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Free Content Sneaking Scientific Validity into Imaging Tools for the Masses

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Consider how imaging is used commonly by various professionals. Photographers are just as likely to use a digital camera as one loaded with film. Although they expect the images to look different, they may not know why they do. If they want them to match, they probably don't know how, or they won't know why they might never exactly match. A graphic designer works with direct-capture digital-image data, scanned photography, and synthetically rendered imagery. (The same is true for a movie editor.) In the long run, it is not cost effective to visually edit each image iteratively until they appear to originate from a single imaging device. A computer scientist may be expected to understand all computer peripherals, including digital-imaging devices such as scanners, displays, and printers. They may need to deal with setting up the color preferences in Photoshop, creating ICC color profiles, and using various hardware and software to achieve acceptable color quality. A librarian is expected to handle digital image archives as seamlessly as a collection of books. An art historian viewing a downloaded image from a museum website (or any image database) assumes that their perceptions are similar to viewing the actual work of art. An archivist is expected to digitize photographic reproductions such that the digital archive is an accurate color reproduction of the original work of art. Professionals dealing with digital imaging are suddenly expected to have the expertise of knowledgeable color imaging scientists and engineers. (Many of these professionals may not even realize that there is such a thing as an imaging or color scientist.) These practices, assumptions, and expectations are real. Are they realistic? Are they achievable? To a large extent, they are realistic and achievable by treating imaging as an analytical tool, a scientific tool. How can typical imaging practices become scientific imaging practices? This is the subject of this keynote address. The following will be discussed: Education in imaging and color science, Review of the human visual system, Input device spectral sensitivity, bits and encoding, color management, image quality metrics, characterization and calibration targets, multi-channel visible spectrum imaging, metadata, and standards.
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Document Type: Research Article

Publication date: January 1, 2002

More about this publication?
  • Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual papers for details.

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