Tran Quoc Khanh is University Professor and Head of the Laboratory of Lighting Technology at the TU Darmstadt in Darmstadt, Germany. He obtained his PhD degree in Lighting Engineering from the TU Ilmenau, Germany. He obtained his Degree of Lecture Qualification (Habilitation) from the same University for his thesis in Colorimetry and Color Image Processing. He gathered industrial experience as a project manager at ARRI CineTechnik in Munchen (Germany). Tran Quoc Khanh authored and co-authored numerous scientific publications and invented several patents in different domains of lighting technology.Peter Bodrogi is senior research fellow at the Laboratory of Lighting Technology of the TU Darmstadt in Darmstadt, Germany. He obtained his PhD degree in Information Technology from the University of Pannonia. He obtained his Degree of Lecture Qualification (Habilitation) from the TU Darmstadt in 2010 for his thesis on the optimization of modern visual technologies. He co-authored numerous scientific publications and invented patents in the domains of self-luminous display technology and lighting technology.Quang Trinh Vinh is senior research fellow at the Laboratory of Lighting Technology of the TU Darmstadt in Darmstadt, Germany. He obtained his ME Degree in regulation technology. He obtained his Dr.-Ing. degree from the TU Darmstadt in 2013. His research subject concerns the complex mathematical modeling of high-power (phosphor-converted) LEDs, including their electric, thermal and optical behavior, and their light quality and color quality. He co-authored several scientific publications and invented patents in LED lighting technology.

• Preface xi1 Introduction 1References 92 Color Appearance and Color Quality: Phenomena and Metrics 112.1 Color Vision 112.2 Colorimetry 162.2.1 Color-Matching Functions and Tristimulus Values 172.2.2 Chromaticity Diagram 192.2.3 Interobserver Variability of Color Vision 202.2.4 Important Concepts Related to the Chromaticity Diagram 212.2.5 MacAdam Ellipses and the u′ − v′ Chromaticity Diagram 242.3 Color Appearance, Color Cognition 262.3.1 Perceived Color Attributes 262.3.2 Viewing Conditions, Chromatic Adaptation, and Other Phenomena 282.3.3 Perceived Color Differences 292.3.4 Cognitive Color, Memory Color, and Semantic Interpretations 292.4 The Subjective Impression of Color Quality and Its Different Aspects 312.5 Modeling of Color Appearance and Perceived Color Differences 352.5.1 CIELAB Color Space 362.5.2 The CIECAM02 Color Appearance Model 372.5.3 Brightness Models 412.5.3.1 The CIE Brightness Model 432.5.3.2 The Ware and Cowan Conversion Factor formula (WCCF) 442.5.3.3 The Berman et al. Model 442.5.3.4 Fotios and Levermore’s Brightness Model 452.5.3.5 Fairchild and Pirrotta’s L∗∗ Model of Chromatic Lightness 452.5.4 Modeling of Color Difference Perception in Color Spaces 452.5.4.1 CIELAB Color Difference 452.5.4.2 CAM02-UCS Uniform Color Space and Color Difference 462.6 Modeling of Color Quality 482.6.1 Color Fidelity Indices 492.6.1.1 The CIE Color-Rendering Index 492.6.1.2 The Color Fidelity Index of the CQS Method 522.6.1.3 The Color Fidelity Index CRI2012 (nCRI) 532.6.1.4 The Color Fidelity Index Rf of the IES Method (2015) 562.6.1.5 RCRI 572.6.1.6 Summary of the Deficiencies of Color Fidelity Metrics 572.6.2 Color Preference Indices 572.6.2.1 Judd’s Flattery Index 572.6.2.2 Gamut Area Index (GAI) in Combination with CIE Ra 582.6.2.3 Thornton’s Color Preference Index (CPI) 582.6.2.4 Memory Color Rendition Index Rm or MCRI 582.6.2.5 The Color Preference Indices of the CQS Method (Qa, Qp) 602.6.3 Color Gamut Indices 612.6.3.1 The Color Gamut Index of the CQS Method (Qg ) 622.6.3.2 The Feeling of Contrast Index (FCI) 622.6.3.3 Xu’s Color-Rendering Capacity (CRC) 622.6.3.4 Gamut Area Index (GAI) 622.6.3.5 Fotios’ Cone Surface Area (CSA) Index 622.6.3.6 The Color Gamut Index Rg of the IES Method (2015) 622.6.3.7 Deficiencies of Color Gamut Metrics 632.6.4 Color Discrimination Indices 632.7 Summary 64References 653 The White Point of the Light Source 713.1 The Location of Unique White in the Chromaticity Diagram 743.2 Modeling Unique White in Terms of L − M and L + M − S Signals 773.3 Interobserver Variability of White Tone Perception 783.4 White Tone Preference 833.5 The White Tone’s Perceived Brightness 853.6 Summary and Outlook 87References 894 Object Colors – Spectral Reflectance, Grouping of Colored Objects, and Color Gamut Aspects 914.1 Introduction: Aims and Research Questions 914.2 Spectral Reflectance of Flowers 944.3 Spectral Reflectance of Skin Tones 964.4 Spectral Reflectance of Art Paintings 974.5 The Leeds Database of Object Colors 984.6 State-of-the-Art Sets of Test Color Samples and Their Ability to Evaluate the Color Quality of Light Sources 1004.7 Principles of Color Grouping with Two Examples for Applications 1144.7.1 Method 1 – Application of the Theory of Signal Processing in the Classical Approach 1204.7.2 Method 2 – the Application of a Visual Color Model in the Classical Approach 1214.7.3 Method 3 – the Application of Visual Color Models in the Modern Approach 1214.7.4 First Example of Color Grouping with a Specific Lighting System Applying Two Methods 1224.7.5 Second Example of Applying Method 3 by Using Modern Color Metrics 1234.8 Summary and Lessons Learnt for Lighting Practice 125References 1265 State of the Art of Color Quality Research and Light Source Technology: A Literature Review 1295.1 General Aspects 1295.2 Review of the State of the Art of Light Source Technology Regarding Color Quality 1325.3 Review of the State of the Art of Colored Object Aspects 1415.4 Viewing Conditions in Color Research 1425.5 Review of the State-of-the-Art Color Spaces and Color Difference Formulae 1455.6 General Review of the State of the Art of Color Quality Metrics 1545.7 Review of the Visual Experiments 1605.8 Review of the State-of-the-Art Analyses about the Correlation of Color Quality Metrics of Light Sources 1615.9 Review of the State-of-the-Art Analysis of the Prediction Potential and Correctness of Color Quality Metrics Verified by Visual Experiments 166References 1716 Correlations of Color Quality Metrics and a Two-Metrics Analysis 1756.1 Introduction: Research Questions 1756.2 Correlation of Color Quality Metrics 1776.2.1 Correlation of Color Metrics for the Warm White Light Sources 1786.2.2 Correlation of Color Quality Metrics for Cold White Light Sources 1846.3 Color Preference and Naturalness Metrics as a Function of Two-Metrics Combinations 1896.3.1 Color Preference with the Constrained Linear Formula (Eq. (6.2)) 1926.3.2 Color Preference with the Unconstrained Linear Formula (Eq. (6.3)) 1946.3.3 Color Preference with the Quadratic Saturation and Linear Fidelity Formula (Eq. (6.4)) 1956.4 Conclusions and Lessons Learnt for Lighting Practice 196References 1987 Visual Color Quality Experiments at the Technische Universität Darmstadt 2017.1 Motivation and Aim of the Visual Color Quality Experiments 2017.2 Experiment on Chromatic and Achromatic Visual Clarity 2047.2.1 Experimental Method 2057.2.2 Analysis and Modeling of the Visual Clarity Dataset 2087.3 Brightness Matching of Strongly Metameric White Light Sources 2127.3.1 Experimental Method 2137.3.2 Results of the Brightness-Matching Experiment 2167.4 Correlated Color Temperature Preference for White Objects 2187.4.1 Experimental Method 2187.4.2 Results and Discussion 2237.4.3 Modeling in Terms of LMS Cone Signals and Their Combinations 2237.4.4 Summary 2257.5 Color Temperature Preference of Illumination with Red, Blue, and Colorful Object Combinations 2257.5.1 Experimental Method 2267.5.2 Results and Discussion 2307.5.3 Modeling in Terms of LMS Cone Signals and Their Combinations 2307.5.4 Summary 2337.6 Experiments on Color Preference, Naturalness, and Vividness in a Real Room 2347.6.1 Experimental Method 2347.6.2 Relationship among the Visual Interval Scale Variables Color Naturalness, Vividness, and Preference 2387.6.3 Correlation of the Visual Assessments with Color Quality Indices 2397.6.4 Combinations of Color Quality Indices and Their Semantic Interpretation for the Set of Five Light Sources 2407.6.4.1 Prediction of Vividness 2407.6.4.2 Prediction of Naturalness 2417.6.4.3 Prediction of Color Preference 2417.6.5 Cause Analysis in Terms of Chroma Shifts and Color Gamut Differences 2437.6.6 Lessons Learnt from Section 7.6 2467.7 Experiments on Color Preference, Naturalness, and Vividness in a One-Chamber Viewing Booth with Makeup Products 2467.7.1 Experimental Method 2477.7.2 Color Preference, Naturalness, and Vividness and Their Modeling 2517.8 Food and Makeup Products: Comparison of Color Preference, Naturalness, and Vividness Results 2567.8.1 Method of the Experiment with Food Products 2577.8.2 Color Preference, Naturalness, and Vividness Assessments: Merging the Results of the Two Experiments (for Multicolored Food and Reddish and Skin-Tone Type Makeup Products) 2587.8.3 Analysis and Modeling of the Merged Results of the Two Experiments 2617.8.4 Effect of Object Oversaturation on Color Discrimination: a Computational Approach 2657.9 Semantic Interpretation and Criterion Values of Color Quality Metrics 2687.9.1 Semantic Interpretation and Criterion Values of Color Differences 2687.9.1.1 Semantic Interpretation of Color Fidelity Indices 2707.9.1.2 Color Discrimination 2727.9.1.3 Criterion Values for White Tone Chromaticity for the Binning of White LEDs 2737.9.2 Semantic Interpretation and Criterion Values for the Visual Attributes of Color Appearance 2767.10 Lessons Learnt for Lighting Practice 277References 2808 Optimization of LED Light Engines for High Color Quality 2838.1 Overview of the Development Process of LED Luminaires 2838.2 Thermal and Electric Behavior of Typical LEDs 2958.2.1 Temperature and Current Dependence of Warm White LED Spectra 2958.2.1.1 Temperature Dependence of Warm White pc-LED Spectra 2958.2.1.2 Current Dependence of Warm White pc-LED Spectra 2978.2.1.3 Current Dependence of the Color Difference of Warm White pc-LEDs 2978.2.2 Temperature and Current Dependence of Color LED Spectra 2998.3 Colorimetric Behavior of LEDs under PWM and CCD Dimming 3008.4 Spectral Models of Color LEDs and White pc-LEDs 3028.5 General Aspects of Color Quality Optimization 3058.6 Appropriate Wavelengths of the LEDs to Apply and a System of Color Quality Optimization for LED Luminaires 3118.6.1 Appropriate Wavelengths of the LEDs to Apply 3118.6.2 Systematization for the Color Quality Optimization of LED Luminaires 3158.6.2.1 Conventional Structures of LED Luminaries in Real Applications 3158.6.2.2 Schematic Description of the Color Quality Optimization of LED Luminaries 3158.6.2.3 Algorithmic Description of Color Quality Optimization in the Development of LED Luminaries 3188.6.2.4 Optimization Solutions 3198.7 Optimization of LED Light Engines on Color Fidelity and Chroma Enhancement in the Case of Skin Tones 3208.8 Optimization of LED Light Engines on Color Quality with the Workflow 3238.8.1 Optimization of the LED Light Engine on Color Quality Using the RGB-W-LED Configuration 3238.8.2 Optimization of the LED Light Engine on Color Quality with the R1 - R2 -G-B1 - B2 - W - LED - configuration 3278.9 Conclusions: Lessons Learnt for Lighting Practice 333References 3349 Human Centric Lighting and Color Quality 3359.1 Principles of Color Quality Optimization for Human Centric Lighting 3359.2 The Circadian Stimulus in the Rea et al. Model 3389.3 Spectral Design for HCL: Co-optimizing Circadian Aspects and Color Quality 3449.4 Spectral Design for HCL: Change of Spectral Transmittance of the Eye Lens with Age 3489.5 Conclusions 354References 35510 Conclusions: Lessons Learnt for Lighting Engineering 357Index 365