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Proceedings Paper

Entropy-constrained predictive compression of SAR raw data
Author(s): Enrico Magli; Gabriella Olmo
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Paper Abstract

In this paper we propose to employ entropy-constrained predictive coding for lossy compression of SAR raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. The proposed predictive coding algorithm performs entropy-constrained quantization of the prediction error, followed by entropy coding; the algorithm exhibits a number of advantages, and notably a very high performance gain, with respect to other techniques such as FBAQ or methods based on transform coding. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm significantly outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.

Paper Details

Date Published: 30 January 2003
PDF: 9 pages
Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003); doi: 10.1117/12.454829
Show Author Affiliations
Enrico Magli, Politecnico di Torino (Italy)
Gabriella Olmo, Politecnico di Torino (Italy)


Published in SPIE Proceedings Vol. 4793:
Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications
Mark S. Schmalz, Editor(s)

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