Multispectral Image Data Compression Using Classified Prediction and KLT in Wavelet Transform Domain

Tae-Su KIM  Bong-Seok KIM  Seung-Jin KIM  Byung-Ju KIM  Kyung-Nam PARK  Kuhn-Il LEE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E86-A   No.6   pp.1492-1497
Publication Date: 2003/06/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Papers Selected from 2002 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2002))
multispectral image data,  redundancies,  data compression,  

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This paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm in the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3-D SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.