Predictor analysis for onboard lossy predictive compression of multispectral and hyperspectral images

Marco Ricci; Enrico Magli

doi:10.1117/1.JRS.7.074591

9 August 2013 Predictor analysis for onboard lossy predictive compression of multispectral and hyperspectral images

Marco Ricci, Enrico Magli

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 074591 (August 2013). https://doi.org/10.1117/1.JRS.7.074591

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.

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Marco Ricci and Enrico Magli "Predictor analysis for onboard lossy predictive compression of multispectral and hyperspectral images," Journal of Applied Remote Sensing 7(1), 074591 (9 August 2013). https://doi.org/10.1117/1.JRS.7.074591

Published: 9 August 2013

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