Gamut mapping algorithms, implemented by color management systems, are an integral part of the color reproduction process. By adjusting the colors with appropriate algorithms, gamut mapping enables original colors to ‘fit’ inside differently shaped color gamuts and authentically transfers images across a range of media. This book illustrates the range of possible gamut mapping strategies for cross-media color reproduction, evaluates the performance of various options and advises on designing new, improved solutions. Starting with overviews of color science, reproduction and management, the text includes: a detailed survey of 90+ gamut mapping algorithms covering color-by-color reduction and expansion, spatial reduction, spectral reduction and gamut mapping for niche applications; a step-by-step example of a color’s journey from original to reproduction, via a digital workflow; a detailed analysis of color gamut computation, including a comparison of alternative techniques and an illustration of the gamuts of salient color sets and media; a presentation of both measurement-based and psychovisual evaluation of individual color reproductions; an overview of alternative approaches to gamut mapping proposed by the ISO and the CIE including an analysis of the building blocks of gamut mapping algorithms and the factors affecting their performance. Color Gamut Mapping is a comprehensive resource for practicing color and imaging engineers, scientists and researchers working in the development of imaging devices, software and solutions. It is also a valuable reference for students of color and imaging science, as well as photographers, graphic designers and artists.

Gamut mapping algorithms, implemented by color management systems, are an integral part of the color reproduction process. By adjusting the colors with appropriate algorithms, gamut mapping enables original colors to ‘fit’ inside differently shaped color gamuts and authentically transfers images across a range of media. This book illustrates the range of possible gamut mapping strategies for cross-media color reproduction, evaluates the performance of various options and advises on designing new, improved solutions. Starting with overviews of color science, reproduction and management, the text includes: a detailed survey of 90+ gamut mapping algorithms covering color-by-color reduction and expansion, spatial reduction, spectral reduction and gamut mapping for niche applications; a step-by-step example of a color’s journey from original to reproduction, via a digital workflow; a detailed analysis of color gamut computation, including a comparison of alternative techniques and an illustration of the gamuts of salient color sets and media; a presentation of both measurement-based and psychovisual evaluation of individual color reproductions; an overview of alternative approaches to gamut mapping proposed by the ISO and the CIE including an analysis of the building blocks of gamut mapping algorithms and the factors affecting their performance. Color Gamut Mapping is a comprehensive resource for practicing color and imaging engineers, scientists and researchers working in the development of imaging devices, software and solutions. It is also a valuable reference for students of color and imaging science, as well as photographers, graphic designers and artists.

Unlike many other art books only give recipes for mixing colors or describe step-by-step painting techniques, *Color and Light* answers the questions that realist painters continually ask, such as: "What happens with sky colors at sunset?", "How do colors change with distance?", and "What makes a form look three-dimensional?" Author James Gurney draws on his experience as a plain-air painter and science illustrator to share a wealth of information about the realist painter's most fundamental tools: color and light. He bridges the gap between abstract theory and practical knowledge for traditional and digital artists of all levels of experience.

To enhance the overall viewing experience (for cinema, TV, games, AR/VR) the media industry is continuously striving to improve image quality. Currently the emphasis is on High Dynamic Range (HDR) and Wide Colour Gamut (WCG) technologies, which yield images with greater contrast and more vivid colours. The uptake of these technologies, however, has been hampered by the significant challenge of understanding the science behind visual perception. Vision Models for High Dynamic Range and Wide Colour Gamut Imaging provides university researchers and graduate students in computer science, computer engineering, vision science, as well as industry R&D engineers, an insight into the science and methods for HDR and WCG. It presents the underlying principles and latest practical methods in a detailed and accessible way, highlighting how the use of vision models is a key element of all state-of-the-art methods for these emerging technologies. - Presents the underlying vision science principles and models that are essential to the emerging technologies of HDR and WCG - Explores state-of-the-art techniques for tone and gamut mapping - Discusses open challenges and future directions of HDR and WCG research

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.

Color Image Processing: Methods and Applications embraces two decades of extraordinary growth in the technologies and applications for color image processing. The book offers comprehensive coverage of state-of-the-art systems, processing techniques, and emerging applications of digital color imaging. To elucidate the significant progress in specialized areas, the editors invited renowned authorities to address specific research challenges and recent trends in their area of expertise. The book begins by focusing on color fundamentals, including color management, gamut mapping, and color constancy. The remaining chapters detail the latest techniques and approaches to contemporary and traditional color image processing and analysis for a broad spectrum of sophisticated applications, including: Vector and semantic processing Secure imaging Object recognition and feature detection Facial and retinal image analysis Digital camera image processing Spectral and superresolution imaging Image and video colorization Virtual restoration of artwork Video shot segmentation and surveillance Color Image Processing: Methods and Applications is a versatile resource that can be used as a graduate textbook or as stand-alone reference for the design and the implementation of various image and video processing tasks for cutting-edge applications. This book is part of the Digital Imaging and Computer Vision series.

The goal of the work reported in this dissertation is to develop methods for the acquisition and reproduction of high quality digital color images. To reach this goal it is necessary to understand and control the way in which the different devices involved in the entire color imaging chain treat colors. Therefore we addressed the problem of colorimetric characterization of scanners and printers, providing efficient and colorimetrically accurate means of conversion between a device-independent color space such as the CIELAB space, and the device-dependent color spaces of a scanner and a printer.