LED Lighting E-Book

• Herausgeber: Wiley-VCH
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 825

Veröffentlichungsjahr: 2014

Beschreibung

Promoting the design, application and evaluation of visually and electrically effective LED light sources and luminaires for general indoor lighting as well as outdoor and vehicle lighting, this book combines the knowledge of LED lighting technology with human perceptual aspects for lighting scientists and engineers.
After an introduction to the human visual system and current radiometry, photometry and color science, the basics of LED chip and phosphor technology are described followed by specific issues of LED radiometry and the optical, thermal and electric modeling of LEDs. This is supplemented by the relevant practical issues of pulsed LEDs, remote phosphor LEDs and the aging of LED light sources. Relevant human visual aspects closely related to LED technology are described in detail for the photopic and the mesopic range of vision, including color rendering, binning, whiteness, Circadian issues, as well as flicker perception, brightness, visual performance, conspicuity and disability glare. The topic of LED luminaires is discussed in a separate chapter, including retrofit LED lamps, LED-based road and street luminaires and LED luminaires for museum and school lighting. Specific sections are devoted to the modularity of LED luminaires, their aging and the planning and evaluation methods of new LED installations. The whole is rounded off by a summary and a look towards future developments.

Leseprobe

Table of Contents

Cover

Related Titles

Title Page

Copyright

Foreword

Table of the Coauthors

Preface

Chapter 1: Introduction

Reference

Chapter 2: The Human Visual System and Its Modeling for Lighting Engineering

2.1 Visual System Basics

2.2 Radiometry and Photometry

2.3 Colorimetry and Color Science

2.4 LED Specific Spectral and Colorimetric Quantities

2.5 Circadian Effect of Electromagnetic Radiation

References

Chapter 3: LED Components – Principles of Radiation Generation and Packaging

3.1 Introduction to LED Technology

3.2 Basic Knowledge on Color Semiconductor LEDs

3.3 Color Semiconductor LEDs

3.4 Phosphor Systems and White Phosphor-Converted LEDs

3.5 Green and Red Phosphor-Converted LEDs

References

Chapter 4: Measurement and Modeling of the LED Light Source

4.1 LED Radiometry, Photometry, and Colorimetry

4.2 Thermal and Electric Behavior of Color Semiconductor LEDs

4.3 Thermal and Electric Behavior of White Phosphor-Converted LEDs

4.4 Consequences for LED Selection Under Real Operation Conditions

4.8 Measurement Methods to Determine the Thermal Characteristics of LED Devices

4.9 Thermal and Optical Behavior of Blue LEDs, Silicon Systems, and Phosphor Systems

4.11 Lifetime Extrapolation

4.12 LED Dimming Behavior

References

Chapter 5: Photopic Perceptual Aspects of LED Lighting

5.1 Introduction to the Different Aspects of Light and Color Quality

5.2 Color Rendering Indices: CRI, CRI2012

5.3 Semantic Interpretation of Color Differences and Color Rendering Indices

5.4 Object Specific Color Rendering Indices of Current White LED Light Sources

5.5 Color Preference Assessment: Comparisons Between CRI, CRI2012, and CQS

5.6 Brightness, Chromatic Lightness, and Color Rendering of White LEDs

5.7 White Point Characteristics of LED Lighting

5.8 Chromaticity Binning of White LEDs

5.9 Visual Experiments (Real Field Tests) on the Color Quality of White LEDs

5.10 Circadian Stimulus, Color Temperature, and Color Rendering of White LEDs

5.11 Flicker and Stroboscopic Perception of White LEDs under Photopic Conditions

References

Chapter 6: Mesopic Perceptual Aspects of LED Lighting

6.1 Foundations and Models of Mesopic Brightness and Visual Performance

6.2 Mesopic Brightness under LED Based and Conventional Automotive Front Lighting Light Sources

6.3 Mesopic Visual Performance under LED Lighting Conditions

6.4 Visual Acuity in the Mesopic Range with Conventional Light Sources and White LEDs

6.5 Detection and Conspicuity of Road Markings in the Mesopic Range

6.6 Glare under Mesopic Conditions

6.7 Bead String Artifact of PWM Controlled LED Rear Lights at Different Frequencies

6.8 Summarizing Remarks to Chapter 6

References

Chapter 7: Optimization and Characterization of LED Luminaires for Indoor Lighting

7.1 Indoor Lighting – Application Fields and Requirements

7.2 Basic Aspects of LED-Indoor Luminaire Design

7.3 Selection Criteria for LED Components and Units

7.5 Principles of LED Radiation Generation with Higher Color Quality and One Correlated Color Temperature

7.6 Optimization and Stabilization of Hybrid LED Luminaires with High Color Rendering Index and Variable Correlated Color Temperature

References

Chapter 8: Optimization and Characterization of LED Luminaires for Outdoor Lighting

8.1 Introduction

8.2 Construction Principles of LED Luminaire Units

8.3 Systematic Approach of LED Luminaire Design for Street Lighting

8.4 Degradation Behavior of LED Street Luminaires

8.5 Maintenance Factor for LED Luminaires

8.6 Planning and Realization Principles for New LED Installations

References

Chapter 9: Summary

Index

EULA

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Guide

Cover

Table of Contents

Foreword

Preface

Chapter 1: Introduction

List of Illustrations

Figure 1.1

Figure 1.2

Figure 1.3

Figure 2.1

Figure 2.2

Figure 2.4

Figure 2.3

Figure 2.5

Figure 2.6

Figure 2.7

Figure 2.8

Figure 2.9

Figure 2.10

Figure 2.11

Figure 2.12

Figure 2.13

Figure 2.14

Figure 2.15

Figure 2.16

Figure 2.17

Figure 2.18

Figure 2.19

Figure 2.20

Figure 2.21

Figure 2.22

Figure 2.23

Figure 2.24

Figure 2.25

Figure 2.26

Figure 2.27

Figure 2.28

Figure 2.29

Figure 2.30

Figure 2.31

Figure 2.32

Figure 2.33

Figure 3.1

Figure 3.2

Figure 3.3

Figure 3.4

Figure 3.5

Figure 3.6

Figure 3.7

Figure 3.8

Figure 3.9

Figure 3.10

Figure 3.11

Figure 3.12

Figure 3.13

Figure 3.14

Figure 3.15

Figure 3.16

Figure 3.17

Figure 3.18

Figure 3.19

Figure 3.20

Figure 3.21

Figure 3.22

Figure 3.23

Figure 3.24

Figure 3.25

Figure 3.26

Figure 3.27

Figure 3.29

Figure 3.30

Figure 3.31

Figure 3.32

Figure 3.33

Figure 3.34

Figure 3.35

Figure 3.36

Figure 3.37

Figure 3.38

Figure 3.39

Figure 3.40

Figure 3.41

Figure 3.42

Figure 3.43

Figure 3.44

Figure 3.45

Figure 3.46

Figure 3.47

Figure 3.48

Figure 3.49

Figure 3.50

Figure 3.51

Figure 3.52

Figure 3.53

Figure 3.54

Figure 3.55

Figure 3.60

Figure 3.61

Figure 3.62

Figure 3.63

Figure 3.64

Figure 3.65

Figure 3.66

Figure 3.67

Figure 3.68

Figure 3.69

Figure 3.70

Figure 4.1

Figure 4.2

Figure 4.3

Figure 4.4

Figure 4.5

Figure 4.6

Figure 4.7

Figure 4.48

Figure 4.8

Figure 4.9

Figure 4.10

Figure 4.11

Figure 4.12

Figure 4.13

Figure 4.14

Figure 4.15

Figure 4.16

Figure 4.17

Figure 4.22

Figure 4.18

Figure 4.19

Figure 4.20

Figure 4.21

Figure 4.23

Figure 4.24

Figure 4.25

Figure 4.26

Figure 4.27

Figure 4.28

Figure 4.29

Figure 4.30

Figure 4.31

Figure 4.32

Figure 4.33

Figure 4.34

Figure 4.35

Figure 4.36

Figure 4.37

Figure 4.38

Figure 4.39

Figure 4.40

Figure 4.41

Figure 4.42

Figure 4.43

Figure 4.44

Figure 4.45

Figure 4.46

Figure 4.47

Figure 4.49

Figure 4.50

Figure 4.51

Figure 4.52

Figure 4.53

Figure 4.54

Figure 4.55

Figure 4.56

Figure 4.57

Figure 4.58

Figure 4.59

Figure 4.60

Figure 4.61

Figure 4.62

Figure 4.63

Figure 4.64

Figure 4.65

Figure 4.66

Figure 4.67

Figure 4.68

Figure 4.69

Figure 4.70

Figure 4.71

Figure 4.72

Figure 4.73

Figure 4.74

Figure 4.75

Figure 4.76

Figure 4.77

Figure 4.78

Figure 4.79

Figure 4.80

Figure 4.81

Figure 4.82

Figure 4.83

Figure 4.84

Figure 4.85

Figure 4.86

Figure 4.87

Figure 4.88

Figure 4.89

Figure 4.90

Figure 5.2

Figure 5.1

Figure 5.3

Figure 5.4

Figure 5.5

Figure 5.6

Figure 5.61

Figure 5.7

Figure 5.9

Figure 5.8

Figure 5.22

Figure 5.24

Figure 5.10

Figure 5.13

Figure 5.11

Figure 5.12

Figure 5.14

Figure 5.15

Figure 5.16

Figure 5.17

Figure 5.18

Figure 5.19

Figure 5.20

Figure 5.21

Figure 5.23

Figure 5.25

Figure 5.26

Figure 5.27

Figure 5.28

Figure 5.29

Figure 5.30

Figure 5.31

Figure 5.32

Figure 5.33

Figure 5.34

Figure 5.35

Figure 5.36

Figure 5.37

Figure 5.38

Figure 5.39

Figure 5.40

Figure 5.41

Figure 5.42

Figure 5.43

Figure 5.44

Figure 5.45

Figure 5.46

Figure 5.47

Figure 5.48

Figure 5.49

Figure 5.50

Figure 5.51

Figure 5.52

Figure 5.53

Figure 5.54

Figure 5.55

Figure 5.56

Figure 5.57

Figure 5.58

Figure 5.59

Figure 5.60

Figure 5.62

Figure 5.63

Figure 5.64

Figure 5.65

Figure 5.66

Figure 5.67

Figure 5.68

Figure 5.69

Figure 5.70

Figure 5.71

Figure 5.72

Figure 5.73

Figure 5.74

Figure 5.75

Figure 6.1

Figure 6.2

Figure 6.3

Figure 6.4

Figure 6.5

Figure 6.6

Figure 6.7

Figure 6.8

Figure 6.9

Figure 6.10

Figure 6.11

Figure 6.12

Figure 6.13

Figure 6.14

Figure 6.15

Figure 6.16

Figure 6.17

Figure 6.18

Figure 6.19

Figure 6.20

Figure 6.21

Figure 6.22

Figure 6.23

Figure 6.24

Figure 6.25

Figure 6.26

Figure 6.27

Figure 6.28

Figure 6.29

Figure 6.30

Figure 6.31

Figure 6.32

Figure 6.33

Figure 6.34

Figure 6.35

Figure 6.36

Figure 6.37

Figure 6.38

Figure 6.39

Figure 6.40

Figure 6.41

Figure 6.42

Figure 7.1

Figure 7.2

Figure 7.3

Figure 7.4

Figure 7.5

Figure 7.6

Figure 7.7

Figure 7.8

Figure 7.9

Figure 7.10

Figure 7.11

Figure 7.12

Figure 7.13

Figure 7.14

Figure 7.15

Figure 7.16

Figure 7.17

Figure 7.18

Figure 7.19

Figure 7.20

Figure 7.21

Figure 7.22

Figure 7.23

Figure 7.24

Figure 7.25

Figure 7.26

Figure 7.27

Figure 7.28

Figure 7.29

Figure 7.30

Figure 7.31

Figure 7.32

Figure 7.33

Figure 7.34

Figure 7.35

Figure 7.36

Figure 7.37

Figure 7.38

Figure 8.1

Figure 8.2

Figure 8.3

Figure 8.4

Figure 8.5

Figure 8.6

Figure 8.7

Figure 8.8

Figure 8.9

Figure 8.10

Figure 8.11

Figure 8.12

Figure 8.13

Figure 8.14

Figure 8.15

Figure 8.16

Figure 8.17

Figure 8.18

Figure 8.19

Figure 8.20

Figure 8.21

Figure 8.22

Figure 8.23

Figure 8.24

Figure 8.25

Figure 8.26

Figure 8.27

Figure 8.28

Figure 8.29

Figure 8.30

Figure 8.31

Figure 8.32

Figure 8.33

Figure 8.35

Figure 8.34

List of Tables

Table 3.1

Table 3.2

Table 3.7

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 4.6

Table 4.7

Table 4.8

Table 4.9

Table 4.10

Table 4.11

Table 4.12

Table 4.13

Table 4.14

Table 4.15

Table 4.16

Table 4.17

Table 4.18

Table 4.19

Table 4.20

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Table 5.6

Table 5.7

Table 6.1

Table 6.2

Table 6.3

Table 6.4

Table 6.5

Table 6.6

Table 6.7

Table 6.8

Table 6.9

Table 6.10

Table 6.11

Table 7.1

Table 7.2

Table 7.3

Table 7.4

Table 7.5

Table 7.6

Table 8.1

Table 8.2

Table 8.3

Table 8.4

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Edited by Tran Quoc Khanh, Peter Bodrogi, Quang Trinh Vinh, and Holger Winkler

LED Lighting

Technology and Perception

Editors

Prof. Tran Quoc Khanh

Technische Universität Darmstadt

Laboratory of Lighting Technology

Darmstadt, Germany

Dr. Peter Bodrogi

Technische Universität Darmstadt

Laboratory of Lighting Technology

Darmstadt, Germany

Dr. Quang Trinh Vinh

Technische Universität Darmstadt

Laboratory of Lighting Technology

Darmstadt, Germany

Dr. Holger Winkler

Merck KGaA

Darmstadt, Germany

All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.

Library of Congress Card No.: applied for

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

Bibliographic information published by the Deutsche Nationalbibliothek

The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at <http://dnb.d-nb.de>.

© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany

All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.

Print ISBN: 978-3-527-41212-9

ePDF ISBN: 978-3-527-67017-8

ePub ISBN: 978-3-527-67016-1

Mobi ISBN: 978-3-527-67015-4

oBook ISBN: 978-3-527-67014-7

Foreword

The lighting industry has been going through a revolution with the phasing out of the tungsten source and the advance of LED technology. The latter is well known for its greater energy efficiency, longer life, and its ability to provide a more adjustable spectral power distribution, illuminance, and beam shape. These features lead to large changes in lighting applications: LED is now not just meeting the basic need of illumination, but also moving into the improvement of work performance, the provision of optimum atmosphere environment, and the achievement of better health and wellbeing. However, its characteristic of providing intensive illuminance in small fields leads to some large performance discrepancies when compared with conventional light sources such as fluorescent and tungsten. Many of the earlier quality measures cannot be applied to LED sources and for this reason new standards and guidelines have recently been developed. So far, there has been no good textbook in illumination engineering to focus mainly on LED technologies.

The book covers a comprehensive range of topics on LEDs from the fundamental sciences of vision, radiometry and photometry, colorimetry, and the circadian rhythm; followed by the manufacturing techniques of radiation generation and packaging, the design, and modeling of light sources; then lighting quality measures under photopic and mesopic regions; and finally, the optimization and characterization of indoor and outdoor lighting.

A reader who is unfamiliar with color science should have no problem to go through the book, as it gives sufficient background information. Thus the book will be suitable for beginners as well as for more experienced readers. A number of topics are focused on the development of new standards by the International Commission on Illumination (CIE), which is based on recent results of LED lighting research and applications. The book can be considered as the most comprehensive and an up-to-date textbook available, which will inject new knowledge for both color engineers and academic researchers.

My congratulations go to the authors for the great efforts in compiling this text. They include a large number of examples of their research results, and have demonstrated that the research and teaching go hand in hand.

I am strongly recommending this textbook as a valuable research and development tool.

Ronnier Luo

Professor of Zhejiang University (China),

Leeds University (UK),

National Taiwan University of Science and Technology (ROC),

Director CIE Division 1

Table of the Coauthors

Coauthors

Chapter

Andreas Benker, Company Merck KGaA (Darmstadt)

3.4

Dr. Ralf Petry, Company Merck KGaA (Darmstadt)

3.4

Aleksander Zych, Company Merck KGaA (Darmstadt)

3.4

Charlotte Bois, Technische Universität Darmstadt & Company Merck KGaA (Darmstadt)

3.5

Hristo Ganev, Technische Universität Darmstadt

4.10 and 8.4

Max Wagner, Technische Universität Darmstadt

4.11

Dmitrij Polin, Technische Universität Darmstadt

5.11

Nils Haferkemper, Technische Universität Darmstadt

6.4

Christoph Schiller, Technische Universität Darmstadt

6.5

Stefan Brückner, Technische Universität Darmstadt

6.7

Preface

LED technology is a dynamically developing technology. It has a substantial impact on worldwide technological development and the way of social thinking. With the rapid growth of LED technology, new materials, industrial value chains, manufacturing methods, and optimizations processes have been established in order to improve energy efficiency, light, and color quality and to reduce the amount of material, environmental, and human resources utilized for lighting applications, and, consequently, to contribute to today's worldwide energy saving efforts. Recognizing the huge potential of LED components and LED systems to generate smart and flexible spectral power distributions, correlated color temperatures, angular luminous intensity distributions and absolute luminous flux levels, the disciplines of lighting engineering, and vision science should improve their current photometric and colorimetric quantities and attributes and calculation methods and research deeply the new aspects of human eye physiology and psychophysics to achieve a new quality for the fundamental description of human visual information processing including light, color, and spatial structure.

In this context, there have been numerous LED related research and development projects in Europe, North America, and Asia that characterized the technological and human physiological aspects of LED components and LED luminaires for indoor and outdoor applications. LED Lighting – Technology and Perception is based on the results of several research and development projects as well as engineering projects in Europe. These projects were conducted under the leadership of the Laboratory of Lighting Technology of the Technische Universität Darmstadt (Germany). This book also summarizes current international research and development outcomes and shows the development tendencies in the field of LED technology and research.

The authors would like to acknowledge the German Federal Ministry for Education and Research (BMBF) and Federal Ministry for Economy (BMWi) for funding our LED research and advancing our LED projects. The authors also would like to thank the German companies Merck (Darmstadt), Arnold & Richter Cine Technik (Munich), Trilux (Ansberg), Bäro (Leichlingen), TechnoTeam (Ilmenau), and Ilexa (Ilmenau) for allowing us the use of their picture materials.

Darmstadt,

September 2014

Tran Quoc Khanh

Peter Bodrogi

Trinh Quang Vinh

Holger Winkler

1Introduction

Peter Bodrogi and Tran Quoc Khanh

The technology of LED lighting (the illumination with light emitting diodes), especially with modern high-power LEDs has developed very rapidly in the past decade. According to the LEDs' tendency to achieve high energy efficiency with superior lighting quality and accordingly, a high level of user acceptance, LEDs have acquired a substantial economical relevance worldwide. The market share of LED light sources and LED luminaires is increasing rapidly at the time of writing. Nevertheless, to ensure good lighting quality for the human light source user, the user's perceptual characteristics (e.g., the way they perceive colors) are considered as important optimization criteria during the design and development of high-tech LED illumination systems. This book presents optimization guidelines for LED technology in the view of human perceptual features within the interdisciplinary framework of lighting engineering.

Lighting engineering deals with the energy efficient and application dependent production, characterization, transmission, and effects of optical radiation on human users taking the aspects of visual perception and light and health aspects into account [1]. The four main subject areas of lighting engineering are shown in Figure 1.1.

Figure 1.1 Subject areas of lighting engineering [1].

As can be seen from Figure 1.1, the principles of light production techniques are important to manufacture light sources (lamps, lamp modules, luminaires, headlights) with certain desirable spectral, energetic, geometric properties, and spatial light distributions of the light they generate. Using advanced illumination techniques, this light is projected onto the object arranged in different room geometries (in interior lighting) or street geometries (in exterior lighting). Light measurement techniques, in turn, have the task to physically measure the descriptor quantities of the visual (e.g., luminance, chromaticity) and light and health (e.g., circadian) characteristics of the illuminating system.

Colorimetry and color science (together with eye psychology and visual psychometry) analyze the answers of the human visual system to the spectral (and spatial) properties of the visual stimulus (i.e., the optical radiation reaching the human eye) in detail. In colorimetry and in color science, numeric descriptor quantities are defined to quantify human perceptions together with the circumstances under which a human visual model is valid, for example, different models for different adaptation levels – ranging between daytime vision down to nighttime vision, or different models for different sizes of the visual stimulus (e.g., 2° vs 10°).

This lighting engineering framework is complemented by theoretical knowledge on the chemistry and material science of phosphors and semiconductors as well as relevant aspects of electronics and thermodynamics. The necessary deep theoretical knowledge is applied to practical problems. Principles of LED design are illustrated by real-life examples so that, at the end, real know-how is conveyed in terms of easy-to-understand and easy-to-use numeric criterion values. The aim is to help apply this knowledge in the everyday practice of engineers involved in the design, development and manufacturing of LED components, light sources, lamps, luminaires, and LED lighting systems. Engineers can use this book as a theoretical reference and a practical guide to solve problems related to these types of questions:

How can the technology of LED light sources and luminaires be optimized for the human user to increase user acceptance? How can the LED user's visual performance and light and health aspects be enhanced?

In this respect, what are the most important components (that generate and distribute electromagnetic radiation and dissipate heat) of LED light sources and luminaires?

What materials can be used to manufacture them and how can these materials be improved and their arrangement improved?

How can the radiation of LED chips be converted by today's diverse variety of phosphors? How can phosphor converted LEDs be combined with pure (colored) chip LEDs to achieve high lighting quality in a high-end interior lighting system?

What are the most important chemical, physical, and technological parameters of these LED devices exhibiting previously unknown design flexibility hence huge optimization potentials for human users?

How can the input (e.g., current, temperature) and output (e.g., spectral radiance) parameters of the LED devices be measured physically?

How can LED devices be modeled and controlled (e.g., by pulse width modulation) – including their aging phenomena?

How does the visual stimulus of the scene illuminated by the LED light source come to existence?

How can LED illumination systems exploit the human visual system's properties to provide an excellent (interior or exterior) environment lit by LEDs for excellent visual performance (excluding glare, flicker, and stroboscopic artifacts) and high lighting quality?

What interactions are there between the light from the LED light source and the colored objects that reflect it and how does this interaction influence the perceptions or the light and health aspects of human users?

How can LED lighting systems be optimized for lower luminance levels in typical nighttime automotive lighting and street/road lighting applications from the point of view of the human visual system in order to optimize brightness perception, visual performance, and visual acuity?

How can objective (numeric) criteria be derived from human measurements that can be used to optimize a LED lighting system by engineering methods? How can we formulate such criteria in terms of really usable numbers in engineering practice?

To answer the above questions and to cope with related practical subjects in real-world application effectively, Figure 1.2 shows the interdisciplinary workflow concept of the present book for the technological and perceptual co-optimization of LEDs.

Figure 1.2 Interdisciplinary workflow concept for the technological and perceptual optimization of LED lighting systems.

As can be seen from Figure 1.2 (going from the upper box in the middle toward the right), the LED lighting system illuminates an indoor scene or an outdoor scene with an arrangement of colored objects with certain spectral reflectance properties at a certain luminance level (daytime, twilight, or nighttime). The light coming from the light source reaches the human eye sometimes directly but is often reflected from the objects of the scene (see also Figure 2.14, left). Reflection changes the spectral composition of the light and a plethora of different color stimuli arises. Parallel to this, the light also generates nonvisual brain signals, for example, via the circadian stimulus which is responsible for the timing of the daily rhythm of the human user.

The stimulus (i.e., the light reaching the eye) has some characteristic properties that strongly influence the perception (see also Figure 2.14, right) it evokes in the human visual system: its viewing angle, its retinal eccentricity after being imaged onto the retina (i.e., the photoreceptive layer in the eye), its luminance, spectral radiance, and spatial structure (e.g., a homogeneous disk or a complex letter structure). Perceptions also depend on the characteristics of the human observer (age, health, and gender). The cognitive interpretations of the perceptions and the decisions based on them (e.g., to purchase or not to purchase a lighting system) also depend on the profession and culture of the user and his or her region of origin he or she happens to live in or has grown up in.

To provide numeric optimization criteria for engineers, usable (i.e., practice oriented and not too complex) models of the human visual system (and nonvisual systems like the Circadian system) can be described systematically and they must be well understood. These models compute the objective optimization criteria for the LED lighting system from the physically measured characteristics of the stimulus (e.g., its spectral radiance distribution). These criteria can be used, in turn, for the optimization process of the different components of the LED illumination system. Now, going back to the upper box in the center of Figure 1.2 again, these optimized components constitute an advanced LED lighting system whose enhanced visual properties can be validated by measuring it physically, computing its improved visual criterion numbers (e.g., a higher color rendering index) and, finally, validating it in a dedicated field study.

As can be seen from Figure 1.2, the objective optimization criteria derived from the human models belong to the left (technological) side of the workflow concept as these criteria represent technological optimization targets. Anyway, technological optimization can be carried out only if usable models of the LED system components are available for the engineer. Models based on the knowledge from physics, chemistry, and material science can be formulated for the LED chip (semiconductor structure), the phosphor, the packaging of the LED light source, its optics, control electronics, the temperature, and current dependence of its light output as well as for the aging of LEDs after thousands or ten thousands of operating hours. If such models and their optimization criteria are known then LED technology will be able to achieve an important one of its ultimate goals: better-quality human perception. This book is intended to help the engineer achieve this ambitious objective by the systematic concept of its chapter structure shown in Figure 1.3.

Figure 1.3 Chapter structure of the book.

As can be seen from Figure 1.3, Chapters 3 and 4 deal with technological aspects (left-hand side) while Chapters 2, 5, and 6 are related to human perceptual issues (right-hand side). Chapter 3 describes the principles of how LEDs generate electromagnetic radiation – including semiconductors, phosphors and packaging. Chapter 4 presents LED specific measurement procedures from the lighting engineer's point of view and applies them to collect data as inputs for usable LED models, both for short-term modeling and for modeling LED aging and to predict their lifetime. Chapter 2 introduces the basics of the human visual system and its models – extended to a Circadian model (timing human daily rhythms by light).

Chapter 5 deals with the photopic perceptual aspects relevant to the design of interior LED lighting, for example, color rendering, white tone quality, and the chromaticity binning of LEDs. Chapter 6 communicates the mesopic (twilight) perceptual aspects of LED illuminating systems relevant to exterior applications requiring lower light levels, for example, brightness perception, glare, and visual performance in nighttime driving with LED car headlamps or LED based street/road lighting. Finally, Chapters 7 and 8 combine and apply the knowledge accumulated in the earlier chapters according to the workflow concept of Figure 1.2 to deduce optimization procedures and principles for LED luminaire design with lots of demonstrative practical and numeric examples. Finally, Chapter 9 recapitulates the most important lessons and findings of the book.

Reference

1. Khanh, T.Q. (2013) Licht- und Farbforschung: Augenphysiologie, Psychophysik, Technologie und Lichtgestaltung.

Z. Licht

,

4

(2013), 60–67.

2The Human Visual System and Its Modeling for Lighting Engineering

Peter Bodrogi and Tran Quoc Khanh

Chapter 2 defines the most important concepts necessary to understand the perceptual aspects of LED (light emitting diode) technology throughout this book including the basics of the human visual system, radiometry and photometry, colorimetry and color science as well as the human circadian system. According to its relevance to the subject, a short section on LED specific colorimetric quantities is added.

Chapter 2 incorporates issues like the photoreceptor structure of the human retina (density and spectral sensitivity of rods and cones), spatial and temporal contrast sensitivity (CS) of the human visual system, color appearance (related and unrelated colors, lightness and brightness, hue, colorfulness, and saturation), color difference perception, chromatic adaptation, blackbody radiators and phases of daylight, color matching functions, and the use of color spaces (CIELAB, CIECAM02). Chapter 2 is intended to provide a short introduction while the interested reader is encouraged to refer to literature (e.g.,[1, 2]).

2.1 Visual System Basics

2.1.1 The Way of Visual Information

Figure 2.1 shows the way of visual information to and through the human brain [3].

Figure 2.1 Schematic view of the way of visual information to and in the human visual system (see text).

The left side of Figure 2.1 shows the two overlapping parts of the visual fields of the two eyes. The optics of the eye images these two parts of the visual field onto the two retinae (in the left eye and the right eye) that contain photoreceptor mosaics and neural preprocessing cells. In the photoreceptors of the retina, light signals are converted into neural action potentials. The neuronal layers of the retina preprocess these signals and forward them through the optic nerves toward the so-called LGN (lateral geniculate nucleus). Nerve fibers from the two eyes cross (partially) in the so-called chiasma (opticum). About 90% of the visual nerves (also called optic nerves) reach the visual cortex (containing the processing units of the different visual sensations and interpretations like motion, color, spatial structure, object segmentation, and recognition) over the LGN. About 10% of the retinal signals reaches other regions of the brain (in the parietal and temporal lobes) responsible, for example, for the control of the release of hormones (see Section 2.5)

2.1.2 Perception

Perception is a psychophysical process. The human being receives physical information (the so-called stimulus) about the state and changes of the environment via sensory organs and processes this information in the brain to obtain perceptions and to take decisions on the basis of the quality and magnitude of these perceptions[3]. The perceptual process is flexible: it depends on the context of the stimulus being perceived and also on previous experience (knowledge) of the human subject. It should be mentioned that not all stimuli result in a perception: some of the stimuli are not perceived at all (e.g., a light signal of very low contrast or electromagnetic radiation with 2 µm wavelength).

2.1.3 Structure of the Human Eye

Figure 2.2 illustrates the structure of the human eye. As can be seen from Figure 2.2, the human eye is an ellipsoid with an average length of about 26 mm and a diameter of about 24 mm. The eye is rotated in all directions by the aid of eye muscles. The outer layer is called sclera. The sclera is continued as the transparent cornea at the front. The choroidea supplies the retina with oxygen and nutrition. The retina is the photoreceptive (interior) layer of the eye also containing the visual preprocessing cells (see Sections 2.1.6–2.1.8). The vitreous body is responsible for maintaining the ellipsoid form of the eye. It consists of a suspension of water (98%) and hyaluronic acid (2%).

Figure 2.2 Structure of the human eye (the optic nerve is also called visual nerve). O, optic disk – the point at which the optic nerve passes through the eye and transmits the preprocessed neural signals of the retina toward the visual cortex.

The optical system of the human eye is a complex, slightly decentered lens system projecting an inverted and downsized image of the environment onto the retina. The cornea, the anterior chamber, and the iris constitute the front part of this optical system and then, the posterior chamber and the biconvex eye lens follow. The lens is held by the zonule fibers. By contracting the ciliary muscles, the focal length of the lens can be changed. The visual angle intersects the retina at the fovea (centralis), the location of sharpest vision.

The most important optical parameters of the components of the eye media include refractive indices (ranging typically between 1.33 and 1.43) and spectral transmission factors. All parameters vary among different persons considerably and are subject to significant changes with aging. Especially, accommodation, visual acuity, and pupil reactions are impaired with increasing age. The spectral transmission of the eye media decreases with age significantly, especially for short wavelengths.

After having reached the retina, light rays have to travel through the retinal layers and in the central retina also the so-called macula lutea (a yellow pigment layer that protects the central retina) before reaching the photoreceptors placed at the rear side of the retina. The optic disk (also called optic nerve head or blind spot) is the point (designated by O in Figures 2.2 and 2.4) at which the optic nerve passes through the eye. The retina is blind at the location O (as the density of rods and cones equals zero there).

2.1.4 The Pupil

A hole located at the center of the iris constitutes the pupil, see Figure 2.2. Its size depends on the amount of irradiance the retina receives, generally varying between 2 (in a bright environment) and 8 mm (in darkness but 8 mm is seldom unless a drug is administered). This range corresponds to a dynamic luminous flux range of 1 : 16 and represents one component of the adaptation mechanisms of the human visual system to changing light levels. The pupil diameter dP (in millimeter units) can be computed (approximately) by the so-called Moon and Spencer equation (Eq. (2.1)) or the so-called DeGroot and Gebhard equation (Eq. 2.2) from the adaptation luminance in the viewing field of the observer (LA in cd m−2 units).

2.1

2.2

Both equations are visualized in Figure 2.3.

Figure 2.3 Pupil diameter dP (in millimeter units) as a function of adaptation luminance in the viewing field of the observer (LA in cd m−2 units), see Eqs. (2.1) and (2.2).

2.1.5 Accommodation

Accommodation is a property of the eye that enables to focus on an object located at an arbitrary distance in front of the eye so that the image of this object on the retina becomes sharp. As the eye lens becomes less elastic with increasing age, the old eye is unable to accommodate to near objects. The range of possible accommodation distances diminishes with decreasing adaptation luminance level.

2.1.6 The Retina

The retina (a layer of a typical thickness of 250 µm in average) is part of the optical system of the eye. The so-called Müller cells (or Müller glia, the support cells for the neurons of the retina) funnel light (like optical fibers) to the photoreceptors that are situated at the opposite side of the retina (viewed from the eye lens). The retina contains a complex cell layer with two types of photoreceptors, rods, and cones. Both the rod receptors and the cone receptors are connected to the nerve fibers of the optic nerve via a complex network that computes neural signals from receptor signals. This network is part of the retina: it is a mesh of different types of processing cells, so-called horizontal, bipolar, amacrine, and ganglion cells.

The retina contains about 6.5 millions of cones and 110–125 millions of rods while the number of nerve fibers is about 1 million. The density of rods and cones is different and depends on retinal location. Recently, a third type of photosensitive cell has been discovered, the so-called ipRGC (intrinsically photosensitive retinal ganglion cell containing the pigment melanopsin) responsible for regulating the circadian rhythm (Section 2.5). Figure 2.4 shows rod density and cone density as a function of retinal location.

Figure 2.4 Rod density and cone density (ordinate, 104/mm2) as a function of retinal location (abscissa: α in degrees). (Drawn after Oesterberg[4].) O, optic disk (blind spot), compare with Figure 2.2.

As can be seen from Figure 2.4, there are no receptors at the position of the optic disk or blind spot as the optic nerve exits the eye at this place (designated by O, compare with Figure 2.2). The fovea is located in the center of the macula lutea region. A characteristic value to represent the diameter of the fovea is 1.5 mm corresponding to about 5° of visual angle. The fovea is responsible for best visual acuity according to the high cone receptor density; see the cone density maximum in Figure 2.4.

The center of the fovea is the foveola (or central pit) which has a diameter of about 0.2 mm (0.7°). The foveola has the highest cone density hence highest visual acuity. In its central part (in the angular range of about 20′), every cone (with a cone diameter of 1.5 µm) is connected to its individual nerve fiber via a single bipolar cell and ganglion cell. There are only cone photoreceptors and no rods in a central region of about 0.350 mm (1.25°) horizontal diameter within the fovea[5] (see the rod density minimum in Figure 2.4). Outside the fovea, cone diameter increases up to about 4.5 µm, cone density decreases, and rod (diameter of rods: 2 µm) density increases to reach a rod maximum at about 20° (temporally rather at 18°), according to Figure 2.4.

Rods are responsible for nighttime vision (also called scotopic vision without the ability of accurate fixation and high visual acuity). Rods are more sensitive than cones (cones are responsible for daytime or photopic vision). Besides pupil contraction, the transition between rod vision and cone vision (in the so-called twilight or mesopic range, see Chapter 6) constitutes a second important adaptation mechanism of the human visual system to changing light levels. (There is a third adaptation mechanism, the gain control of the receptor signals.)

Photoreceptors contain pigments (opsins, certain types of proteins) that change their structure when they absorb photons and generate neural signals that are preprocessed by the horizontal, amacrine, bipolar, and ganglion cells of the retina to yield neural signals for later processing via the different visual (and nonvisual) pathways. Above a luminance of about 100 cd m−2, rods become saturated (they do not produce any signal). Vision is then mediated purely by cone signals.

2.1.7 Cone Mosaic and Spectral Sensitivities

There are three types of cone with pigments of different spectral sensitivities, the so-called L (long-wavelength sensitive), M (middle-wavelength sensitive), and S (short-wavelength sensitive) cones that yield the so-called L, M, and S signals for the perception of homogeneous color patches and colored spatial structures (e.g., a red–purplish rose with fine color shadings). The number of different cone signals (three) has an important psychophysical implication for color science that the designer of a LED light source should be aware of: color vision is trichromatic, color spaces have three dimensions (Section 2.3), and there are three independent psychological attributes of color perception (hue, saturation, brightness).

L, M, and S cones build a retinal cone mosaic. The central (rod-free) part of the cone mosaic is illustrated in Figure 2.5.

Figure 2.5 Cone mosaic of the rod-free inner fovea subtending about 1.25°, that is, about 350 µm. Red dots: long-wavelength sensitive cone photoreceptors (L cones). Green dots: middle-wavelength sensitive cones (M cones). Blue dots: short-wavelength sensitive cones (S cones).

(Figure 1.1 from Ref.[1]. Reproduced with permission from Cambridge University Press.)

As can be seen from Figure 2.5, the central part of the foveola (subtending about 20′ of visual angle or about 100 µm) is free from S cones. This fact results in the so-called small-field tritanopia, that is, the insensitivity to bluish light for very small central viewing fields. There are in average (among different observers) 1.5 times as many L cones as M cones in this region of the retina[1]. L and M cones represent 93% of all cones while S cones represent 7%.

The relative spectral sensitivities of the L, M, and S cones are depicted in Figure 2.6 together with some other important functions are discussed forward. A database of all characteristic functions of the human visual system (including these functions) can be found in the web[6].

Figure 2.6 Relative spectral sensitivities of the L, M, and S cones (for 2°)[6–8] as well as other visual mechanisms that use the LMS cone signals as input. The sensitivity of the ipRGC mechanism (Section 2.5) is also shown. The spectral sensitivity of the rods (dark green curve) is approximated by the V′(λ) function. V(λ): luminous efficiency function (the basis of photometry, for stimuli of standard viewing angle, about 1°–4°); V10(λ): its alternative version for stimuli of greater viewing angle (about 10°).

The spectral sensitivities in Figure 2.6 were measured at the cornea of the eye. They incorporate the average spectral transmission of the ocular media and the macular pigment at a retinal eccentricity of 2°. As can be seen from Figure 2.6, the spectral bands of the L, M, and S cones[6–8] yield three receptor signals for further processing. From these signals, the retina derives two chromatic signals: (i). L–M (the red–green opponent signal or its mediating neural channel) and (ii) S − (L + M) (the yellow–blue opponent signal or channel), and (iii) one achromatic signal, L + M. The L + M channel is usually considered as a luminance channel. The most important role of the luminance channel is that it enables the vision of fine image details (Section 2.1.8). In these signals, the “+” and “−”characters are only symbolic. In vision models, the L, M, and S signals must be weighted, for example, αS − (βL + γM).

As can be seen from Figure 2.6, the L, M, and S cone sensitivity curves have their maxima at 566, 541, and 441, respectively[1]. In photometry (Section 2.2), for stimuli subtending 1°–4° of visual angle, the spectral sensitivity of the L + M channel is approximated by a standardized function, the so-called luminous efficiency function (V(λ)) (the basis of photometry, see Section 2.2) while for spatially more extended (e.g., 10°) stimuli, the so-called V10(λ) function (the CIE - International Commission on Illumination) 10° photopic photometric observer[9] is used.

For practical applications (e.g., the prediction of brightness perception under mesopic, that is, twilight conditions, see Figure 5.41; or to model mesopic spectral detection sensitivity, see Figure 6.2), it is important to compare the spectral sensitivity of the rod (R) mechanism (approximated by the so-called V′(λ) function), with the spectral sensitivities of the L, M, S cones, the spectral sensitivity of the two chromatic mechanisms (L − M and S − (L + M)), with V(λ) and V10(λ) (that roughly represent the L + M signal as mentioned before) and also with the already mentioned ipRGC mechanism (Section 2.5), see Figure 2.6.

2.1.8 Receptive Fields and Spatial Vision

Objects with characteristic spatial details of different spatial frequencies (e.g., a pedestrian on the roadside for exterior lighting or a red rose on the table for interior lighting) illuminated by the LED light source can be discerned by the human observer from their background[10]. The LED light source (and the illuminated objects) can be designed so that the coarse or fine spatial structures are perceived. For correct light source design, in order to consider the properties of this so-called spatial (contrast) vision correctly, the spatial frequency characteristics of the earlier-mentioned channels (L + M, L − M, S − (L + M)) of the human visual system can be understood.

To do so, it is important to study how the human visual system analyzes a spatial structure in a retinal image: ganglion cells gather and process the signals from several cones (at least outside the very central part of the retina where there is a one-to-one correspondence) located inside their so-called receptive fields. Receptive fields of ganglion cells are able to amplify spatial contrasts inside the image: every receptive field has a circular center and a concentric circular surround, see Figure 2.7.

Figure 2.7 Left: Schematic representation of the receptive field of an “on-center” ganglion cell, +: center, −: surround. Middle column: black – no light, white – light stimulus, from top to bottom: 1. no light over the whole receptive field; 2. contrast – light on the center, no light on the surround; 3. light over the whole receptive field; 4. light on the surround. Right column: firing rate, from top to bottom: weak, strong, weak, no response[10].

(Reproduced with permission from Wiley VCH.)

A stimulation of the center and the surround by a light signal leads to opposite firing reactions of the ganglion cell. The ganglion cell is firing when the stimulus is in the center (“on-center cell”) while it is inhibited when the stimulus is in the surround. The other type of ganglion cell (“off-center cell”) is inhibited when the stimulus is in the center and firing when the stimulus is in the surround. This way, spatially changing stimuli (contrasts or edges) increase firing while spatially homogenous stimuli generate only a low response level, see Figure 2.7.

On the human retina, achromatic contrast (i.e., spatial changes of the L + M signal) is detected according to the principle of Figure 2.7. Similar receptive field structures produce the chromatic signals for chromatic contrast, that is, spatial changes of the L − M or S − (L + M) signals. But in the latter case, the spectral sensitivity of the center differs from the spectral sensitivity of the surround because of the different combinations of the L, M, and S cones in the center and in the surround. Such a receptive field structure is called double opponent because there is spatial opponency (center/surround) and spectral (cone) opponency (L/M or S/(L + M))[10].

It is the size and sensitivity of the receptive fields and the spatial aberrations of the eye media (cornea, lens, vitreous humor) that determine the spatial frequency characteristics of the achromatic and chromatic channels[11]. In practical applications including LED lighting, the question is how much achromatic (or chromatic) contrast is needed to detect or recognize a visual object of a given size corresponding to a given spatial frequency while size can be expressed in degrees of visual angle and spatial frequency is expressed in cycles per degree (cpd) units (e.g., 5 cpd means that there are five pairs of thin black and white lines within one degree of visual angle).

Contrast (C) can be measured either by the contrast ratio, that is, the signal value (L + M, L − M or L + M − S) of the object (SO) divided by the signal value of its background (SB), that is, SO/SB or by the so-called Michelson contrast (SO − SB)/(SO + SB). Contrast sensitivity is defined as the reciprocal value of the threshold value of contrast needed to detect or recognize an object at a given spatial frequency. Chromatic CS functions (depending on spatial frequency) of the L − M and S − (L + M) channels are compared with the achromatic contrast (L + M) sensitivity function at a high retinal illuminance level in Figure 2.8.

Figure 2.8 Chromatic contrast sensitivity functions of the L − M and S − (L + M) channels compared with the achromatic contrast sensitivity function (at a high retinal illuminance level and in the fovea). Abscissa: spatial frequency in cpd units, ordinate: contrast sensitivity (relative units)[10, 11].

(Reproduced with permission from Wiley VCH.)

As can be seen from Figure 2.8, achromatic or L + M (luminance) CS is a band-pass function of spatial frequency increasing up to about 3–5 cpd and then decreasing toward high spatial frequencies. For about 40 cpd (corresponding to a visual object of about 1 min of arc) or above, achromatic CS equals zero. This means that it is no use increasing the contrast (even up to infinity, i.e., black on white) if the object is smaller than about 1 min of arc. This is the absolute limit of (foveal) visual acuity. Contrary to the achromatic CS function with bandpass nature, chromatic CS functions are low pass functions of spatial frequency[11]. The most important point is that it is the L + M (luminance) channel (its spectral sensitivity is approximated by the V(λ) function) that enables high visual acuity, that is, the vision of high spatial content but only in the fovea where the density of L cones and M cones is very high, see Figure 2.4.

2.2 Radiometry and Photometry

As mentioned in Chapter 1, lighting engineering deals with the energy efficient and application dependent production, characterization, transmission, and effects of optical radiation on human users taking the aspects of visual perception and light and health aspects into account. Accordingly, after the introduction of the basic properties of the human visual system in Section 2.1, the concepts of radiometry and photometry are summarized as a further important basic knowledge (characterization, transmission) for lighting engineering. Production of LED radiation is dealt with in Chapter 3 while LED specific issues of radiometry, photometry, and colorimetry are sketched in Section 2.4 (about basic concepts) and Section 4.1 (about advanced characterization and measurement).

The subject of radiometry and photometry is more complex and more extensive than what would be possible to present in this short section and its literature is abundant. Hence, the purpose of this section is just to define and illustrate the basic concepts to get an overview and a feeling for a better understanding of this book. The interested reader is advised to study literature (e.g.,[12]) for more detail.

The concept of radiometry can be defined as follows: “Radiometry is the measurement of energy content of electromagnetic radiation fields and the determination of how this energy is transferred from a source, through a medium, and to a detector”[12]. The concept of photometry can be defined as follows: “The radiation transfer concepts … of photometry are the same as those for radiometry. The exception is that the spectral responsivity of the detector, the human eye, is specially defined. Photometric quantities are related to radiometric quantities via the spectral efficiency functions defined for the photopic and scotopic CIE Standard Observer”[12].

These functions are the two standard luminous efficiency functions, the CIE (1924) photopic V(λ) function (for daytime vision) and the CIE (1951) scotopic V′(λ) function (for nighttime rod vision), see Figure 2.6. Therefore, photometry can be considered as a special case of radiometry which is applied to the spectral sensitivity of the human eye as a detector of radiation. To be illustrative, the most important photometric concepts (based on the V(λ) function) can be defined parallel to their radiometric counterparts. Figure 2.9 illustrates the transition from radiometric quantities to photometric quantities.

Figure 2.9 Photometric quantities are V(λ)-weighted radiometric quantities.

As can be seen from Figure 2.9 (left), if the spectral dependence Xeλ of the radiometric quantity Xe is known then the radiometric quantity itself can be obtained by integration in the visible wavelength range between 380 and 780 nm, see Eq. (2.3).

2.3

As can be seen from Figure 2.9 (right), to obtain the corresponding photometric quantity, XV, the function Xeλ can be weighted by the CIE (1924) photopic luminous efficiency function (V(λ) in Figure 2.9, middle) across the visible spectrum (between 380 and 780 nm). Then, this weighted function (Xλ, right) can be integrated in the visible range (between 380 and 780 nm) to obtain the corresponding photometric quantity, XV. This is expressed by Eq. (2.4).

2.4

In Eq. (2.4), XV is a photometric (luminous) quantity for photopic vision, Xeλ is a radiant quantity, Km is the luminous efficacy of radiation (LER) for photopic vision, Km = 683 lm W−1, and V(λ) is the CIE (1924) spectral luminous efficiency function for photopic vision. The value of Km converts the power of electromagnetic radiation to a corresponding photometric unit, lumen (lm). Similarly, scotopic quantities can also be defined but then the V′(λ) function (see the dark green curve in Figure 2.6) shall be used in Eq. (2.4) instead of V(λ) and the value of Km shall be changed to Km = 1699 lm W−1 (scotopic value) instead of Km = 683 lm W−1 (photopic value).

2.2.1 Radiant Power (Radiant Flux) and Luminous Flux

“Radiant power or radiant flux is the power (energy per unit time t) emitted, transferred or received in the form of electromagnetic radiation[12].” It is designated by Φe (unit: W), see Eq. (2.5).

2.5

The corresponding photometric quantity is luminous flux (Φv, unit: lumen, lm) as defined by Eq. (2.6).

2.6

Figure 2.10 illustrates the concept of radiant flux Φe.

Figure 2.10 Illustration of the concept of radiant flux: the total power of all rays emanating from the light source in all directions is considered.

As can be seen from Figure 2.10, radiant flux contains all rays emitted by the light source in all possible directions and the power of all rays shall be integrated to measure it.

2.2.2 Irradiance and Illuminance

Irradiance (Ee) is the ratio of the radiant power (dΦe) incident on an element of a surface (dA) to the area of that element (unit: W m−2), see Eq. (2.7).

2.7

The corresponding photometric quantity is illuminance (Ev, unit: lux, lx = lm m−2) as defined by Eq. (2.8).

2.8

Figure 2.11 illustrates the concept of irradiance (Ee).

Figure 2.11 Illustration of the concept of irradiance: the radiant power dΦe reaches the surface element dA.

2.2.3 Radiant Intensity and Luminous Intensity

“Radiant intensity (Ie, unit: W sr−1