Entropy-constrained predictive compression of SAR raw data

Enrico Magli; Gabriella Olmo

doi:10.1117/12.454829

30 January 2003 Entropy-constrained predictive compression of SAR raw data

Enrico Magli, Gabriella Olmo

Proceedings Volume 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications; (2003) https://doi.org/10.1117/12.454829
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

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.

Citation Download Citation

Enrico Magli and Gabriella Olmo "Entropy-constrained predictive compression of SAR raw data", Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003); https://doi.org/10.1117/12.454829

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