Color Gamut Mapping (The Wiley-IS&T Series in Imaging Science and Technology) - Hardcover

Morovič, Ján

Hardcover

ISBN 10: 0470030321 ISBN 13: 9780470030325

Verlag: Wiley, 2008

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

Dr Ján Morovi , Hewlett-Packard Espanola, S. L, Avda. Grealls, Barcelona, Spain
Ján Morovi is currently a Senior Color Scientist at Hewlett-Packard Espanola, Barcelona, Spain. He is highly regarded in the field of colour science, being Chair of the CIE (International Commission on Illumination) technical committee 8-03 on gamut mapping, a post he has held since 1998. Before his appointment in industry for Hewlett-Packard, Dr Morovi has also had academic experience at the University of Derby, UK. He has delivered lectures to an international audience, both academic and industrial, led industrially funded projects, and developed new commercial technologies. In addition, he has authored 62 academic publications, including book chapters, journal, and conference papers on colour gamut mapping, and also helped to create a CIE technical report detailing guidelines for the Evaluation of Mapping Algorithms.

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.

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.

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Color Gamut Mapping

By Jn Morovic

John Wiley & Sons

Copyright © 2008 John Wiley & Sons, Ltd
All right reserved.
ISBN: 978-0-470-03032-5

Chapter One

Introduction

1.1 WHAT IS COLOR GAMUT MAPPING?

On an average day most of us will come across a myriad of visual content generated using an ever-increasing variety of means. Already at the moment of waking up we may be presented with the flashing digits of an unwelcome alarm clock, accompanied by a sharp burst of light from a window or by a gradual emergence of shapes in the dark. During breakfast we may browse a newspaper or watch the news on a television. We may check our email using a personal computer or send a message, photo or video using our mobile phones and on the way out catch a glimpse of the latest artwork of our kids stuck to a fridge door or glance at some junk mail.

This scenario, which is so everyday as to be unremarkable, is an example of the ubiquity and variety of sources of visual content in our lives. We are regularly exposed to at least natural imagery, i.e. our homes themselves, whose reflective surfaces can be lit by natural or artificial light sources. Besides, we almost certainly view the output of traditional, analog imaging technologies such as drawings, print, photography and television and we regularly interact with digital imaging devices such as mobile phones and personal computers. In the process of work and leisure we also come across other imaging technologies, including digital projectors, cameras and printers. Many of us, therefore, are likely to have already used many, if not all, imaging technologies developed to date.

Reflecting on the diversity of visual content in our environment brings us to the observation that a variety of means can represent the same image and that these means, therefore, need to communicate among themselves. For example, we may wish to capture a moment from a birthday party (Figure 1.1) by taking a picture of it using a digital camera. Supposing we like the picture a lot we may also email it to our friends, send it to others via a mobile phone, print it out on a desktop printer, place it on a website, have a larger version of it printed in a copy-shop and include it in a presentation stored on a DVD and viewed on a television or projected onto a screen. Here, the same content (the scene from the birthday party) is present in at least 10 instances. Depending on how many friends we email and how many visitors come to the website, this number can be a lot larger.

Next, let us think about how we would like the various instances of a given image to relate to each other. Clearly, the simplest and most immediate answer is that we want them all to look the same. In other words, we would like to see 'the same' when we look at the photo on the website and the print we made on a printer. Furthermore, we would like both of these to be 'the same' as when we looked at the actual scene during the birthday party. In some cases, though, we are less interested in an accurate record of an event and instead prefer to have an image that 'looks nice.' If the birthday party took place on an overcast day and everything looked a bit dull at the time, then we may still prefer for a more cheerful appearance to be represented by the images we took of the occasion.

Even though some differences between the various instances of visual content can be considered to be improvements, there are other differences that no one likes to see. Take, for example, the case of receiving the birthday party image and viewing it on your mobile phone in bright daylight. The image is likely to have much lower contrast than the original scene did, so much so that it could be difficult to see altogether. Viewing the image on your personal computer's display could result in a darker result than how you remember the party. Printing the image might give a less colorful appearance than what was shown on your display. So, even if we want all instances of some visual content to 'look the same' or to have changes that make it 'look nice,' there are in practice many other differences between the various instances of an image that are undesirable.

Why is it then that different instances of a given image can look so different? The answer is a rather complex network of interactions among many individual factors, including the following:

1. The instances of an image are viewed by different people. As each one of us responds slightly differently to the light entering our eyes, there will be differences between the experiences two people have when viewing a single image. Furthermore, as soon as we communicate about what we see, our experiences, skills and habits also play a role. When two people talk about a single image and even experience it in the same way, they are likely to express it differently.

2. The instances of an image are viewed under different viewing conditions. If we view two physically identical instances of an image in different environments, then they will look different. For example, viewing a television in the dark can give rise to a greater range of colors (i.e. greater contrast in images, more colorful parts of images, e.g. grass looking more vibrant) than when it is viewed in bright daylight, when it looks a lot duller overall. Note that in this case the television outputs the same image in both cases, but the dark versus bright environment changes its appearance significantly. This is yet more dramatic for images that reflect light (e.g. prints), where in the dark they too are dark and only when more light is present to view them do they acquire a clear appearance. In addition to how much light is present when viewing images, the background of the image is also important (e.g. what color the wall is behind a television), as is the distance at which it is viewed. Finally, it also matters what else is seen when instances of an image are viewed (e.g. whether the paper of a newspaper looks 'white' depends on whether we also see other kinds of paper at the same time) and how the instances are arranged (e.g. whether they immediately next to each other or far apart).

3. The instances of an image are created using different technologies. When the digital data from a camera are displayed on two different displays they are likely to look different, as displays can differ in terms of the materials they use for outputting color as well as in their settings. The digital data - which describe an image as a series of red, green and blue (RGB) values for a grid of spatial locations - give only relative instructions to imaging devices, i.e. the instruction may be to use 100% of a display's red and green colorants and 50% of its blue colorant. However, if the displays have different colorants, then following the same relative instructions will give different results. This constraint can, however, be overcome. The key to the solution is that it is possible to understand the relationship between digital data input to a display and the color appearance of the corresponding output (e.g. we can know what sending RGB = [100%, 100%, 50%] will look like on a given display and we can also work out what RGBs to send to the display if we want a certain color output from it). Then, to get two displays to look the same, we can take the RGBs we send to the first display, work out from them what the corresponding output colors will look like and from these color appearances work out what other...

Details zur Ausgabe

• Verlag: Wiley
• Erscheinungsdatum: 2008
• Sprache: Englisch
• ISBN 10: 0470030321
• ISBN 13: 9780470030325
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 304
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland