Lossless Compression of Hyperspectral Images Using Clustered Linear Prediction With Adaptive Prediction Length

This letter explores the use of adaptive prediction length in clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In the C-DPCM method, linear prediction is performed using coefficients optimized for each spectral cluster separately. The differ...

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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 9; H. 6; S. 1118 - 1121
Hauptverfasser: Mielikainen, Jarno, Huang, Bormin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2012
Schlagworte:
Algorithm design and analysis
Bit rate
Hyperspectral images
Hyperspectral imaging
Image coding
image compression
lossless compression
Prediction algorithms
Table lookup
ISSN:1545-598X, 1558-0571
Online-Zugang:Volltext
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Zusammenfassung:This letter explores the use of adaptive prediction length in clustered differential pulse code modulation (C-DPCM) lossless compression method for hyperspectral images. In the C-DPCM method, linear prediction is performed using coefficients optimized for each spectral cluster separately. The difference between the predicted and original values is entropy coded using an adaptive range coder for each cluster. The results show that the C-DPCM-with-adaptive-prediction-length method has lower bit-per-pixel value than the original C-DPCM method for Consultative Committee for Space Data Systems 2006 AVIRIS test images. Both calibrated and uncalibrated image compression results are improved by adaptive prediction length.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2012.2191531