Compression of SAR imagery using adaptive residual vector quantization

Nasser M. Nasrabadi; Mahesh Venkatraman; Heesung Kwon

doi:10.1117/12.263293

10 January 1997 Compression of SAR imagery using adaptive residual vector quantization

Nasser M. Nasrabadi, Mahesh Venkatraman, Heesung Kwon

Proceedings Volume 3024, Visual Communications and Image Processing '97; (1997) https://doi.org/10.1117/12.263293
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

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.

Citation Download Citation

Nasser M. Nasrabadi, Mahesh Venkatraman, and Heesung Kwon "Compression of SAR imagery using adaptive residual vector quantization", Proc. SPIE 3024, Visual Communications and Image Processing '97, (10 January 1997); https://doi.org/10.1117/12.263293

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