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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley-IS&T Series in Imaging Science and Technology
- Sprache: Englisch
The essential resource for readers needing to understand visual perception and for those trying to produce, reproduce and measure color appearance in various applications such as imaging, entertainment, materials, design, architecture and lighting.
This book builds upon the success of previous editions, and will continue to serve the needs of those professionals working in the field to solve practical problems or looking for background for on-going research projects. It would also act as a good course text for senior undergraduates and postgraduates studying color science.
The 3rd Edition of Color Appearance Models contains numerous new and expanded sections providing an updated review of color appearance and includes many of the most widely used models to date, ensuring its continued success as the comprehensive resource on color appearance models.
Key features:
- Presents the fundamental concepts and phenomena of color appearance (what objects look like in typical viewing situations) and practical techniques to measure, model and predict those appearances.
- Includes the clear explanation of fundamental concepts that makes the implementation of mathematical models very easy to understand.
- Explains many different types of models, and offers a clear context for the models, their use, and future directions in the field.
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Veröffentlichungsjahr: 2013
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Contents
Series Preface
Preface
Acknowledgments
Introduction
WHAT IS A COLOR APPEARANCE MODEL?
1 Human Color Vision
1.1 OPTICS OF THE EYE
1.2 THE RETINA
1.3 VISUAL SIGNAL PROCESSING
1.4 MECHANISMS OF COLOR VISION
1.5 SPATIAL AND TEMPORAL PROPERTIES OF COLOR VISION
1.6 COLOR VISION DEFICIENCIES
1.7 KEY FEATURES FOR COLOR APPEARANCE MODELING
2 Psychophysics
2.1 PSYCHOPHYSICS DEFINED
2.2 HISTORICAL CONTEXT
2.3 HIERARCHY OF SCALES
2.4 THRESHOLD TECHNIQUES
2.5 MATCHING TECHNIQUES
2.6 ONE-DIMENSIONAL SCALING
2.7 MULTIDIMENSIONAL SCALING
2.8 DESIGN OF PSYCHOPHYSICAL EXPERIMENTS
2.9 IMPORTANCE IN COLOR APPEARANCE MODELING
3 Colorimetry
3.1 BASIC AND ADVANCED COLORIMETRY
3.2 WHY IS COLOR?
3.3 LIGHT SOURCES AND ILLUMINANTS
3.4 COLORED MATERIALS
3.5 THE HUMAN VISUAL RESPONSE
3.6 TRISTIMULUS VALUES AND COLOR MATCHING FUNCTIONS
3.7 CHROMATICITY DIAGRAMS
3.8 CIE COLOR SPACES
3.9 COLOR DIFFERENCE SPECIFICATION
3.10 THE NEXT STEP
4 Color Appearance Terminology
4.1 IMPORTANCE OF DEFINITIONS
4.2 COLOR
4.3 HUE
4.4 BRIGHTNESS AND LIGHTNESS
4.5 COLORFULNESS AND CHROMA
4.6 SATURATION
4.7 UNRELATED AND RELATED COLORS
4.8 DEFINITIONS IN EQUATIONS
4.9 BRIGHTNESS–COLORFULNESS VS LIGHTNESS–CHROMA
5 Color Order Systems
5.1 OVERVIEW AND REQUIREMENTS
5.2 THE MUNSELL BOOK OF COLOR
5.3 THE SWEDISH NCS
5.4 THE COLORCURVE SYSTEM
5.5 OTHER COLOR ORDER SYSTEMS
5.6 USES OF COLOR ORDER SYSTEMS
5.7 COLOR NAMING SYSTEMS
6 Color Appearance Phenomena
6.1 WHAT ARE COLOR APPEARANCE PHENOMENA?
6.2 SIMULTANEOUS CONTRAST, CRISPENING, AND SPREADING
6.3 BEZOLD–BRÜCKE HUE SHIFT (HUE CHANGES WITH LUMINANCE)
6.4 ABNEY EFFECT (HUE CHANGES WITH COLORIMETRIC PURITY)
6.5 HELMHOLTZ–KOHLRAUSCH EFFECT (BRIGHTNESS DEPENDS ON LUMINANCE AND CHROMATICITY)
6.6 HUNT EFFECT (COLORFULNESS INCREASES WITH LUMINANCE)
6.7 STEVENS EFFECT (CONTRAST INCREASES WITH LUMINANCE)
6.8 HELSON–JUDD EFFECT (HUE OF NON-SELECTIVE SAMPLES)
6.9 BARTLESON–BRENEMAN EQUATIONS (IMAGE CONTRAST CHANGES WITH SURROUND)
6.10 DISCOUNTING-THE-ILLUMINANT
6.11 OTHER CONTEXT, STRUCTURAL, AND PSYCHOLOGICAL EFFECTS
6.12 COLOR CONSTANCY?
7 Viewing Conditions
7.1 CONFIGURATION OF THE VIEWING FIELD
7.2 COLORIMETRIC SPECIFICATION OF THE VIEWING FIELD
7.3 MODES OF VIEWING
7.4 UNRELATED AND RELATED COLORS REVISITED
8 Chromatic Adaptation
8.1 LIGHT, DARK, AND CHROMATIC ADAPTATION
8.2 PHYSIOLOGY
8.3 SENSORY AND COGNITIVE MECHANISMS
8.4 CORRESPONDING COLORS DATA
8.5 MODELS
8.6 COLOR INCONSTANCY INDEX
8.7 COMPUTATIONAL COLOR CONSTANCY
9 Chromatic Adaptation Models
9.1 VON KRIES MODEL
9.2 RETINEX THEORY
9.3 NAYATANI ET AL. MODEL
9.4 GUTH’S MODEL
9.5 FAIRCHILD’S 1990 MODEL
9.6 HERDING CATS
9.7 CAT02
10 Color Appearance Models
10.1 DEFINITION OF COLOR APPEARANCE MODELS
10.2 CONSTRUCTION OF COLOR APPEARANCE MODELS
10.3 CIELAB
10.4 WHY NOT USE JUST CIELAB?
10.5 WHAT ABOUT CIELUV?
11 The Nayatani et al. Model
11.1 OBJECTIVES AND APPROACH
11.2 INPUT DATA
11.3 ADAPTATION MODEL
11.4 OPPONENT COLOR DIMENSIONS
11.5 BRIGHTNESS
11.6 LIGHTNESS
11.7 HUE
11.8 SATURATION
11.9 CHROMA
11.10 COLORFULNESS
11.11 INVERSE MODEL
11.12 PHENOMENA PREDICTED
11.13 WHY NOT USE JUST THE NAYATANI ET AL. MODEL?
12 The Hunt Model
12.1 OBJECTIVES AND APPROACH
12.2 INPUT DATA
12.3 ADAPTATION MODEL
12.4 OPPONENT COLOR DIMENSIONS
12.5 HUE
12.6 SATURATION
12.7 BRIGHTNESS
12.8 LIGHTNESS
12.9 CHROMA
12.10 COLORFULNESS
12.11 INVERSE MODEL
12.12 PHENOMENA PREDICTED
12.13 WHY NOT USE JUST THE HUNT MODEL?
13 The RLAB Model
13.1 OBJECTIVES AND APPROACH
13.2 INPUT DATA
13.3 ADAPTATION MODEL
13.4 OPPONENT COLOR DIMENSIONS
13.5 LIGHTNESS
13.6 HUE
13.7 CHROMA
13.8 SATURATION
13.9 INVERSE MODEL
13.10 PHENOMENA PREDICTED
13.11 WHY NOT USE JUST THE RLAB MODEL?
14 Other Models
14.1 OVERVIEW
14.2 ATD MODEL
14.3 LLAB MODEL
14.4 IPT COLOR SPACE
15 The CIE Color Appearance Model (1997), CIECAM97s
15.1 HISTORICAL DEVELOPMENT, OBJECTIVES, AND APPROACH
15.2 INPUT DATA
15.3 ADAPTATION MODEL
15.4 APPEARANCE CORRELATES
15.5 INVERSE MODEL
15.6 PHENOMENA PREDICTED
15.7 THE ZLAB COLOR APPEARANCE MODEL
15.8 WHY NOT USE JUST CIECAM97s?
16 CIECAM02
16.1 OBJECTIVES AND APPROACH
16.2 INPUT DATA
16.3 ADAPTATION MODEL
16.4 OPPONENT COLOR DIMENSIONS
16.5 HUE
16.6 LIGHTNESS
16.7 BRIGHTNESS
16.8 CHROMA
16.9 COLORFULNESS
16.10 SATURATION
16.11 CARTESIAN COORDINATES
16.12 INVERSE MODEL
16.13 IMPLEMENTATION GUIDELINES
16.14 PHENOMENA PREDICTED
16.15 COMPUTATIONAL ISSUES
16.16 CAM02-UCS
16.17 WHY NOT USE JUST CIECAM02?
16.18 OUTLOOK
17 Testing Color Appearance Models
17.1 OVERVIEW
17.2 QUALITATIVE TESTS
17.3 CORRESPONDING-COLORS DATA
17.4 MAGNITUDE ESTIMATION EXPERIMENTS
17.5 DIRECT MODEL TESTS
17.6 COLORFULNESS IN PROJECTED IMAGES
17.7 MUNSELL IN COLOR APPEARANCE SPACES
17.8 CIE ACTIVITIES
17.9 A PICTORIAL REVIEW OF COLOR APPEARANCE MODELS
18 Traditional Colorimetric Applications
18.1 COLOR RENDERING
18.2 COLOR DIFFERENCES
18.3 INDICES OF METAMERISM
18.4 A GENERAL SYSTEM OF COLORIMETRY?
18.5 WHAT ABOUT OBSERVER METAMERISM?
19 Device-Independent Color Imaging
19.1 THE PROBLEM
19.2 LEVELS OF COLOR REPRODUCTION
19.3 A REVISED SET OF OBJECTIVES
19.4 GENERAL SOLUTION
19.5 DEVICE CALIBRATION AND CHARACTERIZATION
19.6 THE NEED FOR COLOR APPEARANCE MODELS
19.7 DEFINITION OF VIEWING CONDITIONS
19.8 VIEWING-CONDITIONS-INDEPENDENT COLOR SPACE
19.9 GAMUT MAPPING
19.10 COLOR PREFERENCES
19.11 INVERSE PROCESS
19.12 EXAMPLE SYSTEM
19.13 ICC IMPLEMENTATION
20 Image Appearance Modeling and the Future
20.1 FROM COLOR APPEARANCE TO IMAGE APPEARANCE
20.2 S-CIELAB
20.3 The iCAM FRAMEWORK
20.4 A MODULAR IMAGE DIFFERENCE MODEL
20.5 IMAGE APPEARANCE AND RENDERING APPLICATIONS
20.6 IMAGE DIFFERENCE AND QUALITY APPLICATIONS
20.7 iCAM06
20.8 ORTHOGONAL COLOR SPACE
20.9 FUTURE DIRECTIONS
21 High-Dynamic-Range Color Space
21.1 LUMINANCE DYNAMIC RANGE
21.2 THE HDR PHOTOGRAPHIC SURVEY
21.3 LIGHTNESS–BRIGHTNESS BEYOND DIFFUSE WHITE
21.4 hdr-CIELAB
21.5 hdr-IPT
21.6 EVANS, G0, AND BRILLIANCE
21.7 THE NAYATANI THEORETICAL COLOR SPACE
21.8 A NEW KIND OF APPEARANCE SPACE
21.9 FUTURE DIRECTIONS
References
Index
Wiley-IS&T Series in Imaging Science and Technology
Reproduction of Colour (Sixth Edition)
R. W. G. HuntColorimetry: Fundamentals and Applications
Noburu Ohta and Alan R. RobertsonColor Constancy
Marc EbnerColor Gamut Mapping
Ján MorovičPanoramic Imaging: Sensor-Line Cameras and Laser Range-Finders
Fay Huang, Reinhard Klette and Karsten ScheibeDigital Color Management (Second Edition)
Edward J. Giorgianni and Thomas E. MaddenThe JPEG 2000 Suite
Peter Schelkens, Athanassios Skodras and Touradj Ebrahimi (Eds.)Color Management: Understanding and Using ICC Profiles
Phil Green (Ed.)Fourier Methods in Imaging
Roger L. Easton, Jr.Measuring Colour (Fourth Edition)
R.W.G. Hunt and M.R. PointerThe Art and Science of HDR Imaging
John McCann and Alessandro RizziComputational Colour Science Using MATLAB (Second Edition)
Stephen Westland, Caterina Ripamonti and Vien CheungColor in Computer Vision: Fundamentals and Applications
Theo Gevers, Arjan Gijsenij, Joost van de Weijer and Jan-Mark GeusebroekColor Appearance Models (Third Edition)
Mark D. FairchildPublished in Association with the Society for Imaging Science and Technology
This edition first published 2013© 2013 John Wiley & Sons, Ltd
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Library of Congress Cataloging-in-Publication Data
Fairchild, Mark D. Color appearance models / Mark D. Fairchild. – Third edition. pages cm. – (The Wiley-IS&T series in imaging science and technology) Includes bibliographical references and index.
ISBN 978-1-119-96703-3 (hardback)
1. Color vision. I. Title. QP483.F35 2013 612.8′4–dc23
2013005445
A catalogue record for this book is available from the British Library.
ISBN: 9781119967033
To those that remind me that
a journey of a thousand miles begins with a single step:
Lisa, Acadia, Ellie.
And to all the other animals that fill our lives.
How much of beauty—of color as well as form—on which our eyes daily rest goes unperceived by us?
Henry David Thoreau
Series Preface
Color is a subject that has fascinated scientists, philosophers and people in general for thousands of years. Virtually all people are familiar with the concepts of color and are fluent in its semantic description. However numerical descriptions of color and its manipulation using mathematical models is a field familiar to relatively few scientists and engineers. Indeed, color has only had a well defined mathematical basis for less than 100 years. The field of colorimetry was the first comprehensive mathematical description of color. The use of colorimetry, however, is limited to a description of whether colors appear to match one another under a defined set of viewing conditions. Colorimetry has enabled the precise control of color in many industrial applications including textile and paint manufacture, printing, photography, cinema, lighting and television and displays. Colorimetry however cannot provide us with a numerical description of the relative appearance of colors or how the appearance of colors will change in different viewing environments. This is the domain of color appearance modeling.
Scientific interest in color appearance modeling began with the observation that colors could change their appearance depending on the background they were seen against, the intensity and spectral properties of the illumination and many other factors. We have all experienced these phenomena, often realized when the fabric or paint that looked so perfect in the showroom doesn’t quite look right when we bring it home. The first tentative steps towards a model of color appearance can be traced back to 1976 when the CIE introduced the CIELAB and CIELUV uniform color spaces. These color spaces allowed, for the first time, numerical correlates of lightness, hue, chroma and, in the case of CIELUV, saturation. Interest in color appearance modeling grew rapidly from this point. In the early 1980s leading color scientists including R.W.G. Hunt and Y. Nayatani developed the first versions of their color appearance models, designed to predict numerical correlates of all the perceptual attributes of color under a wide range of viewing conditions. Shortly after, Mark Fairchild developed the RLAB color appearance model. RLAB was a major advance in developing color appearance models for practical use in imaging applications. In 1997 the CIE proposed a simplified color appearance model, CIECAM97s, incorporating a number of features and approaches developed in the earlier models, as well as contributions by M.R. Luo and several others. Five years later CIECAM97s was succeeded by an improved, simpler and better model, CIECAM02. This model has now found widespread application in the imaging and printing industries, and an understanding of it is essential for scientists and engineers working in these areas.
The first two editions ofColor Appearance Models are among the most significant and indispensable texts in the field of color science. This is because the author, Mark Fairchild, provides a technically detailed and comprehensive approach to the subject of color modeling. Mark’s academicbackground, having postgraduate degrees in both imaging science and vision science, make him exceptionally qualified in this area. He has studied under, collaborated with and educated many of today’s leading color scientists. Consequently, his treatment of the subject provides all the necessary context and background required for a full understanding of color appearance models. The book provides an explanation of how color phenomena arise from the anatomy and physiology of the human visual system. It summarizes the methodologies, from the field of psychophysics, that allow us to obtain numerical measures of perceptual phenomena such as color. Colorimetry – the foundation of all color appearance models – is explained clearly and thoroughly. Fairchild provides descriptions and explanations of a very broad range of color appearance phenomena that are addressed by color appearance models. The book also takes us on a historical and technical journey, visiting each of the major advances in color appearance modeling in turn, until finally arriving at today’s most used model – CIECAM02. Fairchild provides a full technical explanation of all the major models as well as expert guidance on the strengths, weaknesses and uses of each model. The latter third of the book covers applications of color appearance modeling, with a strong focus on imaging science and technology. The third edition ofColor Appearance Models extends the treatment of applications to the field of high dynamic-range (HDR) imaging. This is one of the important new challenges in imaging science and photography. Color appearance models provide the important scientific insights required for development and refinement of new HDR technologies.
Mark Fairchild is counted among the world’s finest and most influential color imaging scientists and educators. His third edition ofColor Appearance Models is destined to become a classic color science textbook. The Society for Imaging Science and Technology (IS&T) and John Wiley & Sons Ltd are proud to be able to make this outstanding book available to students, scientists and engineers working in color related fields.
Geoffrey J. WoolfeCanon Information Systems Research AustraliaPublications Vice President, Society for Imaging Science and Technology
Preface
The law of proportion according to which the several colors are formed, even if a man knew he would be foolish in telling, for he could not give any necessary reason, nor indeed any tolerable or probable explanation of them.
Plato
Despite Plato’s warning, this book is about one of the major unresolved issues in the field of color science, the efforts that have been made toward its resolution, and the techniques that can be used to address currenttechnological problems. The issue is the prediction of the color appearanceexperienced by an observer when viewing stimuli in natural, complexsettings. Useful solutions to this problem have impacts in a number of industries such as lighting, materials, and imaging. In lighting, color appearance models can be used to predict the color rendering properties of various light sources allowing specification of quality rather than just efficiency. In materials fields (coatings, plastics, textiles,etc.), color appearance models can be used to specify tolerances across a wider variety of viewing conditions than is currently possible and to more accurately evaluate metamerism. The imaging industries have produced the biggest demand for accurate and practical color appearance models. The rapid growth in color imaging technology, particularly the desktop publishing and digital photography markets, has led to the emergence of color management systems. It is widely acknowledged that such systems require color appearance models to allow images originating in one medium and viewed in a particular environment to be acceptably reproduced in a second medium and viewed under different conditions. While the need for color appearance models is recognized, their development has been at the forefront of color science and largely confined to the discourse of academic journals and conferences. This book brings the fundamental issues and current solutions in the area of color appearancemodeling together in a single place for those needing to solve practicalproblems or looking for background for ongoing research projects.
Everyone knows what color is, but the accurate description and specification of colors is quite another story. In 1931, the Commission Internationale de l’Éclairage (CIE) recommended a system for color measurement establishing the basis for modern colorimetry. That system allows the specification of color matches through CIE XYZ tristimulus values. It was immediately recognized that more advanced techniques were required. The CIE recommended the CIELAB and CIELUV color spaces in 1976 to enable uniform international practice for the measurement of color differences and establishment of color tolerances. While the CIE system of colorimetry has been applied successfully for over 80 years, it is limited to the comparison of stimuli that are identical in every spatial and temporal respect and viewed under matched viewing conditions. CIE XYZ values describe whether or not two stimuli match. CIELAB values can be used to describe the perceived differences between stimuli in a single set of viewing conditions. Color appearance models extend the current CIE systems to allow the description of what color stimuli would look like under a variety of viewing conditions. The application of such models opens up a world of possibilities for the accurate specification, control, and reproduction of color.
Understanding color-appearance phenomena and developing models to predict them have been the topics of a great deal of research — particularly in the last 20–30 years. Color appearance remains a topic of much active research that is often being driven by technological requirements. Despite the fact that the CIE is not yet able to recommend a single color appearance model as the best available for all applications, there are many who need to implement some form of a model to solve their research, development, and engineering needs. One such application is the development of color management systems based on the International Color Consortium (ICC) Profile Format that continues to be developed by the ICC and incorporated into essentially all modern computer operating systems. Implementation of color management using ICC profiles requires the application of color appearance models with no specific instructions on how to do so. Unfortunately, the fundamental concepts, phenomena, and models of color appearance are not recorded in a single source. Generally, one interested in the field must search out the primary references across a century of scientific journals and conference proceedings. This is due to the large amount of active research in the area. While searching for and keeping track of primaryreferences is fine for those doing research on color appearance models, it should not be necessary for every scientist, engineer, and software developer interested in the field. The aim of this book is to provide the relevant information for an overview of color appearance and details of many of the most widely used models in a single source. The general approach has been to first provide an overview of the fundamentals of color measurement and the phenomena that necessitate the development of color appearance models. This eases the transition into the formulation of the various models and their applications that appear later in the book. This approach has proven quite useful in various university courses, short courses, and seminars in which the full range of material must be presented in a limited time.
Chapters 1 through 3 provide a review of the fundamental concepts of human color vision, psychophysics, and the CIE system of colorimetry that are prerequisite to understanding the development and implementation of color appearance models. Chapters 4 through 7 present the fundamental definitions, descriptions, and phenomena of color appearance. Thesechapters provide a review of the historical literature that has led to modern research and development of color appearance models. Chapters 8 and 9 concentrate on one of the most important component mechanisms of color appearance, chromatic adaptation. The models of chromatic adaptation described in Chapter 9 are the foundation of the color appearance models described in later chapters. Chapter 10 presents the definition of color appearance models and outlines their construction using the CIELAB color space as an example. Chapters 11 through 13 provide detailed descriptions of the Nayataniet al., Hunt, and RLAB color appearance models along with the advantages and disadvantages of each. Chapter 14 reviews the ATD and LLAB appearance models that are of increasing interest for some applications. Chapter 15 presents the CIECAM97s model established as a recommendation by the CIE just as the first edition of this book went to press (and included as an appendix in that edition). Also included is a description of the ZLAB simplification of CIECAM97s. Chapter 16 describes the recently formulated CIECAM02 model that represents a significant improvement of CIECAM97s and is the best possible model based on current knowledge. Chapters 17 and 18 describe tests of the various models through a variety of visual experiments and colorimetric applications of the models. Chapter 19 presents an overview of device-independent color imaging, the application that has provided the greatest technological push for the development of color appearance models. Finally, Chapters 20 and 21 introduce the concept of image appearance modeling as a potential future direction for color appearance modeling research, provide an overview of iCAM as one example of an image appearance model, and introduce new approaches to appearance prediction without color spaces and in high-dynamic-range environments.
While the field of color appearance modeling remains young and likely to continue developing in the near future, this book includes extensive material that will not change. Chapters 1 through 10 provide overviews of fundamental concepts, phenomena, and techniques that will change little, if at all, in the coming years. Thus, these chapters should serve as a steady reference. The models, tests, and applications described in the later chapters will continue to be subject to evolutionary changes as research progresses. However, these chapters do provide a useful snapshot of the current state of affairs and provide a basis from which it should be much easier to keep track of future developments. To assist readers in this task, a webpage has been set up (www.cis.rit.edu/Fairchild/CAM.html), which lists important developments and publications related to the material in this book. A spreadsheet with example calculations can also be found there.
‘Yes,’ I answered her last night;‘No,’ this morning sir, I say,Colors seen by candle-lightWill not look the same by day.
Elizabeth Barrett Browning
Acknowledgments
A project like this book is never really completed by a single author. I particularly thank my family for the undying support that encouraged completion of this work. The research and learning that led to this book is directly attributable to my students. Much of the research would not have been completed without their tireless work, and I would not have learned about color appearance models were it not for their keen desire to learn more and more about them from me. I am deeply indebted to all of my students and friends — those that have done research with me, those working at various times at the Rochester Institute of Technology (RIT), and those that have participated in my university and short courses at all levels. There is no way to list all of them without making an omission, so I will take the easy way out and thank them as a group. I am also indebted to those that reviewed various chapters while the first edition of this book was being prepared and provided useful insights, suggestions, and criticisms as well as those who helped with revisions of the later editions. Thank you to Addison-Wesley for convincing me to write the first edition and then publishing it and to IS&T, the Society for Imaging Science and Technology, for having the vision to publish the second and third editions with Wiley. It has been a joy to work with all of the IS&T staff throughout my color imaging career. Thanks to all of the industrial and government sponsors of our research and education at RIT that lead to many of the results and analyses presented in this volume. I have been fortunate to work with a fascinating variety of students, staff, and faculty colleagues over the years; this edition would not have been possible without them.
M.D.F.Honeoye Falls, NY
Ye’ll come away from the links with a new hold on life, that is certain if ye play the game with all yer heart.
Michael Murphy, Golf in the Kingdom
Introduction
Standing before it, it has no beginning;even when followed, it has no end.In the now, it exists; to the present apply it,follow it well, and reach its beginning.
Tao Te Ching, 300–600 BCE
Like beauty, color is in the eye of the beholder. For as long as human scientific inquiry has been recorded, the nature of color perception has been a topic of great interest. Despite tremendous evolution of technology, fundamental issues of color perception remain unanswered. Many scientific attempts to explain color rely purely on the physical nature of light and objects. However, without the human observer there is no color. It is often asked whether a tree falling in the forest makes a sound if no one is there to observe it. Perhaps equal philosophical energy should be spent wondering what color its leaves are.
You can observe a lot by just watching.
Yogi Bera
WHAT IS A COLOR APPEARANCE MODEL?
It is common to say that certain wavelengths of light, or certain objects, are a given color. This is an attempt to relegate color to the purely physical domain. Instead it is proper to state that those stimuli are perceived to be of a certain color when viewed under specified conditions. Attempts to specify color as a purely physical phenomenon fall within the domain of spectrophotometry and spectroradiometry. When the lowest-level sensory responses of an average human observer are factored in, the domain of colorimetry has been entered. When the many other variables that influence color perception are considered, in order to better describe our perceptions of stimuli, one is within the domain of color appearance modeling — the subject of this book.
Consider the following observations:
The headlights of an oncoming automobile are nearly blinding at night, but barely noticeable during the day.
As light grows dim, colors fade from view while objects remain readily apparent.
Stars disappear from sight during the daytime.
The walls of a freshly painted room appear significantly different from the color of the sample that was used to select the paint in a hardware store.
Artwork displayed in different color mat board takes on a significantly different appearance.
Printouts of images do not match the originals on a self-luminous display (
e.g
., computer monitor, tablet, smart phone, television).
Scenes appear more colorful and of higher contrast on a sunny day than on an overcast day.
Blue and green objects (
i.e
., board-game pieces) become indistinguishable under dim incandescent illumination.
It is nearly impossible to select appropriate socks (
e.g
., black, brown, or blue) in the early morning light.
There is no such thing as a gray, or brown, lightbulb.
There are no colors described as reddish-green or yellowish-blue.
None of the above observations can be explained by physical measurements of materials and/or illumination alone. Rather, such physical measurements must be combined with other measurements of the prevailing viewing conditions and models of human visual perception in order to make reasonable predictions of these effects. This aggregate is precisely the task that color appearance models are designed to manage. Each of the observations outlined above, and many more like them, can be explained as instances of various color appearance phenomena and predicted by color appearance models. They cannot be explained by the established techniques of color measurement, sometimes referred to as basic colorimetry. Hutchings (1999), in the first chapter of his book on food color and appearance, provides a delightful review of the complexities of specifying the appearance of stimuli that all enjoy perceiving. This book details the differences between basic colorimetry and color appearance models, provides fundamental background on human visual perception and color appearance phenomena, and describes the application of color appearance models to current technological problems such as digital color reproduction. Upon completion of this book, a reader should be able to fairly easily explain the causes of, if not the physiological mechanisms for, each of the appearance phenomena listed above. Fairchild (2011a) and <whyiscolor.org> provide an introductory and inquisitive look at the fundamental questions of color appearance and color science from the perspectives of students ranging from pre-school to graduate school.
Basic colorimetry provides the fundamental color measurement techniques that are used to specify stimuli in terms of their sensory impact for an average human observer. These techniques are absolutely necessary as the foundation for color appearance models. However, on their own, the techniques of basic colorimetry can only be used to specify whether or not two stimuli, viewed under identical conditions, match in color for an average observer. Advanced colorimetry aims to extend the techniques of basic colorimetry to enable the specification of color difference perceptions and ultimately color appearance. There are several established techniques for color difference specification that have been formulated and refined over the past several decades. These techniques have also reached the point that a few, agreed upon, standards are used throughout the world while research continues to fine-tune, improve, and extend them. Color appearance models aim to go the final step. This would allow the mathematical description of the appearance of stimuli in a wide variety of viewing conditions. Such models have been the subject of much research in the late twentieth and early twenty-first centuries and have become required for practical applications. There are a variety of models that have been proposed. These models have found their way into color imaging systems through the refinement and extension of color management techniques. Techniques derived from color appearance models are even found in the image capture and display algorithms of popular smart phones. Such applications require an ever-broadening array of scientists, engineers, programmers, imaging specialists, and others to understand the fundamental philosophy, construction, and capabilities of color appearance models as described in the ensuing chapters.
Learning is best accomplished with positive feedback to assure that new ideas are assimilated and replace pre-existing misunderstandings. As such, and so as not to make the learning process too difficult, here are some clues to the explanations of the color appearance observations listed near the beginning of this introduction.
The change of appearance of oncoming headlights can be largely explained by the processes of light adaptation and described by Weber’s law.
The fading of color in dim light while objects remain clearly visible is explained by the transition from trichromatic cone vision to monochromatic rod vision.
The incremental illumination of a star on the daytime sky is not large enough to be detected, while the same physical increment on the darker nighttime sky is easily perceived, because the visual threshold to luminance increments has changed between the two viewing conditions.
The paint chip does not match the wall due to changes in the size, surround, and illumination of the stimulus and due to inter-reflections among adjacent walls that serves to increase perceived saturation.
Changes in the color of a surround or background profoundly influence the appearance of stimuli. This can be particularly striking for photographs and other artwork.
Assuming the display and printer are accurately calibrated and characterized, differences in media, white point, luminance level, image size, and surround can still force the printed image to look significantly different from the original.
The Hunt effect and Stevens effect describe the apparent increase in colorfulness and contrast of scenes with increases in illumination level.
Low levels of incandescent illumination do not provide the energy required by the short-wavelength sensitive mechanisms of the human visual system (the least sensitive of the color mechanisms) to distinguish green objects from blue objects.
In the dim early morning light, the ability to distinguish dark colors is diminished.
The perceptions of gray and brown only occur as related colors, thus they cannot be observed as light sources that are normally the brightest element of a scene.
The hue perceptions red and green (or yellow and blue) are encoded in a bipolar fashion by our visual system and thus cannot exist together.
Given those clues, it is time to read on and further unlock the mysteries of color appearance. All of the topics in these examples are explored in more detail, and from various perspectives, throughout the text.
1
Human Color Vision
Color appearance models aim to extend basic colorimetry to specify the perceived color of stimuli in a wide variety of viewing conditions. To fully appreciate the formulation, implementation, and application of color appearance models, several fundamental topics in color science must first be understood. These are the topics of the first few chapters of this book. Since color appearance represents several of the dimensions of our visual experience, any system designed to predict correlates to these experiences must be based, to some degree, on the form and function of the human visual system. All of the color appearance models described in this book are derived with human visual function in mind, although most also include some empirical modeling of the visual system as a “black box.” It becomes much simpler to understand the formulations of the various models if basic visual anatomy, physiology, and performance of the visual system are understood. Thus, this book begins with a treatment of the human visual system.
As necessitated by the limited scope available in a single chapter, this treatment of the visual system is an overview of the topics most important for an appreciation of color appearance modeling. The field of vision science is immense, complex, and fascinating. Readers are encouraged to explore the literature and the many useful texts with differing perspectives on human vision in order to gain further insight and details. Of particular note are the review paper on the mechanisms of color vision by Lennie and D’Zmura (1988), the text on human color vision by Kaiser and Boynton (1996), the more general text on the foundations of vision by Wandell (1995), the comprehensive treatment by Palmer (1999), and edited collections on color vision by Backhaus et al. (1998) and Gegenfurtner and Sharpe (1999). Other interesting and more recent texts on vision include the extensive and complete volume by Chalupa and Werner (2004), the revision of Dowling’s (2012) classic on the retina, Livingstone’s (2002) interesting treatment of the relationships between art and biology of seeing, Mausfeld and Heyer’s (2003) book focused on perception, Schwab’s (2012) discussion of the evolution of vision, and Valberg’s (2005) revised edition covering all of vision, but with some more focus on color. General texts on sensation and perception, such as Wolfe et al. (2012), are also excellent sources for learning fundamental aspects of the human visual system. Johnsen (2012) provides a slightly different perspective on visual systems and other optical phenomena in nature. The material that is briefly summarized in this chapter is treated in more detail in those references.
1.1 OPTICS OF THE EYE
Our visual perceptions are initiated and strongly influenced by the anatomical structure of the eye. Figure 1.1 shows a schematic representation of the optical structure of the human eye with some key features labeled. The human eye can be thought of as acting like a camera. The cornea and lens act together like a camera lens to focus an image of the visual world on the retina at the back of the eye, which acts like the image sensor (e.g., CCD) of a camera. These and other structures have a significant impact on our perception of color.
Figure 1.1 Schematic diagram of the human eye with some key structures labeled
The Cornea
The cornea is the transparent outer surface of the front of the eye through which light passes. It serves as the most significant image-forming element of the eye since its curved surface at the interface with air represents the largest change in index of refraction within the eye’s optical system. The cornea is avascular, receiving its nutrients from marginal blood vessels and the fluids surrounding it. Refractive errors, such as nearsightedness (myopia), farsightedness (hyperopia), or astigmatism, can be attributed to variations in the shape of the cornea with respect to the location and the shape of the retina. These refractive errors are sometimes corrected with laser surgery to reshape the cornea.
The Lens
The lens serves the function of accommodation. It is a layered, flexible structure that varies in index of refraction. It is a naturally occurring gradient-index optical element with the index of refraction higher in the center of the lens than at the edges. This feature serves to reduce some of the aberrations that might normally be present in a simple optical system.
The shape of the lens is controlled by the ciliary muscles. When we gaze at a nearby object, the lens becomes “fatter” and thus has increased optical power to allow us to focus on the near object. When we gaze at a distant object, the lens becomes “flatter” resulting in the decreased optical power required to bring more distant objects into sharp focus. As we age, the internal structure of the lens changes, resulting in a loss of flexibility. Generally, when the age of about 45–50 years is reached, the lens has completely lost its flexibility and observers can no longer focus on near objects (this is called presbyopia, or “old eye”). It is at this point that most people must resort to reading glasses or bifocals.
Concurrent with the hardening of the lens is an increase in its optical density. The lens absorbs and scatters short-wavelength (blue and violet) energy. As it hardens, the level of this absorption and scattering increases. In other words, the lens becomes more and more yellow with age. Various mechanisms of chromatic adaptation generally make us unaware of these gradual changes. However, we are all looking at the world through a yellow filter that not only changes with age, but is significantly different from observer to observer. The effects are most noticeable when performing critical color matching or comparing metameric color matches with other observers. The effect is particularly apparent with purple objects and nearly monochromatic stimuli such as the primaries of wide-gamut displays. Since an older lens absorbs most of the blue energy reflected from a purple object but does not affect the reflected red energy, older observers will tend to report that the object is significantly more red than reported by younger observers. Important issues regarding the characteristics of lens aging and its influence on visual performance are discussed by Pokorny et al. (1987), Werner and Schefrin (1993), and Schefrin and Werner (1993) and in the Commission Internationale de l’Éclairage (CIE) (2006) report on physiological color matching functions.
The Humors
The volume between the cornea and the lens is filled with aqueous humor, which is essentially water. The region between the lens and the retina is filled with vitreous humor, which is also a fluid, but with a higher viscosity similar to that of gelatin. Both humors exist in a state of slightly elevated pressure (relative to air pressure) to assure that the flexible eyeball retains its shape and dimensions in order to avoid the deleterious effects of wavering retinal images. The flexibility of the entire eyeball serves to increase its resistance to injury. It is much more difficult to break a structure that gives way under impact than one of equal “strength” that attempts to remain rigid. Since the indices of refraction of the humors are roughly equal to that of water, and those of the cornea and lens are only slightly higher, the rear surface of the cornea and the entire lens have relatively little optical power (in comparison with the front surface of the cornea).
The Iris
The iris is the sphincter muscle that controls pupil size. The iris is pigmented, giving each of us our specific eye color. Eye color is determined by the concentration and distribution of melanin within the iris. The pupil, which is the hole in the middle of the iris through which light passes, defines the level of illumination on the retina. Pupil size is largely determined by the overall level of illumination, but it is important to note that it can also vary with nonvisual phenomena such as arousal. (This effect can be observed by enticingly shaking a toy in front of a cat and paying attention to its pupils.) Thus it is difficult to accurately predict pupil size from the prevailing illumination. In practical situations, pupil diameter varies from about 3 to 7 mm. This change in pupil diameter results in approximately a five-fold change in pupil area and therefore retinal illuminance. The visual sensitivity change with pupil area is further limited by the fact that marginal rays are less effective at stimulating visual response in the cones than central rays (the Stiles–Crawford effect). The change in pupil diameter alone is not sufficient to explain excellent human visual function over prevailing illuminance levels that can vary over 10 orders of magnitude or more.
The Retina
The optical image formed by the eye is projected onto the retina. The retina is a thin layer of cells, approximately the thickness of tissue paper, located at the back of the eye and incorporating the visual system’s photosensitive cells and initial signal processing and transmission “circuitry.” These cells are neurons, part of the central nervous system, and can appropriately be considered a part of the brain. The photoreceptors, rods and cones, serve to transduce the information present in the optical image into chemical and electrical signals that can be transmitted to the later stages of the visual system. These signals are then processed by a network of cells and transmitted to the brain through the optic nerve. More detail on the retina is presented in “The retina.”
Behind the retina is a layer known as the pigmented epithelium. This dark pigment layer serves to absorb any light that happens to pass through the retina without being absorbed by the photoreceptors. The function of the pigmented epithelium is to prevent light from being scattered back through the retina, thus reducing the sharpness and contrast of the perceived image. Nocturnal animals give up this improved image quality in exchange for a highly reflective tapetum that reflects the light back in order to provide a second chance for the photoreceptors to absorb the energy. This is why the eyes of a deer, or other nocturnal animal, caught in the headlights of an oncoming automobile appear to glow. They are acting like very efficient retro-reflectors by focusing the light from the car they are looking at through the animal’s eyes and right back to the car itself.
The Fovea
Perhaps the most important structural area on the retina is the fovea. The fovea is the area on the retina where we have the best spatial and color vision. When we look at, or fixate, an object in our visual field, we move our head and eyes such that the image of the object falls on the fovea. As you are reading this text, you are moving your eyes to make the various words fall on your fovea as you read them. To illustrate how drastically spatial acuity falls off as the stimulus moves away from the fovea, try to read the preceding text in this paragraph while fixating on the period at the end of this sentence. It is probably difficult, if not impossible, to read the text that is only a few lines away from the point of fixation. The fovea covers an area that subtends about 2° of visual angle in the central field of vision. To visualize 2° of visual angle, a general rule is that the width of your thumbnail, held at arm’s length, is approximately 1° of visual angle. (Also, the moon and sun each subtend almost exactly 0.5° of visual angle in the sky, an interesting coincidence that enhances the possibility of the Earth having both complete lunar and solar eclipses.)
The Macula
The fovea is also protected by a yellow filter known as the macula. The macula serves to protect this critical area of the retina from intense exposures to short-wavelength energy. It might also serve to reduce the effects of chromatic aberration that cause the short-wavelength image to be rather severely out of focus most of the time. Unlike the lens, the macula does not become more yellow with age. However, there are significant differences in the optical density of the macular pigment from observer to observer and in some cases between a single observer’s left and right eyes. The yellow filters of the lens and macula, through which we all view the world, are the major source of variability in color vision between observers with normal color vision.
The Optic Nerve
A last key structure of the eye is the optic nerve. The optic nerve is made up of the axons (outputs) of the ganglion cells, the last level of neural processing in the retina. It is interesting to note that the optic nerve is made up of approximately one million fibers carrying information generated by approximately 130 million photoreceptors. Thus there is a clear compression of the visual signal prior to transmission to higher levels of the visual system. A one-to-one “pixel map” of the visual stimulus is never available for processing by the brain’s higher visual mechanisms. This processing is explored in greater detail below. Since the optic nerve takes up all of the space that would normally be populated by photoreceptors, there is a small area in each eye in which no visual stimulation can occur. This area is known as the blind spot.
The structures described above have a clear impact in shaping and defining the information available to the visual system that ultimately results in the perception of color appearance. The action of the pupil serves to define retinal illuminance levels that, in turn, have a dramatic impact on color appearance. The yellow-filtering effects of the lens and macula modulate the spectral responsivity of our visual system and introduce significant inter-observer variability. The spatial structure of the retina serves to help define the extent and nature of various visual fields that are critical for defining color appearance. The neural networks in the retina reiterate that visual perception in general, and specifically color appearance, cannot be treated as simple point-wise image processing problems. Several of these important features are discussed in more detail in the following sections on the retina, visual physiology, and visual performance.
1.2 THE RETINA
Figure 1.2 illustrates a cross-sectional representation of the retina. The retina includes several layers of neural cells beginning with the photoreceptors, the rods and cones. A vertical signal processing chain through the retina can be constructed by examining the connections of photoreceptors to bipolar cells, which are in turn connected to ganglion cells, which form the optic nerve. Even this simple pathway results in the signals from multiple photoreceptors being compared and combined. This is because multiple photoreceptors provide input to many of the bipolar cells, and multiple bipolar cells provide input to many of the ganglion cells. More importantly, this simple concept of retinal signal processing ignores two other significant types of cells. These are the horizontal cells, which connect photoreceptors and bipolar cells laterally to one another, and the amacrine cells, which connect bipolar cells and ganglion cells laterally to one another. Figure 1.2 provides only a slight indication of the extent of these various interconnections.
Figure 1.2 Schematic diagram of the “wiring” of cells in the human retina
The specific processing that occurs in each type of cell is not completely understood and is beyond the scope of this chapter. However, it is important to realize that the signals transmitted from the retina to the higher levels of the brain via the ganglion cells are not simple point-wise representations of the receptor signals, but rather consist of sophisticated combinations of the receptor signals. To envision the complexity of the retinal processing, keep in mind that each synapse between neural cells can effectively perform a mathematical operation (add, subtract, multiply, and divide) in addition to the amplification, gain control, and nonlinearities that can occur within the neural cells. Thus the network of cells within the retina can serve as a sophisticated image computer. This is how the information from 130 million photoreceptors can be reduced to signals in approximately one million ganglion cells without loss of visually meaningful data.
It is interesting to note that light passes through all of the neural machinery of the retina prior to reaching the photoreceptors. This has little impact on visual performance since these cells are largely transparent and in fixed position, thus not perceived. It also allows the significant amounts of nutrients required, and waste produced, by the photoreceptors to be processed through the back of the eye.
Rods and Cones
Figure 1.3 provides a representation of the two classes of retinal photoreceptors: rods and cones. Rods and cones derive their respective names from their prototypical shape. Rods tend to be long and slender while peripheral cones are conical. This distinction is misleading since foveal cones, which are tightly packed due to their high density in the fovea, are long and slender, resembling peripheral rods.
The more important distinction between rods and cones is in visual function. Rods serve vision at low luminance levels (e.g., less than 1 cd/m2) while cones serve vision at higher luminance levels. Thus the transition from rod to cone vision is one mechanism that allows our visual system to function over a large range of luminance levels. At high luminance levels (e.g., greater than 100 cd/m2), the rods are effectively saturated and only the cones function. In the intermediate luminance levels, both rods and cones function and contribute to vision. Vision when only rods are active is referred to as scotopic vision. Vision served only by cones is referred to as photopic vision, and the term mesopic vision is used to refer to vision in which both rods and cones are active at intermediate luminance levels.
Figure 1.3 Illustrations of prototypical rod and cone photoreceptors
Rods and cones also differ substantially in their spectral sensitivities as illustrated in Figure 1.4(a). There is only one type of rod receptor with a peak spectral responsivity at approximately 510 nm. There are three types of cone receptors with peak spectral responsivities spaced through the visual spectrum.
The three types of cones are most properly referred to as L, M, and S cones. These names refer to the long-wavelength, middle-wavelength, and short-wavelength sensitive cones, respectively. Sometimes the cones are denoted with other symbols such as RGB or ργβ suggestive of red, green, and blue sensitivities. As can be seen in Figure 1.4(a) this concept can be misleading, and the LMS names are more appropriately descriptive. Note that the spectral responsivities of the three cone types are broadly overlapping, a design that is significantly different from the “color separation” responsivities that are often built into physical imaging systems. Such non-overlapping sensitivities, often incorporated in imaging systems for practical reasons, are the fundamental reason that accurate color reproduction is often difficult, if not impossible, to achieve.
The three types of cones clearly serve color vision. Since there is only one type of rod, the rod system is incapable of color vision. This can easily be observed by viewing a normally colorful scene at very low luminance levels. Figure 1.4(b) illustrates the two CIE spectral luminous efficiency functions, the V ′(λ) function for scotopic (rod) vision and the V(λ) function for photopic (cone) vision. These functions represent the overall sensitivity of the two systems with respect to the perceived brightness of the various wavelengths. Since there is only one type of rod, the V ′(λ) function is identical to the spectral responsivity of the rods and depends on the spectral absorption of rhodopsin, the photosensitive pigment in rods. The V(λ) function, however, represents a combination of the three types of cone signals rather than the responsivity of any single cone type.
Figure 1.4 (a) Spectral responsivities of the L, M, and S cones and (b) the CIE spectral luminous efficiency functions for scotopic, V´(λ), and photopic, V(λ), vision
Note the difference in peak spectral sensitivity between scotopic and photopic vision. With scotopic vision we are more sensitive to shorter wavelengths. This effect, known as the Purkinje shift, can be observed by finding two objects, one blue and the other red, that appear the same lightness when viewed in daylight. When the same two objects are viewed under very low luminance levels, the blue object will appear quite light while the red object will appear nearly black because of the scotopic spectral sensitivity function’s sensitivity to blue energy and almost complete lack of sensitivity to red energy.
Another important feature about the three cone types is their relative distribution in the retina. It turns out that the S cones are relatively sparsely populated throughout the retina and completely absent in the most central area of the fovea. There are far more L and M cones than S cones, and there are approximately twice as many L cones as M cones. The relative populations of the L : M : S cones are approximately 40 : 20 : 1. These relative populations must be considered when combining the cone responses (plotted with individual normalizations in Figure 1.4(a)) to predict higher-level visual responses. Figure 1.5 provides a schematic representation of the foveal photoreceptor mosaic with completely inaccurate and false coloring to represent a hypothetical distribution with the L cones in red, M cones in green, and S cones in blue. Figure 1.5 is presented simply as a convenient visual representation of the cone populations and should not be taken literally.
As illustrated in Figure 1.5, there are no rods present in the fovea. This feature of the visual system can also be observed when trying to look directly at a small dimly illuminated object, such as a faint star at night. It disappears since its image falls on the foveal area where there are no rods to detect the dim stimulus. Figure 1.6 shows the distribution of rods and cones across the retina. Several important features of the retina can be observed in Figure 1.6. First, note the extremely large numbers of photoreceptors. In some retinal regions, there are about 150 000 photoreceptors per square millimeter of retina! Also note that there are far more rods (around 120 million per retina) than cones (around 7 million per retina). This might seem somewhat counterintuitive since cones function at high luminance levels and produce high visual acuity while rods function at low luminance levels and produce significantly reduced visual acuity (analogous to low-speed fine-grain photographic film vs a high-speed coarse-grain film). The solution to this apparent mystery lies in the fact that single cones feed into ganglion cell signals while rods pool their responses over hundreds of receptors (feeding into a single ganglion cell) in order to produce increased sensitivity at the expense of acuity. This also partially explains how the information from so many receptors can be transmitted through one million ganglion cells. Figure 1.6 also illustrates that cone receptors are highly concentrated in the fovea and more sparsely populated throughout the peripheral retina while there are no rods in the central fovea. The lack of rods in the central fovea allows that valuable space to be used to produce the highest possible spatial acuity with the cone system. A final feature to be noted in Figure 1.6 is the blind spot. This is the area, 12–15° from the fovea, where the optic nerve is formed and there is no room for photoreceptors.
Figure 1.7 provides some stimuli that can be used to demonstrate the existence of the blind spot. One reason the blind spot generally goes unnoticed is that it is located on opposite sides of the visual field in each of the two eyes. However, even when one eye is closed, the blind spot is not generally visible. To observe your blind spot, close your left eye and fixate the cross in Figure 1.7(a) with your right eye. Then adjust the viewing distance of the book until the spot to the right of the cross disappears when it falls on the blind spot. Note that what you see when the spot disappears is not a black region. Rather it appears to be an area of blank paper. This is an example of a phenomenon known as filling-in. Since your brain no longer has any signal indicating a change in the visual stimulus at that location, it simply fills in the most probable stimulus, in this case a uniform white of the paper. The strength of this filling-in can be illustrated by using Figure 1.7(b) to probe your blind spot. In this case, with your left eye closed, fixate the cross with your right eye and adjust the viewing distance until the gap in the line disappears when it falls on your blind spot. Amazingly the perception is that of a continuous line since that is now the most probable visual stimulus. If you prefer to perform these exercises using your left eye, simply turn the book upside down to find the blind spot on the other side of your visual field.
Figure 1.5 (a) A representation of the retinal photoreceptor mosaic artificially colored to represent the relative proportions of L (colored red), M (green), and S (blue) cones in the human retina. Modeled after Williamset al. (1991). (b) The same representation for a hypothetical deuteranope whose M cones contain L-cone photopigments (or whose M cones have been replaced with L cones)
Figure 1.6 Density (receptors per square millimeter) of rod and cone photoreceptors as a function of location on the human retina
Figure 1.7 Stimuli used to illustrate the presence of the blind spot and “filling-in” phenomena. Close your left eye. Fixate the cross with your right eye and adjust the viewing distance until (a) the spot falls on your blind spot or (b) the gap in the line falls on your blind spot. Note the perception in that area in each case
The filling-in phenomenon goes a long way to explain the function of the visual system. The signals present in the ganglion cells represent only local changes in the visual stimulus. Effectively, only information about spatial or temporal transitions (i.e., edges) is transmitted to the brain. Perceptually this code is sorted out by examining the nature of the changes and filling-in the appropriate uniform perception until a new transition is signaled. This coding provides tremendous savings in bandwidth to transmit the signal and can be thought of as somewhat similar to run-length encoding that is sometimes used in digital imaging.
Intrinsically Photosensitive Retinal Ganglion Cells
Within the past decade, the properties and roles of intrinsically photosensitive retinal ganglion cells (ipGRC) are beginning to be understood. These are ganglion cells that are directly photosensitive due to a unique photopigment within their cellular structure, known as melanopsin. Thus, rather than simply transmitting the signals from rod and cone photoreceptors, these ganglion cells transmit photo-signals produced intrinsically. The spectral responsivity of ipRGCs is quite broad and peaks at roughly 480 nm, between the S-cones (approximately 440 nm) and rods (approximately 505 nm), with a width similar to the V(λ) function. Thus, ipRGCs represent a third class of photoreceptors in the retina and one that could have an interesting impact on how color appearance and chromatic adaptation are studied and modeled.
These cells have been implicated in a number of visual functions including modulation of circadian rhythms (Rea 2011), control of pupilary response, visual responses, and adaptation. The impacts of modification of these responses can include ailments such as seasonal affective disorder, obesity, cancer, and respiratory illness. Clearly ipRGCs are important to our welfare and visual performance and it remains to be seen how their understanding might improve our ability to predict color appearance.
