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E-raamat: Color Quality of Semiconductor and Conventional Light Sources

(TU Darmstadt, Darmstadt, Germany), (TU Darmstadt, Darmstadt, Germany), (TU Darmstadt, Darmstadt, Germany)
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  • Ilmumisaeg: 28-Dec-2016
  • Kirjastus: Blackwell Verlag GmbH
  • Keel: eng
  • ISBN-13: 9783527803460
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Color Quality of Semiconductor and Conventional Light SourcesSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 28-Dec-2016
  • Kirjastus: Blackwell Verlag GmbH
  • Keel: eng
  • ISBN-13: 9783527803460
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Meeting the need for a reliable publication on the topic and reflecting recent breakthroughs in the field, this is a comprehensive overview of color quality of solid-state light sources (LED-OLED and laser) and conventional lamps, providing academic researchers with an in-depth review of the current state while supporting lighting professionals in understanding, evaluating and optimizing illumination in their daily work.
Preface xi
1 Introduction 1(10)
References
9(2)
2 Color Appearance and Color Quality: Phenomena and Metrics 11(60)
2.1 Color Vision
11(5)
2.2 Colorimetry
16(10)
2.2.1 Color-Matching Functions and Tristimulus Values
17(2)
2.2.2 Chromaticity Diagram
19(1)
2.2.3 Interobserver Variability of Color Vision
20(1)
2.2.4 Important Concepts Related to the Chromaticity Diagram
21(3)
2.2.5 MacAdam Ellipses and the u' - v' Chromaticity Diagram
24(2)
2.3 Color Appearance, Color Cognition
26(5)
2.3.1 Perceived Color Attributes
26(2)
2.3.2 Viewing Conditions, Chromatic Adaptation, and Other Phenomena
28(1)
2.3.3 Perceived Color Differences
29(1)
2.3.4 Cognitive Color, Memory Color, and Semantic Interpretations
29(2)
2.4 The Subjective Impression of Color Quality and Its Different Aspects
31(4)
2.5 Modeling of Color Appearance and Perceived Color Differences
35(13)
2.5.1 CIELAB Color Space
36(1)
2.5.2 The CIECAMO2 Color Appearance Model
37(4)
2.5.3 Brightness Models
41(4)
2.5.3.1 The CIE Brightness Model
43(1)
2.5.3.2 The Ware and Cowan Conversion Factor Formula (WCCF)
44(1)
2.5.3.3 The Berman et al. Model
44(1)
2.5.3.4 Fotios and Levermore's Brightness Model
45(1)
2.5.3.5 Fairchild and Pirrotta's L** Model of Chromatic Lightness
45(1)
2.5.4 Modeling of Color Difference Perception in Color Spaces
45(3)
2.5.4.1 CIELAB Color Difference
45(1)
2.5.4.2 CAM02-UCS Uniform Color Space and Color Difference
46(2)
2.6 Modeling of Color Quality
48(16)
2.6.1 Color Fidelity Indices
49(8)
2.6.1.1 The CIE Color-Rendering Index
49(3)
2.6.1.2 The Color Fidelity Index of the CQS Method
52(1)
2.6.1.3 The Color Fidelity Index CRI2012 (nCRI)
53(3)
2.6.1.4 The Color Fidelity Index Rf of the IES Method (2015)
56(1)
2.6.1.5 RCRI
57(1)
2.6.1.6 Summary of the Deficiencies of Color Fidelity Metrics
57(1)
2.6.2 Color Preference Indices
57(4)
2.6.2.1 Judd's Flattery Index
57(1)
2.6.2.2 Gamut Area Index (GAI) in Combination with CIE Ra
58(1)
2.6.2.3 Thornton's Color Preference Index (CPI)
58(1)
2.6.2.4 Memory Color Rendition Index Rn, or MCRI
58(2)
2.6.2.5 The Color Preference Indices of the CQS Method (Qa, Qp)
60(1)
2.6.3 Color Gamut Indices
61(2)
2.6.3.1 The Color Gamut Index of the CQS Method (Qg)
62(1)
2.6.3.2 The Feeling of Contrast Index (FCI)
62(1)
2.6.3.3 Xu's Color-Rendering Capacity (CRC)
62(1)
2.6.3.4 Gamut Area Index (GAI)
62(1)
2.6.3.5 Fotios' Cone Surface Area (CSA) Index
62(1)
2.6.3.6 The Color Gamut Index Rg of the IES Method (2015)
62(1)
2.6.3.7 Deficiencies of Color Gamut Metrics
63(1)
2.6.4 Color Discrimination Indices
63(1)
2.7 Summary
64(1)
References
65(6)
3 The White Point of the Light Source 71(20)
3.1 The Location of Unique White in the Chromaticity Diagram
74(3)
3.2 Modeling Unique White in Terms of L - M and L + M - S Signals
77(1)
3.3 Interobserver Variability of White Tone Perception
78(5)
3.4 White Tone Preference
83(2)
3.5 The White Tone's Perceived Brightness
85(2)
3.6 Summary and Outlook
87(2)
References
89(2)
4 Object Colors - Spectral Reflectance, Grouping of Colored Objects, and Color Gamut Aspects 91(38)
4.1 Introduction: Aims and Research Questions
91(3)
4.2 Spectral Reflectance of Flowers
94(2)
4.3 Spectral Reflectance of Skin Tones
96(1)
4.4 Spectral Reflectance of Art Paintings
97(1)
4.5 The Leeds Database of Object Colors
98(2)
4.6 State-of-the-Art Sets of Test Color Samples and Their Ability to Evaluate the Color Quality of Light Sources
100(14)
4.7 Principles of Color Grouping with Two Examples for Applications
114(11)
4.7.1 Method 1 - Application of the Theory of Signal Processing in the Classical Approach
120(1)
4.7.2 Method 2 - the Application of a Visual Color Model in the Classical Approach
121(1)
4.7.3 Method 3 - the Application of Visual Color Models in the Modern Approach
121(1)
4.7.4 First Example of Color Grouping with a Specific Lighting System Applying Two Methods
122(1)
4.7.5 Second Example of Applying Method 3 by Using Modern Color Metrics
123(2)
4.8 Summary and Lessons Learnt for Lighting Practice
125(1)
References
126(3)
5 State of the Art of Color Quality Research and Light Source Technology: A Literature Review 129(46)
5.1 General Aspects
129(3)
5.2 Review of the State of the Art of Light Source Technology Regarding Color Quality
132(9)
5.3 Review of the State of the Art of Colored Object Aspects
141(1)
5.4 Viewing Conditions in Color Research
142(3)
5.5 Review of the State-of-the-Art Color Spaces and Color Difference Formulae
145(9)
5.6 General Review of the State of the Art of Color Quality Metrics
154(6)
5.7 Review of the Visual Experiments
160(1)
5.8 Review of the State-of-the-Art Analyses about the Correlation of Color Quality Metrics of Light Sources
161(5)
5.9 Review of the State-of-the-Art Analysis of the Prediction Potential and Correctness of Color Quality Metrics Verified by Visual Experiments
166(5)
References
171(4)
6 Correlations of Color Quality Metrics and a Two-Metrics Analysis 175(26)
6.1 Introduction: Research Questions
175(2)
6.2 Correlation of Color Quality Metrics
177(12)
6.2.1 Correlation of Color Metrics for the Warm White Light Sources
178(6)
6.2.2 Correlation of Color Quality Metrics for Cold White Light Sources
184(5)
6.3 Color Preference and Naturalness Metrics as a Function of Two-Metrics Combinations
189(7)
6.3.1 Color Preference with the Constrained Linear Formula (Eq. (6.2))
192(2)
6.3.2 Color Preference with the Unconstrained Linear Formula (Eq. (6.3))
194(1)
6.3.3 Color Preference with the Quadratic Saturation and Linear Fidelity Formula (Eq. (6.4))
195(1)
6.4 Conclusions and Lessons Learnt for Lighting Practice
196(2)
References
198(3)
7 Visual Color Quality Experiments at the Technische Universitat Darmstadt 201(82)
7.1 Motivation and Aim of the Visual Color Quality Experiments
201(3)
7.2 Experiment on Chromatic and Achromatic Visual Clarity
204(8)
7.2.1 Experimental Method
205(3)
7.2.2 Analysis and Modeling of the Visual Clarity Dataset
208(4)
7.3 Brightness Matching of Strongly Metameric White Light Sources
212(6)
7.3.1 Experimental Method
213(3)
7.3.2 Results of the Brightness-Matching Experiment
216(2)
7.4 Correlated Color Temperature Preference for White Objects
218(7)
7.4.1 Experimental Method
218(5)
7.4.2 Results and Discussion
223(1)
7.4.3 Modeling in Terms of LMS Cone Signals and Their Combinations
223(2)
7.4.4 Summary
225(1)
7.5 Color Temperature Preference of Illumination with Red, Blue, and Colorful Object Combinations
225(9)
7.5.1 Experimental Method
226(4)
7.5.2 Results and Discussion
230(1)
7.5.3 Modeling in Terms of LMS Cone Signals and Their Combinations
230(3)
7.5.4 Summary
233(1)
7.6 Experiments on Color Preference, Naturalness, and Vividness in a Real Room
234(12)
7.6.1 Experimental Method
234(4)
7.6.2 Relationship among the Visual Interval Scale Variables Color Naturalness, Vividness, and Preference
238(1)
7.6.3 Correlation of the Visual Assessments with Color Quality Indices
239(1)
7.6.4 Combinations of Color Quality Indices and Their Semantic Interpretation for the Set of Five Light Sources
240(3)
7.6.4.1 Prediction of Vividness
240(1)
7.6.4.2 Prediction of Naturalness
241(1)
7.6.4.3 Prediction of Color Preference
241(2)
7.6.5 Cause Analysis in Terms of Chroma Shifts and Color Gamut Differences
243(3)
7.6.6 Lessons Learnt from Section 7.6
246(1)
7.7 Experiments on Color Preference, Naturalness, and Vividness in a One-Chamber Viewing Booth with Makeup Products
246(10)
7.7.1 Experimental Method
247(4)
7.7.2 Color Preference, Naturalness, and Vividness and Their Modeling
251(5)
7.8 Food and Makeup Products: Comparison of Color Preference, Naturalness, and Vividness Results
256(12)
7.8.1 Method of the Experiment with Food Products
257(1)
7.8.2 Color Preference, Naturalness, and Vividness Assessments: Merging the Results of the Two Experiments (for Multicolored Food and Reddish and Skin-Tone Type Makeup Products)
258(3)
7.8.3 Analysis and Modeling of the Merged Results of the Two Experiments
261(4)
7.8.4 Effect of Object Oversaturation on Color Discrimination: a Computational Approach
265(3)
7.9 Semantic Interpretation and Criterion Values of Color Quality Metrics
268(9)
7.9.1 Semantic Interpretation and Criterion Values of Color Differences
268(8)
7.9.1.1 Semantic Interpretation of Color Fidelity Indices
270(2)
7.9.1.2 Color Discrimination
272(1)
7.9.1.3 Criterion Values for White Tone Chromaticity for the Binning of White LEDs
273(3)
7.9.2 Semantic Interpretation and Criterion Values for the Visual Attributes of Color Appearance
276(1)
7.10 Lessons Learnt for Lighting Practice
277(3)
References
280(3)
8 Optimization of LED Light Engines for High Color Quality 283(52)
8.1 Overview of the Development Process of LED Luminaires
283(12)
8.2 Thermal and Electric Behavior of Typical LEDs
295(5)
8.2.1 Temperature and Current Dependence of Warm White LED Spectra
295(4)
8.2.1.1 Temperature Dependence of Warm White pc-LED Spectra
295(2)
8.2.1.2 Current Dependence of Warm White pc-LED Spectra
297(1)
8.2.1.3 Current Dependence of the Color Difference of Warm White pc-LEDs
297(2)
8.2.2 Temperature and Current Dependence of Color LED Spectra
299(1)
8.3 Colorimetric Behavior of LEDs under PWM and CCD Dimming
300(2)
8.4 Spectral Models of Color LEDs and White pc-LEDs
302(3)
8.5 General Aspects of Color Quality Optimization
305(6)
8.6 Appropriate Wavelengths of the LEDs to Apply and a System of Color Quality Optimization for LED Luminaires
311(9)
8.6.1 Appropriate Wavelengths of the LEDs to Apply
311(4)
8.6.2 Systematization for the Color Quality Optimization of LED Luminaires
315(8)
8.6.2.1 Conventional Structures of LED Luminaries in Real Applications
315(1)
8.6.2.2 Schematic Description of the Color Quality Optimization of LED Luminaries
315(3)
8.6.2.3 Algorithmic Description of Color Quality Optimization in the Development of LED Luminaries
318(1)
8.6.2.4 Optimization Solutions
319(1)
8.7 Optimization of LED Light Engines on Color Fidelity and Chroma Enhancement in the Case of Skin Tones
320(3)
8.8 Optimization of LED Light Engines on Color Quality with the Workflow
323(10)
8.8.1 Optimization of the LED Light Engine on Color Quality Using the RGB-W-LED Configuration
323(4)
8.8.2 Optimization of the LED Light Engine on Color Quality with the R1 - R2 - G - B1 - B2 - W - LED - configuration
327(6)
8.9 Conclusions: Lessons Learnt for Lighting Practice
333(1)
References
334(1)
9 Human Centric Lighting and Color Quality 335(22)
9.1 Principles of Color Quality Optimization for Human Centric Lighting
335(3)
9.2 The Circadian Stimulus in the Rea et al. Model
338(6)
9.3 Spectral Design for HCL: Co-optimizing Circadian Aspects and Color Quality
344(4)
9.4 Spectral Design for HCL: Change of Spectral Transmittance of the Eye Lens with Age
348(6)
9.5 Conclusions
354(1)
References
355(2)
10 Conclusions: Lessons Learnt for Lighting Engineering 357(8)
Index 365
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.
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