Topics include illumination, the virtual camera, colour spaces, gamut mapping and colour management. Implementation of the colour reproduction and gamut-mapping algorithms demands a number of numerical and statistical methods, including multidimensional interpolation and approximation, regression, computational geometry, tensor-product and simplex splines, principal components analysis, and linear and non-linear optimization. Programming assignments in colour reproduction will be required, as well as a final project on a relevant research topic. Students taking this course should have a strong background in computer graphics and numerical methods.
Illumination (6 hrs)
Basics of light and materials. Data structures for spectral power distributions (SPDs), surface geometry, reflectance, and transmittance.The Virtual Camera (6 hrs)
Rendering and the virtual camera. Algorithms for manipulating the optical path, trichromatic and spectral colour mapping, comparisons with digital camera technology.Peceptual Response and Colour Spaces (6 hrs)
Mathematical models of the human visual system and trichromatic colour spaces, the CIE standard observer. Algorithms for colour mapping in high-dimensional spaces, linear reflectance models. Principal components analysis (PCA) and singular value decomposition (SVD).Image Display (6 hrs)
Data structures for printer and monitor gamuts. Statistical methods for fitting colour data, multidimensional interpolation and approximation, regression.Gamut Mapping (6 hrs)
Gamut-mapping algorithms: black- and white-point mapping, neutral axis alignment, advanced methods. Linear and non-linear optimization for out-of-gamut colour projection.
Computational geometry of device gamuts. Tensor-product and simplex spline methods for modelling and gamut mapping.Colour Management and Operating Systems (6 hrs)
Colour management algorithms in various operating systems. International Color Consortium (ICC) profiles, ColorSync, Kodak Color Management System (KCMS), ANSI IT8 targets.