Computational color technology

Description

Henry Kang provides the fundamental color principles and mathematical tools to prepare the reader for a new era of color reproduction, and for subsequent applications in multispectral imaging, medical imaging, remote sensing, and machine vision. This book is intended to bridge the gap between color science and computational color technology, putting color adaptation, color constancy, color transforms, color display, and color rendition in the domain of vector-matrix representations and theories. [i]Computational Color Technology[/i] deals with color digital images on the spectral level using vector-matrix representations so that the reader can learn to process digital color images via linear algebra and matrix theory. Read more... 1. Tristimulus specification -- 1.1. Definitions of CIE tristimulus values -- 1.2. Vector-space representations of tristimulus values -- 1.3. Object spectrum -- 1.4. Color-matching functions -- 1.5. CIE standard illuminants -- 1.6. Effect of illuminant -- 1.7. Stimulus function -- 1.8. Perceived object -- 1.9. Remarks -- References. 2. Color principles and properties -- 2.1. Visual sensitivity and color-matching functions -- 2.2. Identity property -- 2.3. Color match -- 2.4. Transitivity law -- 2.5. Proportionality law -- 2.6. Additivity law -- 2.7. Dependence of color-matching functions on choice of primaries -- 2.8. Transformation of primaries -- 2.9. Invariant of matrix A (transformation of tristimulus vectors) -- 2.10. Constraints on the image reproduction -- References. 3. Metamerism -- 3.1. Types of metameric matching -- 3.2. Matrix R theory -- 3.3. Properties of matrix R -- 3.4. Metamers under different illuminants -- 3.5. Metameric correction -- 3.6. Indices of metamerism -- References. 4. Chromatic adaptation -- 4.1. Von Kries hypothesis -- 4.2. Helson-Judd-Warren transform -- 4.3. Nayatani model -- 4.4. Bartleson transform -- 4.5. Fairchild model -- 4.6. Hunt model -- 4.7. BFD transform -- 4.8. Guth model -- 4.9. Retinex theory -- 4.10. Remarks -- References. 5. CIE color spaces -- 5.1. CIE 1931 chromaticity coordinates -- 5.2. CIELUV space -- 5.3. CIELAB space -- 5.4. Modifications -- 5.5. CIE color appearance model -- 5.6. S-CIELAB -- References. 6. RGB color spaces -- 6.1. RGB primaries -- 6.2. Transformation of RGB primaries -- 6.3. RGB color-encoding standards -- 6.4. Conversion mechanism -- 6.5. Comparisons of RGB primaries and encoding standards -- 6.6. Remarks -- References. 7. Device-dependent color spaces -- 7.1. Red-green-blue (RGB) color space -- 7.2. Hue-saturation-value (HSV) space -- 7.3. Hue-lightness-saturation (HLS) space -- 7.4. Lightness-saturation-hue (LEF) space -- 7.5. Cyan-magenta-yellow (CMY) color space -- 7.6. Ideal block-dye model -- 7.7. Color gamut boundary of block dyes -- 7.8. Color gamut boundary of imaging devices -- 7.9. Color gamut mapping -- 7.10. CIE guidelines for color gamut mapping -- References. 8. Regression -- 8.1. Regression method -- 8.2. Forward color transformation -- 8.3. Inverse color transformation -- 8.4. Extension to spectral data -- 8.5. Results of forward regression -- 8.6. Results of inverse regression -- 8.7. Remarks -- References. 9. Three-dimensional lookup table with interpolation -- 9.1. Structure of 3D lookup table -- 9.2. Geometric interpolations -- 9.3. Cellular regression -- 9.4. Nonuniform lookup table -- 9.5. Inverse color transform -- 9.6. Sequential linear interpolation -- 9.7. Results of forward 3D interpolation -- 9.8. Results of inverse 3D interpolation -- 9.9. Remarks -- References. 10. Metameric decomposition and reconstruction -- 10.1. Metameric spectrum decomposition -- 10.2. Metameric spectrum reconstruction -- 10.3. Results of spectrum reconstruction -- 10.4. Application -- 10.5. Remarks -- References. 11. Spectrum decomposition and reconstruction -- 11.1. Spectrum reconstruction -- 11.2. General inverse method -- 11.3. Spectrum decomposition and reconstruction methods -- 11.4. Principal component analysis -- 11.5. Basis vectors -- 11.6. Spectrum reconstruction from the input spectrum -- 11.7. Spectrum reconstruction from tristimulus values -- 11.8. Error metrics -- 11.9. Results and discussions -- 11.10. Applications -- References. 12. Computational color constancy -- 12.1. Image irradiance model -- 12.2. Finite-dimensional linear models -- 12.3. Three-two constraint -- 12.4. Three-three constraint -- 12.5. Gamut-mapping approach -- 12.6. Lightness/Retinex model -- 12.7. General linear transform -- 12.8. Spectral sharpening -- 12.9. Von Kries color prediction -- 12.10. Remarks -- References. 13. White-point conversion -- 13.1. White-point conversion via RGB space -- 13.2. White-point conversion via tristimulus ratios of illuminants -- 13.3. White-point conversion via difference in illuminants -- 13.4. White-point conversion via polynomial regression -- 13.5. Remarks -- References. 14. Multispectral imaging -- 14.1. Multispectral irradiance model -- 14.2. Sensitivity and uniformity of a digital camera -- 14.3. Spectral transmittance of filters -- 14.4. Spectral radiance of illuminant -- 14.5. Determination of matrix AE -- 14.6. Spectral reconstruction -- 14.7. Multispectral image representation -- 14.8. Multispectral image quality -- References. 15. Densitometry -- 15.1. Densitometer -- 15.2. Beer-Lambert-Bouguer law -- 15.3. Proportionality -- 15.4. Additivity -- 15.5. Proportionality and additivity failures -- 15.6. Empirical proportionality correction -- 15.7. Empirical additivity correction -- 15.8. Density-masking equation -- 15.9. Device-masking equation -- 15.10. Performance of the device-masking equation -- 15.11. Gray balancing -- 15.12. Gray-component replacement -- 15.13. Digital implementation -- 15.14. Remarks -- References. 16. Kubelka-Munk theory -- 16.1. Two-constant Kubelka-Munk theory -- 16.2. Single-constant Kubelka-Munk theory -- 16.3. Determination of the single constant -- 16.4. Derivation of Saunderson's correction -- 16.5. Generalized Kubelka-Munk model -- 16.6. Cellular extension of the Kubelka-Munk model -- 16.7. Applications -- References. 17. Light-reflection model -- 17.1. Three-primary Neugebauer equations -- 17.2. Demichel Dot-overlap model -- 17.3. Simplifications -- 17.4. Four-primary Neugebauer equation -- 17.5. Cellular extension of the Neugebauer equations -- 17.6. Spectral extension of the Neugebauer equations -- References. 18. Halftone printing models -- 18.1. Murray-Davies equation -- 18.2. Yule-Nielsen model -- 18.3. Area coverage-density relationship -- 18.4. Clapper-Yule model -- 18.5. Hybrid approaches -- 18.6. Cellular extension of color-mixing models -- 18.7. Dot gain -- 18.8. Comparisons of halftone models -- References. 19. Issues of digital color imaging -- 19.1. Human visual model -- 19.2. Color appearance model -- 19.3. Integrated spatial-appearance model -- 19.4. Image quality -- 19.5. Imaging technology -- 19.6. Device-independent color imaging -- 19.7. Device characterization -- 19.8. Color spaces and transforms -- 19.9. Spectral reproduction -- 19.10. Color gamut mapping -- 19.11. Color measurement -- 19.12. Color-imaging process -- 19.13. Color architecture -- 19.14. Transformations between sRGB and Internet FAX color standard -- 19.15. Modular implementation -- 19.16. Results and discussion -- 19.17 Remarks -- References -- Appendices -- Index