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Image data compression

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Image Processing, Analysis and Machine Vision

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Abstract

Image processing is often very difficult due to the large amounts of data used to represent an image. Technology permits ever-increasing image resolution (spatially and in grey levels), and increasing numbers of spectral bands, and there is a consequent need to limit the resulting data volume. Consider an example from the remote sensing domain where image data compression is a very serious problem. A Landsat D satellite broadcasts 85 × 106 bits of data every second and a typical image from one pass consists of 6100 × 6100 pixels in 7 spectral bands — in other words 260 megabytes of image data. A Japanese Advanced Earth Observing Satellite (ADEOS) will be launched in 1994 with the capability of observing the Earth’s surface with a spatial resolution of 8 metres for the polychromatic band and 16 metres for the multispectral bands. The transmitted data rate is expected to be 120 Mbps [Arai 90]. Thus the amount of storage media needed for archiving of such remotely sensed data is enormous. One possible way how to decrease the necessary amount of storage is to work with compressed image data.

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Author information

Authors and Affiliations

  1. University of Iowa, Iowa City, Iowa, USA

    Milan Sonka PhD

  2. Czech Technical University, Prague, Czech Republic

    Vaclav Hlavac PhD

  3. University of Leeds, Leeds, England

    Roger Boyle DPhil, MBCS, CEng

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  1. Milan Sonka PhD
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  2. Vaclav Hlavac PhD
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  3. Roger Boyle DPhil, MBCS, CEng
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© 1993 Milan Sonka, Vaclav Hlavac and Roger Boyle

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Sonka, M., Hlavac, V., Boyle, R. (1993). Image data compression. In: Image Processing, Analysis and Machine Vision. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-3216-7_12

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  • DOI: https://doi.org/10.1007/978-1-4899-3216-7_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-412-45570-4

  • Online ISBN: 978-1-4899-3216-7

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