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Proceedings Paper

Bandwidth reduction of high-frequency sonar imagery in shallow water using content-adaptive hybrid image coding
Author(s): Frances B. Shin; David H. Kil
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Paper Abstract

One of the biggest challenges in distributed underwater mine warfare for area sanitization and safe power projection during regional conflicts is transmission of compressed raw imagery data to a central processing station via a limited bandwidth channel while preserving crucial target information for further detection and automatic target recognition processing. Moreover, operating in an extremely shallow water with fluctuating channels and numerous interfering sources makes it imperative that image compression algorithms effectively deal with background nonstationarity within an image as well as content variation between images. In this paper, we present a novel approach to lossy image compression that combines image- content classification, content-adaptive bit allocation, and hybrid wavelet tree-based coding for over 100:1 bandwidth reduction with little sacrifice in signal-to-noise ratio (SNR). Our algorithm comprises (1) content-adaptive coding that takes advantage of a classify-before-coding strategy to reduce data mismatch, (2) subimage transformation for energy compaction, and (3) a wavelet tree-based coding for efficient encoding of significant wavelet coefficients. Furthermore, instead of using the embedded zerotree coding with scalar quantization (SQ), we investigate the use of a hybrid coding strategy that combines SQ for high-magnitude outlier transform coefficients and classified vector quantization (CVQ) for compactly clustered coefficients. This approach helps us achieve reduced distortion error and robustness while achieving high compression ratio. Our analysis based on the high-frequency sonar real data that exhibit severe content variability and contain both mines and mine-like clutter indicates that we can achieve over 100:1 compression ratio without losing crucial signal attributes. In comparison, benchmarking of the same data set with the best still-picture compression algorithm called the set partitioning in hierarchical trees (SPIHT) reveals that some weak targets can completely disappear in certain situations because SPIHT is not content adaptive.

Paper Details

Date Published: 4 September 1998
PDF: 12 pages
Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324198
Show Author Affiliations
Frances B. Shin, Lockheed Martin Corp. (United States)
David H. Kil, Lockheed Martin Corp. (United States)


Published in SPIE Proceedings Vol. 3392:
Detection and Remediation Technologies for Mines and Minelike Targets III
Abinash C. Dubey; James F. Harvey; J. Thomas Broach, Editor(s)

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