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Proceedings Paper

Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length
Author(s): Jarno Mielikainen
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Paper Abstract

This paper extends clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In C-DPCM method the spectra of a hyperspectral image is clustered, and an optimized predictor is calculated for each cluster. Prediction is performed using a linear predictor. After prediction, the difference between the predicted and original values is computed. The difference is entropy-coded using an adaptive entropy coder for each cluster. The proposed use of adaptive prediction length is shown have lower bits/pixel value than the original C-DPCM method for new AVIRIS test images. Both calibrated are uncalibrated images showed improvement over fixed prediction length.

Paper Details

Date Published: 24 August 2010
PDF: 7 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100M (24 August 2010); doi: 10.1117/12.863227
Show Author Affiliations
Jarno Mielikainen, Yonsei Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

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