Lossless compression of multispectral image data

Nasir D. Memon, Khalid Sayood, Spyros S. Magliveras

Research output: Contribution to journalArticle

Abstract

While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. In this paper, we present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a predictive tree, which defines a noncausal prediction model for an image. We present a backward adaptive technique and a forward adaptive technique. We then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains a minimal.

Original languageEnglish (US)
Pages (from-to)282-289
Number of pages8
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume32
Issue number2
DOIs
StatePublished - Mar 1994

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multispectral image
Image compression
spectral correlation
compression
Spectral resolution
spectral resolution
savings
high resolution
predictions
approximation
prediction
simulation

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Geochemistry and Petrology
  • Geophysics
  • Electrical and Electronic Engineering

Cite this

Lossless compression of multispectral image data. / Memon, Nasir D.; Sayood, Khalid; Magliveras, Spyros S.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 2, 03.1994, p. 282-289.

Research output: Contribution to journalArticle

Memon, Nasir D. ; Sayood, Khalid ; Magliveras, Spyros S. / Lossless compression of multispectral image data. In: IEEE Transactions on Geoscience and Remote Sensing. 1994 ; Vol. 32, No. 2. pp. 282-289.
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