NDLI: Transform Based Lossy Compression of Multispectral Images

Content Provider	Springer Nature Link
Author	Kaarna, A. Parkkinen, J.
Copyright Year	2001
Abstract	We have composed compression methods, which are based on the one-, two- and three-dimensional wavelet transforms. In addition, clustering the spectra of the image is considered. These compression methods, combining compression in the spectral and spatial domains, are compared by using a dataset of 65 multispectral images. The experimental results show that, at low compression ratios, the best method is based on the three-dimensional wavelet transform, where the spatial and spectral dimensions are transformed simultaneously. At higher compression ratios, a high quality wavelet-based compression method in the spatial dimensions combined with the principal component analysis in the spectral dimension gives the best results. This means that components of compressed images can be used as features in pattern analysis applications. The selection of the compression parameters is image-dependent. The results also show that, for multispectral images, both spectral and spatial compression could be used, and for best results, optimal bit allocation in encoding should be found. The results from the methods suggested are compared to the results found in the literature.
Starting Page	39
Ending Page	50
Page Count	12
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	4
Issue Number	1
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2014-04-11
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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