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Journal of Applied Remote Sensing

Predictor analysis for onboard lossy predictive compression of multispectral and hyperspectral images
Author(s): Marco Ricci; Enrico Magli
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Paper Abstract

The predictive lossy compression paradigm, which is emerging as an interesting alternative to conventional transform coding techniques, is studied. We first discuss this paradigm and outline the advantages and drawbacks with respect to transform coding. Next, we consider two low-complexity predictors and compare them under equal conditions on a large set of multispectral and hyperspectral images. Besides their rate-distortion performance, we attempt to gain some insight on the “quality” of the prediction residuals, comparing bit-rate and variance, and calculating the kurtosis. The results allow us to outline the directions for improvement of the algorithms, mainly in the treatment of noisy channels and the use of appropriate statistical models for the entropy-coding stage.

Paper Details

Date Published: 9 August 2013
PDF: 15 pages
J. Appl. Remote Sens. 7(1) 074591 doi: 10.1117/1.JRS.7.074591
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Marco Ricci, Politecnico di Torino (Italy)
Enrico Magli, Politecnico di Torino (Italy)

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