22 July 1997 Lossy compression of acoustic backscatter data
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We develop lossy compression algorithms for underwater acoustic data and evaluate the effects of the compression on two applications: target-detection and study of ocean floor temperature. We use data from an experiment of sediment transport conducted by the Applied Physics Laboratory at the University of Washington. We apply a variety of wavelet- based vector quantization data compression algorithms to acoustic sonar scans. We sue pruned tree-structured vector quantization (PTSVQ) with the generalized Breiman, Friedman, Olshen, and Stone algorithm to simultaneously prune trees that correspond to different wavelet subbands. We determine that while targets can be detected at compression ratios of over 100:1, compression ratios greater than 4:1 lead to unacceptable loss in accuracy for use of the data in ocean floor temperature studies. We find that PTSVQ applied to wavelet coefficients uniformly gives better results at low bit rates than PTSVQ applied to the untransformed data. For target detection, better compression is obtained by compressing in polar coordinates while for ocean temperature measurement, better compression is obtained by compressing in Cartesian coordinates. Finally although SNR versus entropy measurements are a popular and easy way of measuring the success of compression experiments, they are not good predictors of compression performance for scientific data.
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Jill R. Goldschneider, Jill R. Goldschneider, Andrew G. Bruce, Andrew G. Bruce, Don B. Percival, Don B. Percival, } "Lossy compression of acoustic backscatter data", Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); doi: 10.1117/12.280855; https://doi.org/10.1117/12.280855

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