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4 - Digital image coding

Published online by Cambridge University Press:  26 January 2010

Jenq-Neng Hwang
Affiliation:
University of Washington
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Summary

Image compression is the application of data compression techniques to two-dimensional digital images I(x, y), to reduce the redundancy of the image data for storage or transmission in an efficient form. Image compression can be classified into two categories: lossless or lossy. Lossless compression, which achieves smaller compression ratios than lossy compression, mainly takes advantage of the image contents containing a non uniform probability distribution for a variable-length representation of the image pixels. Such images include technical drawings, icons or comics, and high-value contents such as medical imagery or image scans made for archival purposes. However, lossy compression methods, especially when they achieve a very high compression ratio, can introduce compression artifacts. Nevertheless, lossy compressions are especially suitable for natural images, such as photos, in applications where a minor (sometimes imperceptible) loss of fidelity is acceptable when it is desirable to achieve a substantial reduction in bitrate. Most of the stateof-the-art image compression standards use a combination of lossy and lossless algorithms to achieve the best performance.

The Joint Photographic Experts Group (JPEG) [1], a discrete cosine transform(DCT)-based technique, is the most widely used standardized lossy image compression mechanism; it was designed for compressing either full-color or gray-scale images of natural, real-world, scenes. It works well for photographs, naturalistic artwork and similar material if the compression ratio is about 20 : 1, which is much better than the 4 : 1 compression ratio provided by a lossless compression method such as the Graphics Interchange Format (GIF) [2]. However, it does not work so well for lettering, simple cartoons, or line drawings.

Type
Chapter
Information
Multimedia Networking
From Theory to Practice
, pp. 62 - 106
Publisher: Cambridge University Press
Print publication year: 2009

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  • Digital image coding
  • Jenq-Neng Hwang, University of Washington
  • Book: Multimedia Networking
  • Online publication: 26 January 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511626654.005
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  • Digital image coding
  • Jenq-Neng Hwang, University of Washington
  • Book: Multimedia Networking
  • Online publication: 26 January 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511626654.005
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Digital image coding
  • Jenq-Neng Hwang, University of Washington
  • Book: Multimedia Networking
  • Online publication: 26 January 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511626654.005
Available formats
×