BOOK REVIEWS

Digital Color Imaging Handbook, edited by GauravSharma. CRC Press, Boca Raton, FL, USA, 2003. 797pp., ISBN 0–8493-0900-X Hardback $125

This is a hefty book at 797 pages and is part of the CRCSeries in Electrical Engineering and Applied Signal Pro-cessing. Those of us are most familiar with the CRC’sHandbook of Chemistry and Physics. For any reader thatdoes not have this book currently sagging a bookshelf, it isan enormous tome filled with facts, physical constants, andpounds of elemental and compound minutiae. Although thebook under review is indeed quite large and is referred to asa handbook, it is a substantially different kind of book, andits intent is to provide the scientific background applicableto the many facets of digital color imaging in a narrativeform. It is not a “how-to” manual that guides you throughthe process of successfully using state-of-the-art commer-cial color and imaging technology hardware and softwarepackages.

Since volumes have been written on the majority of thechapter topics, I think that the most realistic expectation onecan have of such a book is that it provides a well-thought-out mix of topics with a good distillation of the material andample references. Most of the chapters provide a goodoverview of their respective topics and supply extensivecitations to additional relevant material.

Chapter 1 was written by Gaurav Sharma and is titled“Color Fundamentals for Digital Imaging.” At 114 pages,this is a significant chunk of the book and, in my opinion,this was probably the most difficult chapter to compose.This is because, first, there is a lot of material out there onbasic color science and, second, such a lead-off chaptermust really try to pull together the overall aims and philos-ophy of the book as a whole. This chapter does a good jobat giving an overview of the underpinnings of color sciencerelevant to digital imaging and provides 347 references forthe reader to delve deeper into the various topics. It providesdetail in areas such as current CIE color spaces and currentCIE-based color difference metrics. Most of the material iscovered with wide brushstrokes in an overview style. Itprovides a basic introduction to material in subsequentchapters.

Chapter 2, “Visual Psychophysics and Color Appear-ance,” was written by Garrett Johnson and Mark Fairchild.This chapter does a laudable job at distilling the areas ofbasic visual psychophysics, color appearance phenomenon,and chromatic adaptation. It provides a good level of cov-erage of those topics in a readable style. In addition, there isalso a good review of general terminology as well as thatspecific to viewing conditions. The review of color appear-ance models touches very briefly on the history surroundingtheir development and provides greater detail on CIELAB

and CIECAM97, along with a brief discussion of ongoingwork in this area.

Chapter 3, written by Patrick Emmel, is entitled “PhysicalModels for Color Prediction” is based on his Ph.D. thesis.The beginning of the chapter discusses basic principles ofreflection and refraction, light absorption, scattering, andfluorescence as well as facets of models such as radiativetransfer, Kubelka–Munk (two-flux), and the Saundersoncorrection as it applies to surface phenomena. This chapterthen goes on to discuss a generalized approach that modelsreflective surfaces using a three-matrix correction that in-corporates facets of the three main models listed above(light scattering, Kubelka–Munk, and the Saunderson cor-rection). The author discusses the better predictive power ofsuch a combinatorial model, which incorporates a greaternumber of physical phenomena, with the trade-off of com-putational time. This chapter is a bit “heavier” than most ofthe others in terms of its technical content.

Chapter 4 is titled “Color Management for Digital Imag-ing Systems” and was written by Edward Giorgianni,Thomas Madden, and Kevin Spaulding. Given the contentof this chapter, a better title would have been “An EncodingSpecification for Digital Color Management.” The authorsprimarily discuss one bimodal encoding paradigm (RIMM/ROMM). They do a good job of elucidating it, but I ex-pected a lot more from the titled chapter. The content is anexpanded version of a paper written by two of the authorsfor a 2000 IS&T PICS conference. I envision such a chapteras providing a great deal of glue, if you will, that binds a lotof the other material in the book (e.g., characterization,calibration, gamut mapping, rendering intents, and encod-ing) into context. Even if the authors did want to focussolely on encoding paradigms, other encoding approachesare given no significant discussion. For example, despite thefact that the authors mention that it can work well withincertain applications, the sRGB space is mentioned onlyonce. A more balanced review is warranted. There arepapers, books, and standards that discuss the many aspectsof workflow, color management elements, and architecture.The end of Chapter 1 tacks on some basic review that reallyshould have been elaborated on more fully in this chapter,given its title. Even if these aspects were not discussed ingreat detail, a more thorough review of the title topic withconsiderably more references would have been preferable.

Written by Raja Bala, Chapter 5 details “Device charac-terization” and weighs in at 110 pages. This is another largechunk of the book that reviews a significant amount ofmaterial. The chapter provides a review of the basic conceptsand techniques of model-based and empirical device cali-bration and characterization as well as various methods forinterpolation and data fitting. The bulk of the chapter is thenfilled out with calibration and characterization approaches

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for scanners, digital cameras, CRT and liquid crystal dis-plays, as well as printers. In the case of print devices, thereis discussion of the aforementioned model-based and em-pirical characterizations, hybrid approaches, inverse charac-terization, as well as characterizations that use scanners andthose for high-fidelity printing. The chapter provides exten-sive references as well as cross-references to other chapters,which helps put the topic into context. It also underscoresthe author’s message that characterization does not stand onits own as the single entity that guarantees successful qual-ity image results but it is an essential part of the entire imagereproduction chain.

Chapter 6, “Digital Color Halftones,” was written byCharles Haines, Shen-Ge Wang, and Keith Knox. This isanother sizeable chapter, and it does a very good job ofproviding a balanced review of and insight into the mechan-ics and considerations surrounding the use of digital half-toning. I particularly liked this chapter’s instructional style.Its methodical approach to evaluating the overall mechanicsof the technology along with the inherent trade-offs associ-ated with the various components help the reader build anunderstanding of the topic. There is a lot of practical insightprovided; for example, how do orientation and registryeffect color gamut? This is particularly helpful for one whois somewhat new to the technology, but also provides agood concise reference for the reader with more experiencein the area. In one section, the user is guided in cookbookfashion through the steps of the decision process for select-ing digital halftone screens for a new printer. These includeelements such as overall printer characteristics, appropriatehalftone methodology, structure, and size, as well as theconsideration of UCR/GCR (undercolor removal/gray com-ponent replacement) strategies. In the narrative, the authorsalso give insight on how to extend the process beyondxerographic printers to such devices as LED arrays andinkjet technologies. In addition to extensive references, thetext also provides a substantial section of recommendedreadings.

Written by A. Ufuk Agar, Farhan Baqai, and Jan Alle-bach, Chapter 7, “Human Visual Model-Based Color Half-toning,” expands on the previous chapter to elucidate themanners in which a human visual system (HSV) can befactored into the imaging chain and used to create moreeffective image-processing algorithms. The aforementionedimaging chain includes the digital color halftone image, thephysical printing technology, and the HVS. This chapterfocuses on the latter two facets of the chain. Both hardcopyprinting models and human visual system models are dis-cussed in context. The reader who wants additional in-depthtreatment is referred to other chapters in the book. Of thethree principal types of halftoning algorithm approaches,namely pointwise (e.g., dithering), neighboring (e.g., errordiffusion), and iterative, the latter method is the focusbecause of the flexibility and power with which such meth-ods can exploit key aspects of both hardcopy and HSVmodels. Two halftoning design tasks are elucidated. Thefirst is dispersed-dot, which utilizes a color direct binarysearch (CDBS) methodology. The second is a clustered-dot

periodic screen using a stepwise approach that incorporateslattice theory, color device models, and an HSV. The au-thors also briefly discuss computational limitations, keyareas of opportunity, and the future of such techniques.

Chapter 8, “Compression of Color Images,” by RicardoQueiroz, gives a very basic review of compression by ex-amining the “intersection of compression and color withoutexploring them individually.” The result is more of a con-ceptual overview. The author cites that the vastness of thefield makes it impossible to cover the topic of standardmethods like JPEG with any of the required depth. As aresult, a general approach is outlined and there is a verybrief description of how various standard encoding methodsfit in to the generalized scheme. Color transforms are alsoexamined as is the compression of both RGB and CMYKimages. The chapter is informative but anyone looking toget a foothold on standard compression methods will haveto go beyond this work. The literature cited is a start but theinterested reader will want to search beyond for additionalrelevant work.

Chapter 9, “Color Quantization,” by Luc Brun and AlainTremeau, is a logical follow-up to the previous chapterbecause quantization is, in effect, a type of compression.The aim of color image quantization is to reduce the numberof colors in an image while minimizing perceived degrada-tion in the image quality. As a result, images can be moreefficiently stored and more easily and economically trans-mitted in multimedia applications. Historically, such quan-tization methods have been designed around the applica-tion-dependent trade-offs of both computational efficiencyand quality of the quantized image. After a brief mention ofimage-dependent quantization, the chapter focuses on thehigher quality method of image-independent quantization.The authors examine in detail the preprocessing consider-ations as well as a variety of clustering methods, algorithmsbased on weighted errors, postclustering methods, mappingmethods, and dithering methods. The authors site the ongo-ing research in this area and discuss improvements that mayresult on color metrics designed for complex image scenesrather than CIE-based metrics.

Chapter 10, “Gamut Mapping,” by Jan Morovic, presentsthe various complex facets of the gamut-mapping problemsencountered when reproducing color images in various me-dia. This is another chapter that does a good job of present-ing a systematic instructional narrative that clearly eluci-dates the topic and simplifies it by crafting the discussionaround what happens when an image is reproduced betweena pair of media. The concepts of gamut, rendering intent,color spaces, and input/output data are discussed as arevarious means of describing the color gamut boundary. Thechapter then discusses a number of means of gamut clip-ping, gamut mapping, and gamut compression. There is alsoa brief mention of gamut expansion, spatial solutions, andmore complex approaches. Most importantly, the chapteremphasizes that this is an area of rapid learning and devel-opment. As a result, there are no clear answers in terms ofthe “best” method to use. This chapter gives the reader a

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good foundation to help make choices among the availablemethods and to continue learning more about this area.

Chapter 11, “Efficient Color Transformation Implemen-tation,” by Raja Bala and Victor Klassen, provides a goodsystematic overview of the transformation techniques usedin color imaging. Transforming color information can in-volve the use of complex functions that lead to slow com-putation. Large one-to-one lookup tables (LUTs) are cum-bersome and prohibitive to store. As a result, varioustechniques are used to create representative samplings fromwhich efficient color transformation can then be achieved.The authors briefly discuss interpolation techniques andfollow that with acceleration techniques, both of which arediscussed primarily with respect to multidimensional LUTs.The chapter is rounded out with discussion of a variety ofmethods for efficient transformation of image date throughLUTs.

Chapter 12, “Color Image Processing for Digital Cam-eras,” by Ken Parulski and Kevin Spaulding, is the closingchapter in the volume. This chapter covers diverse topics,including the basics of camera architecture, color imagesensors, demosaicing, exposure and white balance, tonescale and color processing, noise reduction and sharpening,and image compression and file formats. Most of the mate-rial provided is a good distillation; however, a few of thesections such as “Noise Reduction and Sharpening” and“Image Compression and File Formats” would have bene-fited from a bit more narrative detail. There are a number ofrelevant references that point readers to additional work andappropriate standards.

Overall, this is a reasonably good reference text. If I haveone complaint, it is that the book does not cross-referenceitself as much as it could. To be fair, there are some chaptersthat do a good job of cross-referencing but others standcompletely alone. For the expert reader this is not super-critical, although it may prove a little problematic for some-one newer to the field. For this reviewer, one thing that isvery valuable is the extensive citations provided at the endof most chapters. There is one inconsistency in that Chap-ters 1, 9, and 10 provide the citations in bibliography style,whereas the other chapters use a narrative numerical order.A consistent usage would impart a more unified look. As is,the chapters flow reasonably well in terms of their writtenstyle, which is difficult to achieve when you have so manyauthors.

It is always a problem to integrate color images into a textand provide a book at a sellable price. There are color platesprovided in the center of the book but as with other colorvolumes, the book would benefit from having them in placewithin the text. Again this is relatively minor. However,there are other books in the digital-imaging domain thatsuccessfully and economically integrate color throughout—which influences reader expectations. As stated earlier, thisbook is not a “how-to” that guides you through the processof successfully using state-of-the-art commercial color andimaging technology packages. It does, however, provide agood mix of the mathematics, physical science, computer

science, and the engineering backgrounds that are the un-derpinning of much of that technology.

JOANN TAYLOR

Published online in Wiley InterScience (www.interscience.wiley.com).DOI 10.1002/col.20049

Handbook of Image Quality: Characterization and Pre-diction, by Brian W. Keelan with contributions fromRobert E. Cookingham, Paul J. Kane, Karin Topfer, andRichard B. Wheeler. Marcel Dekker, Inc., 2002, 516 pp.,ISBN 0–8247-0770–2 Hardcover $195

The Handbook of Image Quality is authored by Dr. BrianKeelan, a scientist and engineer at the Eastman KodakCompany in Rochester, New York. Dr. Keelan has assem-bled an array of 31 chapters, grouped into three mainsections that offer a glimpse into the methods that he and hiscolleagues use to quantify image quality. Various chaptersare coauthored by several of Dr. Keelan’s colleagues whohave a special expertise in the topic of that chapter. Thebook is worthwhile for engineers interested in learningabout the general principles and mathematical methods usedby the team at Eastman Kodak.

The first section describes general quantitative methodsthat are used to characterize image quality. The secondsection describes how to use these methods to developspecific image quality metrics. The third section describeshow to use these metrics to characterize the quality of animaging system.

The mathematical tools are only as useful as their specificconnection to useful physical and psychological quantities.Hence, the author spends much of the first section develop-ing his view of a key perceptual concept: the just noticeabledifference or JND. The author defines a JND as a unit ofperception or a “measure of the perceptual continuum.”Much of the work in the book, including the mathematicaldevelopment, assumes that there is an internal perceptualscale of image quality and that JNDs are the units of thisinternal scale.

This book’s use of the JND, a fundamental concept inperception and pychology, differs from the original use ofthis term that was introduced by Fechner in 1860. Fechner(and many others) introduced the JND as a stimulus-re-ferred quantity. Specifically, psychologists generally de-scribe a JND as the smallest change in a stimulus property,such as intensity or wavelength, that can be detected reli-ably.

The thinking described in this volume extends the classicconcept of a JND into much more psychological terms.According to the authors, a “JND increment” is the smallestphysical change (“units of an objective metric”) that pro-duces a JND unit of perception. This endows the idea witha much more complex, and unobservable, status. It is thisinternal psychological scale that forms the basis for most ofthe mathematical developments. Hence, the difference ismuch more than a trivial difference in terminology.

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For example, the authors also introduce the term 50%JNDs of Quality. In practice, the 50% JNDs of Quality arecalculated from subjective judgments about image qualitywithout a specific stimulus-referred measurement. Carryingon into theory, 50% JND of Quality is the stimulus trans-formation such that 50% of the time people perceive oneimage to have higher quality than all other images. Insummary, the terms JND, JND increments, and 50% JNDsof Quality and the accompanying mathematical formalismstend to confuse the issues and assumptions underlying theirapproach to quantifying image quality.

The majority of the early chapters are spent outliningmathematical formalisms. Then, the authors provide somevery useful and practical advice about how to ask people tomake judgments about image quality for displayed andprinted images. They describe one particularly usefulmethod, which they call the “quality ruler.” This methodconsists of asking people to judge images with respect to aset of standard images. The standard images are assignednumbers to reflect their position in an implied image qualityruler. Their “quality ruler” method takes less time thanpairwise comparisons in which people are asked to indicatewhich of two images they prefer. The “quality ruler” as-sumes, however, that images can be ordered along a singlestimulus dimension. In some cases, this assumption mightbe valid. For example, if the images are uniform and theonly difference is the amplitude of the distribution of noise,then this assumption could very well hold. Change thespatial distribution of noise, however, and the assumptionwill surely be violated. The authors are aware of these issuesand address them later in the book. However, they do notwarn users that before they use the “quality ruler” method,they should test that the assumptions are valid.

In the first section of the book, the authors avoid theproblem of how to evaluate the perception of image qualitywhen images differ by more than one stimulus dimension orattribute. They describe the troublesome interactions thatcould occur if stimulus dimensions interact. But then theyassert that these interactions are not common. If readers areinterested in exploring how to quantify interactions betweenstimulus dimensions they should consult MultidimensionalScaling by Kruskal and Wish.1

The second section of the book covers the design ofobjective metrics where a metric is defined as a “singlenumber that correlates with an attribute of image quality.”To define a metric, one must be able to calculate an “ob-jective measure.” Examples of objective measures includethe modulation transfer function (MTF) of a device or thenoise power spectrum (NPS). The authors assume that read-ers know how to make these measurements. They use termssuch as streaking NPS or the banding NPS to describe theamplitude of the NPS restricted to the spatial frequency ofprint streaking and print banding, respectively.

The authors end this section of the book by giving thereader helpful advice, hints, and examples that illustratehow they developed objective metrics. For example, theysuggest that, whenever possible, one should study the ef-fects of a single stimulus variable on image quality ratings

in isolation. If image quality ratings are monotonicallyrelated to values along the isolated stimulus dimension, thenconsider developing a potential objective metric. If possible,find a transformation of stimulus dimension that generates alinear relationship between image quality ratings and thetransformed stimulus dimension. If there is another stimulusdimension or factor that influences image quality ratings, tryto predict quality ratings by scaling the transformed stimu-lus dimension by a factor that is dependent on the seconddimension.

For example, let’s assume that the amplitude of a noisespectrum can predict image quality ratings. If spatial fre-quency, orientation and location of the noise in an imagealso influences image quality ratings, one might be able topredict image quality ratings by a “multivariate formalism”that weights the amplitude of the noise spectrum by a factorthat depends on the frequency, orientation and location ofthe noise.

In the final section of the book, titled “Modeling systemquality”, the authors point out that it is important to considerhow each of the components in an imaging system influenceimage quality. They propose several different approaches toachieve this goal. First, they suggest that one should char-acterize how different system components affect objectivemetrics such as the “mean channel signal (MCS), imagewavelength spectrum (IWS),” modulation transfer function(MTF), and the noise power spectrum (NPS). They note thatone could calculate the MTF of a display from the Gaussianblur of a pixel and the NPS of a printer by analysis ofprinted uniform color patches. They show examples ofMTFs derived from grayscale targets (such as a slanted-edge, square wave, and isotropic noise) that were sampledby a color filter array or by a halftone rendering algorithm.Unfortunately, the authors give no details about the mea-surement conditions and instruments by which these MTFswere calculated, so the readers cannot reproduce the anal-ysis.

A second approach to modeling system image quality isto build a software package to model all of the componentsof the imaging system. Although the authors neither de-scribe such a system nor offer a plan for building such asystem, they do stress the importance of a systems approachto image quality evaluation. They also describe various userinterface features that they would like to see in a systems-based software simulation package that would model theeffects of a camera lens, camera film, film scanner, anddisplay. I agree that this is a good idea and it should bedone!

The third section concludes with examples of imaging-system parameters that influence measurements of subjec-tive image quality. For example, how does the gain of anunsharp masking filter and the position accuracy of a filmscanner affect subjective judgments of image quality? Howdo parameters for auto focusing, camera and printer reso-lution, and JPEG quantization influence image quality?Again, the authors provide no details of how they obtainedtheir measurements so the reader can neither understand norreplicate their results. They do suggest that if one has many

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stimulus parameters and associated quality ratings, onecould use these measurements to predict how various pa-rameters interact to determine an overall system quality.They specifically advocate the use of Monte Carlo simula-tions, and they include the results—but not the methods—from their own simulations. The absence of detail makes thebook somewhat less useful, of course, and it is difficult toknow how to evaluate the usefulness of the methods withoutknowing these details.

The Handbook of Image Quality is neither an introduc-tory textbook nor a reference book. It does, however, de-scribe the general principles and mathematical methodsused by the team at Eastman Kodak to characterize howvarious components in imaging systems influence the sub-jective image quality of printed and displayed images.

JOYCE E. FARRELL

1. Kruskal JB, Wish M. Multidimensional scaling. London: Sage; 1978.

Published online in Wiley InterScience (www.interscience.wiley.com).DOI 10.1002/col.20050

Seeing in Depth, Volume I: Basic Mechanisms, VolumeII: Depth Perception, by Ian P. Howard and Brian J.Rogers. I Porteous, 2002. 2 vols., 650 and 625 pp., ISBN0–9730873-0–7 (v. 1); ISBN 0–9730873-1–5 (v. 2)Hardcover $80

Seeing in Depth, which comes in two volumes, contains asurvey of knowledge about the visual perception of thethree-dimensional visual world. There are 14 chapters (659pages) in Volume I and 16 chapters (626 pages) in VolumeII. The primary interest of this book is biological vision.They mentioned partial machine vision and computationalmodels only when relating to an understanding of the livingsystem. The book was developed from Howard and Rog-ers’s Binocular Vision and Stereopsis, which was publishedin 1995. The topics have been reorganized, many newsections and chapters have been added, and the literaturereview has been brought up to date. The present book coversall aspects of depth perception, including a review of mon-ocular cues to depth.

I have tried to read this very thick two-volume book.Some materials are interesting and familiar, but most aregeared toward psychophysics and physiology and I lostinterest. I noticed that the famous book Vision, by MITprofessor David Marr, was cited in the references, which isgood. I am in the field of computer science specificallyworking with computer vision and pattern recognition. Thisbook seemingly does not cover computer processing of

vision. For example, the famous book Computer Vision byBallard and Brown is never referred to.

Of course, to do a good job in interpreting natural sceneimages, we need to understand the physiology and psycho-physics of visual perception systems. However, the lack oftransplanting human vision into computer vision is incom-plete in any sense. Although I enjoyed reading the book, dueto time constraints and job duties, I often found that I waspulled back into the research areas of computer vision andpattern recognition. Let me state more about specific chap-ters below.

Chapter 2 reviews the history of visual perception to theearly 20th century. It is very interesting. Until the 17thcentury, the word optics meant all things pertaining tovision. The early-stage display systems are also reviewed inthis chapter.

Chapter 3 introduces psychophysics and analysis. Psy-chophysics is the quantitative study of how people or ani-mals detect, resolve, discriminate, identify, categorize, anddescribe defined stimuli. This chapter briefly outlines psy-chophysical methods with an emphasis on those used in thestudy of depth perception. Some mathematical backgroundsare introduced, such as Fourier analysis and transfer func-tions. Nonlinear filtering systems, Bayesian interface, andgeometric transformation are also reviewed. However, thoseare briefly mentioned. If a beginner wants to understand themathematics, more references are needed.

Chapter 4 describes sensory coding. I’m happy to see thematerial on Gabor filters which have been used heavily incomputer vision, more specifically in face identification andmedical imaging. Gabor filters are defined by harmonicfunctions modulated by a Gaussian distribution. They bearsome similarity to Fourier filters but are limited to certainfrequency bands.

Chapters 5 to 14 describe the physiology of the visualsystem, pathology, and visual functions of different species.

Chapters 15 to 21 introduce binocular correspondence,stereoscopic acuity, and classification of binocular disparity.Chapters 22 to 29 describe depth contrast, depth from mon-ocular cues and vergence, depth from motion parallax, andseeing motion in depth. Chapter 30, which concludes thebook, covers stereoscopic techniques and applications.

Overall, this book is good and comprehensive in present-ing depth perception using psychophysical, physiological,and computational techniques. However, it would be betterto also include a more extensive review of the newly de-veloped computerized techniques such as neural networks,support vector machine, fuzzy classifier, and syntactic rec-ognition.

FRANK Y. SHIH

Published online in Wiley InterScience (www.interscience.wiley.com).DOI 10.1002/col.20051

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