Digital Color Imaging Handbook (Electrical Engineering & Applied Signal Processing Series)

The exponential growth in the use of color capture and display devices has made this handbook a must for engineers, scientists, and students in image processing and a range of related disciplines. It provides comprehensive coverage of the field, covering the fundamentals of color perception and measurement, as well as the color and image processing algorithms and technology behind current color input and output devices. It also explores how to improve on the processing of color images, how to calibrate different color devices, and digitally process color images. Digital Color Imaging Handbook......Page 1 Electrical Engineering & Applied Signal Processing Series......Page 3 Copyright......Page 6 Preface......Page 7 About the Editor......Page 10 Contributors......Page 11 Contents......Page 13 Ch1 Color Fundamentals for Digital Imaging......Page 14 1.1 Introduction......Page 15 1.2 Physical stimuli for color......Page 16 1.2.1 The stimulus error......Page 18 1.3 Human color perception and trichromacy......Page 19 1.4 Color matching......Page 22 1.4.1 Color-matching functions......Page 23 1.4.2 Metamerism and black space......Page 26 1.5.1 CIE standards......Page 28 1.5.2 Colorimetry for re.ective objects......Page 30 1.5.3 Chromaticity coordinates and chromaticity diagrams......Page 35 1.5.4 Transformation of primaries: NTSC, SMPTE, and CCIR primaries......Page 38 1.6 Alternative color speci.cation systems......Page 39 1.7 Uniform color spaces and color differences......Page 41 1.7.1 The CIE 1976 L*u*v* space......Page 42 1.7.2 The CIE 1976 L*a*b* space......Page 43 1.7.3 Limitations of CIELAB and CIELUV spaces......Page 45 1.7.4 Alternative color difference formulae......Page 47 1.8 Limitations of CIE colorimetry......Page 53 1.9 Psychophysics of color......Page 54 1.9.1 Chromatic adaptation and color constancy......Page 56 1.9.2 Opponent processes theory and color appearance models......Page 57 1.10 Spatial characteristics of color vision......Page 60 1.11 Color image reproduction and recording devices......Page 64 1.11.1 Color output systems......Page 65 1.11.1.1 Cathode ray tubes......Page 69 1.11.1.2 LCD displays......Page 73 1.11.1.3 Contone printers......Page 76 1.11.1.4 Halftone printers......Page 80 1.11.1.5 Recent advances in color displays and printing......Page 83 1.11.2 Image characteristics......Page 84 1.11.3 Computer-generated imager......Page 85 1.11.4.1 Spectroradiometers and spectrophotometers......Page 86 1.11.4.2 Colorimeters and photometers......Page 90 1.11.4.3 Photographic .lm-based recording schemes......Page 91 1.11.4.4 Digital dolor cameras and scanners......Page 92 1.11.5 Multispectral recording and reproduction systems......Page 98 1.11.5.1 Principal-component recording......Page 100 1.11.6 Quantization and coding......Page 101 1.11.7 Device color spaces......Page 102 1.12 Color management and calibration......Page 103 1.12.1.1 Input device calibration......Page 104 1.12.1.2 Output device calibration......Page 106 1.12.2 Color management systems......Page 107 1.12.3 Gamut mapping......Page 108 1.12.4 Appearance matching......Page 109 Acknowledgments......Page 110 References......Page 111 Ch2 Visual Psychophysics & Color Appearance......Page 128 2.1 Introduction......Page 129 2.2 Terminology......Page 130 2.2.1 Color......Page 131 2.2.3 Hue......Page 132 2.2.4 Brightness and lightness......Page 133 2.2.5 Colorfulness and chroma......Page 134 2.2.6 Saturation......Page 135 2.2.7 Digital color reproduction: brightness–colorfulness or lightness – chroma......Page 136 2.3.2 Psychophysical techniques......Page 137 2.3.3 Hierarchy of scales......Page 138 2.3.4 Threshold and scaling: a historical perspective on Weber, Fechner, and Stevens......Page 139 2.3.5 Psychophysical methods: threshold techniques......Page 141 2.3.5.1 Method of adjustment......Page 142 2.3.5.2 Method of limits......Page 143 2.3.5.3 Method of constant stimuli......Page 144 2.3.5.4 Matching techniques......Page 145 2.3.6 Psychophysical methods: scaling techniques......Page 146 2.4.1 Stimulus......Page 150 2.4.3 Background......Page 151 2.4.5 Modes of viewing......Page 152 2.5.1 Spatially structured phenomena......Page 154 2.5.2 Luminance phenomena......Page 157 2.5.4 Surround phenomena......Page 160 2.5.5 Color constancy and discounting the illuminant......Page 161 2.6.1 Light and dark adaptation......Page 162 2.6.2 Chromatic adaptation......Page 163 2.6.4 von Kries model......Page 166 2.6.6 Nayatani’s model......Page 168 2.6.7 Fairchild model......Page 169 2.6.8 Spectrally sharpened chromatic adaptation models......Page 171 2.7 Color appearance models......Page 172 2.7.1 CIELAB as a color appearance model......Page 173 2.7.2 The genesis of color appearance models......Page 175 2.7.3 CIECAM97s......Page 176 2.7.3.1 Chromatic adaptation......Page 177 2.7.3.2 Appearance correlates......Page 179 2.7.3.3 Using the model......Page 180 References......Page 181 Ch3 Physical Models for Color Prediction......Page 185 3.1 Introduction......Page 186 3.2 A few results from radiometry......Page 187 3.3.1 Basic laws......Page 190 3.4 Light absorption......Page 192 3.5 Light scattering......Page 198 3.5.1 Rayleigh scattering......Page 200 3.5.2 Mie scattering......Page 201 3.5.3 Multiple scattering......Page 204 3.6.1 Radiative transfer......Page 205 3.6.2 Kubelka-Munk model (two-flux model)......Page 206 3.6.3 Surface phenomena and Saunderson correction......Page 211 3.6.4 Multichannel model......Page 215 3.7 The fluorescence phenomenon......Page 217 3.7.1 Fluorescence: transparent layer......Page 219 3.7.2 From a one-flux to a two-flux model for a reflective substrate......Page 223 3.7.3 Spectral prediction for reflective fluorescent material......Page 226 3.7.4 Measuring the parameters of the fluorescence model......Page 228 3.8.1 The Murray-Davis equation......Page 232 3.8.2 The classical Neugebauer theory......Page 233 3.8.3 Extended Neugebauer theory......Page 234 3.8.4 The Yule-Nielsen equation......Page 235 3.8.5 The Clapper-Yule equation......Page 236 3.8.6 Advanced models......Page 237 3.9 New mathematical framework for color prediction of halftones......Page 240 3.9.1 Some particular cases of interest......Page 244 3.10 Concluding remarks......Page 245 References......Page 247 Ch4 Color Management for Digital Imaging Systems......Page 251 4.2 Color management paradigms......Page 252 4.3 Digital color encoding......Page 254 4.4 Color encoding methods......Page 255 4.5 Image states......Page 257 4.6 Standard image-state color encoding specifications......Page 261 4.6.1 Criteria for selection of RIMM/ROMM RGB color encoding specifications......Page 263 4.6.2 ROMM RGB color encoding specification......Page 267 4.6.2.2 Nonlinear encoding of ROMM RGB......Page 269 4.6.3 RIMM RGB color encoding specification......Page 270 4.6.3.2 Nonlinear encoding of RIMM RGB......Page 271 4.6.4 ERIMM RGB color encoding specification......Page 272 4.6.4.1 Nonlinear encoding for ERIMM RGB......Page 273 4.7 Image states in a color managed architecture......Page 274 4.8 Digital color management with JPEG 2000......Page 277 4.9 Summary......Page 278 References......Page 279 Ch5 Device Characterization......Page 281 5.1 Introduction......Page 283 5.2.1 Device calibration......Page 284 5.2.2 Device characterization......Page 285 5.2.3 Input device calibration and characterization......Page 287 5.2.4 Output device calibration and characterization......Page 293 5.3.1 Color target design......Page 297 5.3.2.2 Instrument-based approaches......Page 298 5.3.3 Absolute and relative colorimetry......Page 301 5.4 Multidimensional data fitting and interpolation......Page 303 5.4.2 Weighted least-squares regression......Page 306 5.4.3 Polynomial regression......Page 307 5.4.4.1 Shepard's interpolation......Page 310 5.4.4.2 Local linear regression......Page 311 5.4.5 Lattice-based interpolation......Page 313 5.4.6 Sequential interpolation......Page 316 5.4.7 Neural networks......Page 318 5.4.8 Spline fitting......Page 320 5.5 Metrics for evaluating device characterization......Page 323 5.6 Scanners......Page 325 5.6.1 Calibration......Page 326 5.6.2 Model-based characterization......Page 327 5.6.3 Empirical characterization......Page 330 5.7 Digital still cameras......Page 331 5.7.1 Calibration......Page 332 5.7.2 Model-based characterization......Page 333 5.7.3 Empirical characterization......Page 334 5.7.4 White-point estimation and chromatic adaptation transform......Page 335 5.8.1 Calibration......Page 336 5.8.2 Characterization......Page 339 5.8.3 Visual techniques......Page 340 5.9 Liquid crystal displays......Page 341 5.9.1 Calibration......Page 342 5.10.1 Calibration......Page 343 5.10.1.1 Channel-independent calibration......Page 344 5.10.1.2 Gray-balanced calibration......Page 346 5.10.2 Model-based printer characterization......Page 349 5.10.2.1 Beer-Bouguer model......Page 350 5.10.2.2 Kubelka-Munk model......Page 353 5.10.2.3 Neugebauer model......Page 356 5.10.3.1 Lattice-based techniques......Page 369 5.10.3.2 Sequential interpolation......Page 370 5.10.4 Hybrid approaches......Page 372 5.10.5 Deriving the inverse characterization function......Page 373 5.10.5.1 CMY printers......Page 374 5.10.5.2 CMYK printers......Page 376 5.10.7.1 Forward characterization......Page 380 5.10.8 Projection transparency printing......Page 382 5.11 Characterization for multispectral imaging......Page 383 5.12 Device emulation and proofing......Page 384 5.13 Commercial packages......Page 385 References......Page 386 Least-squares optimization......Page 392 Derivation of 3x3 matrix from display RGB to XYZ given white point and chromaticities of the primaries......Page 394 Ch6 Digital Color Halftones......Page 396 6.1 Introduction......Page 398 6.1.1 History of halftoning......Page 399 6.2.1 Halftone structure......Page 400 6.2.2 Threshold array halftone algorithms......Page 401 6.2.3 Spatially adaptive halftone algorithms......Page 403 6.2.4 Trade-offs and color halftoning issues......Page 404 6.3.1 Noting the printer's special characteristics......Page 406 6.3.2 Decisions involved in choosing halftone structure......Page 408 6.3.3 Choosing frequencies, stochastic, cluster, error diffusion......Page 410 6.3.4 UCR/GCR strategy: minimum or maximum......Page 411 6.4.1 Implementation......Page 412 6.4.3 Partial dots......Page 414 6.4.5 Angled screens (Holladay)......Page 416 6.4.6 Rational tangent screens......Page 418 6.4.7 Supercells and accurate screens......Page 422 6.4.7.1 Multi-center screens......Page 423 6.4.8 High addressability......Page 425 6.4.9 Halftone grid relationships......Page 427 6.4.10.2 Dot-center migration......Page 429 6.4.10.3 Dot cell boundary......Page 430 6.4.10.5 Dot gain......Page 432 6.4.10.7 Dot frequency......Page 434 6.4.10.11 Dot shape......Page 435 6.4.10.12 Where the dots touch......Page 437 6.4.10.13 Data precision and file size......Page 439 6.4.11 Angle family......Page 440 6.4.13 Calibration......Page 441 6.4.13.1 Screen threshold assignments......Page 442 6.4.13.2 Individual screen calibration......Page 443 6.4.13.3 Neutral calibration......Page 444 6.4.13.4 Color characterization......Page 445 6.5.1 Orientations......Page 446 6.5.1.2 Dot-off-dot......Page 447 6.5.2 Model predictions......Page 448 6.5.2.1 Sensitivity to registration......Page 450 6.5.2.3 Sensitivity to moira......Page 452 6.6 Moire......Page 453 6.6.2 Dot center phase......Page 454 6.6.3 Two-color moire......Page 455 6.6.4 Three-color moire......Page 456 6.7.1 Introduction......Page 457 6.7.2 Dual representation of nonorthogonal screens......Page 460 6.7.3 Moire-free conditions......Page 462 6.7.5 An example of moire-free nonorthogonal screens......Page 466 6.8.1 Introduction......Page 468 6.8.2 Error diffusion algorithm......Page 469 6.8.4 Spectral analysis of error diffusion......Page 471 6.8.5 Error image and edge enhancement......Page 473 6.8.6 Color error diffusion......Page 474 6.8.7 Vector error diffusion......Page 476 6.8.8 Semi-vector error diffusion......Page 478 6.8.9 Stochastic screens......Page 479 6.9.1 Introduction......Page 481 6.9.2 Dot overlapping......Page 482 6.9.3 Two-by-two centering concept......Page 484 6.9.4 Neugebauer equations and YuleÒNielsen modification......Page 487 6.9.5 Calibrating 2......Page 489 6.9.6 Halftone printer characterization......Page 490 6.9.7 Feedback using a 2......Page 492 References......Page 495 Recommended readings......Page 498 Ch7 Human Visual Model-Based Color Halftoning......Page 502 7.1 Introduction......Page 503 7.2 Color hardcopy models......Page 505 7.3 Color human visual system models......Page 510 7.4 HVS model-based iterative color halftoning algorithms......Page 514 7.4.2 Color direct binary search......Page 515 7.4.3 Two-by-two centering-based CDBS......Page 523 7.4.4 Iterative RGB......Page 528 7.5 HVS-model-based color error diffusion......Page 535 7.6 HVS-based clustered-dot color screen design......Page 536 7.6.1.2 Periodicity matrix......Page 539 7.6.2 Clustered-dot color screen design......Page 540 7.6.2.1 Discrete parameter halftone cell......Page 541 7.6.2.2 Macrodot shape and growth......Page 543 7.6.3 Printer and perceptual model and error metrics......Page 546 7.6.3.1 Color device model......Page 548 7.6.3.2 Error metrics......Page 549 7.6.4 Optimization......Page 551 7.6.5 Experimental results......Page 553 7.7 Summary and conclusions......Page 556 References......Page 563 8.1 Compression basics......Page 569 8.2.1 Transform coding......Page 572 8.2.2 Predictive coding......Page 577 8.2.3 Rate-distortion trade-off......Page 578 8.2.4 Distortion measure......Page 579 8.3 Standard image coders......Page 580 8.4 Multidimensional color model and transforms......Page 584 8.5 Color transforms......Page 587 8.6 Compressing RGB images......Page 590 8.7 Compressing CMYK images......Page 592 8.8 Summary......Page 595 References......Page 596 Ch9 Color Quantization......Page 598 9.1 Introduction......Page 599 9.2 Image independent quantization methods......Page 601 9.3.1 Prequantization......Page 602 9.3.2 Histogram calculation......Page 603 9.4 Clustering methods......Page 604 9.4.1 3......Page 608 9.4.2 Three-dimensional splitting methods......Page 610 9.4.2.1 Splitting strategy......Page 611 9.4.2.2 Cluster selection......Page 612 9.4.2.3 Cutting axis......Page 613 9.4.2.4 Cutting position......Page 614 9.4.3 Grouping methods......Page 616 9.4.3.1 The merge and box algorithm......Page 617 9.4.3.2 The max-min algorithm......Page 618 9.4.4 Merge methods......Page 619 9.5 Quantization algorithms based on weighted errors......Page 621 9.6.1 The LBG and k-means algorithms......Page 627 9.6.2 The NeuQuant neural-net image quantization algorithm......Page 628 9.7 Mapping methods......Page 629 9.7.1 Improvements of the trivial inverse colormap method......Page 630 9.7.3 Inverse colormap operations using k Ò d trees......Page 632 9.7.4 The locally sorted search algorithm......Page 633 9.7.5 Inverse colormap operation using a three-dimensional Voronoë diagram......Page 634 9.7.6 Inverse colormap operation by a two-dimensional Voronoë diagram......Page 635 9.8.1 Error diffusion methods......Page 636 9.8.2 Ordered dither methods......Page 638 9.8.3 Vector dither methods......Page 639 9.8.4 Joint quantization and dithering methods......Page 640 9.9 Conclusion and perspectives......Page 642 References......Page 643 Ch10 Gamut Mapping......Page 647 10.1 Introduction......Page 648 10.2.1 Reproduction intent......Page 650 10.2.2.1 Original data......Page 652 10.2.2.2 Intermediate color space......Page 653 10.2.2.3 Reproduction data......Page 654 10.3.1 Implications of color gamut definition......Page 655 10.3.2.1 Colors of image and media in example scenario......Page 656 10.3.2.2 Methods for describing color gamut boundaries......Page 659 10.3.2.3 Visualizing gamut boundaries......Page 663 10.3.3 Line gamut boundary algorithms......Page 664 10.3.4.1 Imaging medium gamuts......Page 667 10.3.4.2 Gamut mismatch in example scenario......Page 668 10.4 Gamut mapping algorithms......Page 669 E clipping algorithms......Page 670 10.4.1.2 Other clipping algorithms......Page 675 10.4.2 Simple gamut-compression algorithms......Page 677 10.4.2.1 Simultaneous compression algorithms......Page 678 10.4.2.3 Choosing the original gamut......Page 680 10.4.3 Composite gamut mapping algorithms......Page 681 10.4.4 Other algorithms for mapping into a smaller gamut......Page 684 10.4.4.2 Spatial GMAs......Page 685 10.5.1 Media......Page 686 10.5.2 Images......Page 688 10.6 Summary......Page 689 References......Page 690 Ch11 Efficient Color Transformation Implementation......Page 694 11.1 Introduction......Page 695 11.2.1 Regular lattice structures......Page 696 11.2.1.1 Lattice dimensions: power of two or one greater?......Page 697 11.2.2 Two-dimensional interpolation geometries......Page 699 11.2.3 Three-dimensional interpolation geometries......Page 701 11.2.3.1 Trilinear interpolation......Page 703 11.2.3.2 Prism interpolation......Page 704 11.2.3.3 Pyramidal interpolation......Page 705 11.2.3.4 Tetrahedral interpolation......Page 706 11.3 Interpolation on irregular lattices......Page 708 11.4.2 Caching output values......Page 710 11.4.3 Hashing......Page 711 11.4.5 Eliminating multiplications......Page 712 11.4.6 Eliminating tests......Page 713 11.5 Color transforming palettized images......Page 714 11.6.1 Introduction......Page 715 11.6.2 Results......Page 716 11.7.1 Correcting for RGB devices......Page 717 11.7.3 Color correction in the JPEG compressed domain......Page 719 11.8 Color transforming multiresolution images......Page 720 11.8.2 Combining multiresolution analysis and color correction......Page 722 11.8.3 Results......Page 724 11.9.1 Introduction......Page 726 11.9.2 Results......Page 728 11.10 Conclusions......Page 730 References......Page 731 Ch12 Color Image Processing for Digital Cameras......Page 733 12.2 Digital camera architecture......Page 734 12.2.1 Digital camera hardware......Page 735 12.2.2 Color separation methods......Page 736 12.2.4 Unrendered camera processing......Page 738 12.3 Color image sensors......Page 739 12.3.1 Full-frame CCDs......Page 740 12.3.2 Interline CCDs......Page 741 12.3.4 Color filter array patterns......Page 742 12.3.5 Sensor spectral response......Page 743 12.4 Color de-mosaicing in single-sensor cameras......Page 745 12.5.2 Dynamic range......Page 747 12.5.3 White balance determination......Page 749 12.6.1 Capture colorimetry model......Page 750 12.6.2 Tone scale/color rendering......Page 753 12.6.3 Output model......Page 755 12.6.4 Processing configurations......Page 757 12.7.1 Noise reduction......Page 758 12.7.2 Edge sharpening......Page 759 12.8.1 Exif/JPEG image format......Page 760 References......Page 761

digital Technology Now Enables Unparalleled Functionality And Flexibility In The Capture, Processing, Exchange, And Output Of Color Images. But Harnessing Its Potential Requires Knowledge Of Color Science, Systems, Processing Algorithms, And Device Characteristics-topics Drawn From A Broad Range Of Disciplines. One Can Acquire The Requisite Background With An Armload Of Physics, Chemistry, Engineering, Computer Science, And Mathematics Books And Journals- Or One Can Find It Here, In The Digital Color Imaging Handbook.

unprecedented In Scope, This Handbook Presents, In A Single Concise And Authoritative Publication, The Elements Of These Diverse Areas Relevant To Digital Color Imaging. The First Three Chapters Cover The Basics Of Color Vision, Perception, And Physics That Underpin Digital Color Imaging. The Remainder Of The Text Presents The Technology Of Color Imaging With Chapters On Color Management, Device Color Characterization, Digital Halftoning, Image Compression, Color Quantization, Gamut Mapping, Computationally Efficient Transform Algorithms, And Color Image Processing For Digital Cameras.

each Chapter Is Written By World-class Experts And Largely Self-contained, But Cross References Between Chapters Reflect The Topics' Important Interrelations. Supplemental Materials Are Available For Download From The Crc Web Site, Including Electronic Versions Of Some Of The Images Presented In The Book.

the Exponential Growth In The Use Of Color Receiving And Display Instruments Makes This Handbook A Must For Students, Engineers, And Scientists In Image Processing And A Range Of Related Disciplines. It Provides Comprehensive Coverage Of The Field, Examining The Fundamentals Of Color Perception, Color Measurement, And Color Appearance. It Describes Current Applications And The Technology And The Impact Of Color Input And Output Devices On Color Rendition. It Also Explores How To Improve On The Processing Of Color Images, How To Calibrate Instruments, Split Pixels, And Digitally Process Color Images.

Digital technology now enables unparalleled functionality and flexibility in the capture, processing, exchange, and output of color images. But harnessing its potential requires knowledge of color science, systems, processing algorithms, and device characteristics-topics drawn from a broad range of disciplines. One can acquire the requisite background with an armload of physics, chemistry, engineering, computer science, and mathematics books and journals- or one can find it here, in the Digital Color Imaging Handbook. Unprecedented in scope, this handbook presents, in a single concise and authoritative publication, the elements of these diverse areas relevant to digital color imaging. The first three chapters cover the basics of color vision, perception, and physics that underpin digital color imaging. The remainder of the text presents the technology of color imaging with chapters on color management, device color characterization, digital halftoning, image compression, color quantization, gamut mapping, computationally efficient transform algorithms, and color image processing for digital cameras. Each chapter is written by world-class experts and largely self-contained, but cross references between chapters reflect the topics' important interrelations. Supplemental materials are available for download from the CRC Web site, including electronic versions of some of the images presented in the book.