but not for communication of colours to engineers in outside laboratories. There remains a great deal of similarity between these early colorimeters and our modern high dynamic range visual display units that incorporate solid state light sources and electro-chemically controlled shutters.

1.2.2 Development of the CIE Method of Visual Colorimetry

In the late 1920s two researchers in the London area began studies to better quantify the methods of visual colorimetry. One was John Guild at the National Physical Laboratory, and the other was a graduate student at the Imperial College, W. David Wright. Being interested in both the basic science of colorimetry and the needs of commerce, Guild [3] built a colorimeter that was similar to those of Donaldson with red, green and blue filters as primaries but with finer resolution and the best optical characterisation available to a national standardising laboratory. Wright [4, 5], being a physics graduate student, was more interested in making the most thorough determination of these colour mixture functions and built his visual colorimeter using prism monochromators for the primaries and seems to have first used the term “trichromator” for such an instrument. Guild was able to convince seven people to go through the difficult task of making colour matches to 30 or so narrow bands of wavelengths. The result was a spectral curve of colour mixtures representing the amounts of each of Guild’s red, green or blue primaries that would be mixed to match a given wavelength of light. Figure 1.2 shows a typical set of colour mixture curves. Wright was a bit more successful at recruiting observers and had 10 people make matches on his trichromator. Wright’s colorimeter then produced curves that described the amounts of his primary wavelengths (460nm, 530nm, 650nm) required to match the spectral colours. Even before Guild had published Wright took the two sets of data and compared them. They found that the colour mixtures curves in terms of primaries, RGB, of both experiments were very similar and so, using linear algebra, they transformed the two sets of results to a common set of monochromatic primaries and then averaged the results. Finally, they normalised the results using a white point determined by NPL and made the middle wavelength or green mixture function identical to the CIE 1929 standard of photometry. Details of how they were able to achieve this can be found in the review by Fairman [6