Publication

Color Strategies for Image Databases

Sabine Süsstrunk
2001
Conference Papers

Abstract

In this paper, color encoding strategies for different image database applications are discussed. The color image workflow is examined in detail, and master and derivative file encoding strategies are outlined in relation to capture, maintenance, and deployment of image files. For the most common image database purposes, recommendations are given as to which type of color encoding is most suitable. Advantages and disadvantages of sensor, input-referred, output-referred, and output device specific color encodings are discussed in relation with image usage. The role of ICC color management in an image database environment is also considered.

Official source

https://infoscience.epfl.ch/entities/publication/07b6bdc9-cb9b-40ec-94ad-357d8941885f

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Sabine Süsstrunk
2001
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/07b6bdc9-cb9b-40ec-94ad-357d8941885f

About this result

Related concepts (22)

Color management

In digital imaging systems, color management (or colour management) is the controlled conversion between the color representations of various devices, such as s, digital cameras, monitors, TV screens, film printers, computer printers, offset presses, and corresponding media. The primary goal of color management is to obtain a good match across color devices; for example, the colors of one frame of a video should appear the same on a computer LCD monitor, on a plasma TV screen, and as a printed poster.

Raw image format

A camera raw image file contains unprocessed or minimally processed data from the of either a digital camera, a motion picture film scanner, or other . Raw files are so named because they are not yet processed, and contain large amounts of potentially redundant data. Normally, the image is processed by a raw converter, in a wide-gamut internal color space where precise adjustments can be made before to a viewable file format such as JPEG or PNG for storage, printing, or further manipulation.

Content-based image retrieval

Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the problem, that is, the problem of searching for s in large databases (see this survey for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see ). "Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image.

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