Transform Based Lossy Compression of Multispectral Images |
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Authors: | A Kaarna J Parkkinen |
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Affiliation: | (1) Lappeenranta University of Technology, Department of Information Technolofy, Lappeenranta, Finland, FI;(2) University of Joensuu, Department of Computer Science, Joensuu, Finland, FI |
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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. |
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Keywords: | :Bit allocation Clustering Lossy image compression Multispectral images Principal component analysis Wavelet transform |
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