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Proceedings Paper

Compression of SAR imagery using adaptive residual vector quantization
Author(s): Nasser M. Nasrabadi; Mahesh Venkatraman; Heesung Kwon
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Paper Abstract

Compression of SAR imagery for battlefield digitization is discussed in this paper. THe images are first processed to separate out possible target areas. These target areas are compressed losslessly to avoid any degradation of the images. The background information which is usually necessary to establish context, is compressed using a hybrid vector quantization algorithm. An adaptive variable rate residual vector quantizer is use to compress the residual signal generated by a neural network predictor. The vector quantizer codebooks are optimized for entropy coding using an entropy-constrained algorithm to further improve the coding performance. This constrained vector-quantizer combination performs extremely well as suggested by the experimental results.

Paper Details

Date Published: 10 January 1997
PDF: 12 pages
Proc. SPIE 3024, Visual Communications and Image Processing '97, (10 January 1997); doi: 10.1117/12.263293
Show Author Affiliations
Nasser M. Nasrabadi, Army Research Lab. (United States)
Mahesh Venkatraman, SUNY/Buffalo (United States)
Heesung Kwon, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 3024:
Visual Communications and Image Processing '97
Jan Biemond; Edward J. Delp, Editor(s)

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