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Proceedings Paper

Analysis of compression methods applied to hyperspectral images
Author(s): Joan S. Serra-Sagrista; David Gavilan; Julian Minguillon
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Paper Abstract

Several well-known methods for lossy compression of still images are here analyzed to evaluate their performance for hyperspectral images. The lossy compression methods discussed are the JPEG standard, and four approaches based on the Wavelet Transform: the Embedded coding of ZeroTree wavelet coefficients, the Set Partitioning in Hierarchical Trees, a Lattice Vector Quantizer, and the new JPEG2K. Experiments are first performed on corpuses of natural grayscale still images to provide a general framework of the performance of each method. Then experiments are performed on several hyperspectral images taken with CASI and AVIRIS sensors. Experiments show that it is possible to employ the basic lossy compression methods for hyperspectral image coding. The wavelet-based approaches produce results consistently better than the JPEG: JPEG can not achieve compression ratios above 75:1; on the other side, with EZT, SPIHT and LVQ compression ratios of 250:1 or higher may be reached. For JPEG2K, higher compression ratios than JPEG may also be reached, but with a PSNR quality lower than the three other techniques. At compression ratios about 8:1, the wavelet methods yield results 1.5 dB better than those of JPEG. These results help to explain why JPEG2K standard uses the WT instead of the DCT.

Paper Details

Date Published: 13 March 2003
PDF: 12 pages
Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); doi: 10.1117/12.463139
Show Author Affiliations
Joan S. Serra-Sagrista, Univ. Autonoma de Barcelona (Spain)
David Gavilan, Univ. Autonoma de Barcelona (Spain)
Julian Minguillon, Univ. Autonoma de Barcelona (Spain)

Published in SPIE Proceedings Vol. 4885:
Image and Signal Processing for Remote Sensing VIII
Sebastiano B. Serpico, Editor(s)

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