30 January 2003 Entropy-constrained predictive compression of SAR raw data
Author Affiliations +
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Enrico Magli, Enrico Magli, Gabriella Olmo, 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); doi: 10.1117/12.454829; https://doi.org/10.1117/12.454829
PROCEEDINGS
9 PAGES


SHARE
Back to Top