NDLI: A remapping technique based on permutations for lossless compression of multispectral images

Content Provider	IEEE Xplore Digital Library
Author	Arnavut, Z.
Copyright Year	1997
Description	Author affiliation: Remote Sensing Appl. Labs., Nebraska Univ., Omaha, NE, USA (Arnavut, Z.)
Abstract	Multispectral images, such as Thematic Mapper (TM) images, have high spectral correlation among some bands. These bands also have different dynamic ranges. Hence, when linear predictive techniques employed to exploit the spectral and spatial correlation among the bands of a TM image, the variance of the prediction errors becomes greater. Markas and Reif (1993), have used histogram equalization (modification) techniques for lossy compression of multispectral images. In general, histogram equalization techniques are not reversible. However, by defining a monotonically increasing transformation, so that two adjacent gray values will not map to the same gray value of the transformed image, and selecting a target image with a wider probability density function than the source image, one can define a reversible mapping. We introduce a distinct reversible remapping scheme which utilizes sorting permutations. This technique differs from histogram equalization. It is a reversible transformation. We show that, by utilizing the remapping technique introduced and employing linear predictive techniques on a pair of bands, one can achieve better lossless compression than the results reported previously.
Starting Page	407
Ending Page	416
File Size	563099
Page Count	10
File Format	PDF
ISBN	0818677619
ISSN	10680314
DOI	10.1109/DCC.1997.582067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-03-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Multispectral imaging Remote sensing Histograms Pixel Data compression Image reconstruction Satellites Transform coding Laboratories
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications

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