Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length

Jarno Mielikainen

doi:10.1117/12.863227

24 August 2010 Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length

Jarno Mielikainen

Author Affiliations +

Proceedings Volume 7810, Satellite Data Compression, Communications, and Processing VI; 78100M (2010) https://doi.org/10.1117/12.863227
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

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.

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Jarno Mielikainen "Clustered linear prediction for lossless compression of hyperspectral images using adaptive prediction length", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100M (24 August 2010); https://doi.org/10.1117/12.863227

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KEYWORDS

Hyperspectral imaging

Image compression

Calibration

Current controlled current source

Data communications

Data compression

Lithium

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