Share Email Print
cover

Proceedings Paper

Entropy-constrained predictive compression of SAR raw data
Author(s): Enrico Magli; Gabriella Olmo
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top