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Proceedings Paper

Sonar feature-based bandwidth compression
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Paper Abstract

A sonar bandwidth compression (BWC) technique which, unlike conventional methods, adaptively varies the coding resolution in the compression process based on a priori information is described. This novel approach yields a robust compression system whose performance exceeds the conventional methods by factors of 2-to-1 and 1.5-to-1 for display- formatted and time series sonar data, respectively. The data is first analyzed by a feature extraction routine to determine those pixels of the image that collectively comprise intelligence-bearing signal features. The data is then split into a foreground image which contains the extracted source characteristic and a larger background image which is the remainder. Since the background image is highly textured, it suffices to code only the local statistics rather than the actual pixels themselves. This results in a substantial reduction of the bit rate required to code the background image. The feature-based compression algorithm developed for sonar imagery data is also extended to the sonar time series data via a novel approach involving an initial one-dimensional DCT transformation of the time series data before the actual compression process. The unique advantage of this approach is that the coding is done in an alternative two-dimensional image domain where, unlike the original time domain, it is possible to observe, differentiate, and prioritize essential features of data in the compression process. The feature-based BWC developed for sonar data is potentially very useful for applications involving highly textured imagery. Two such applications are synthetic aperture radar and ultrasound medical imaging.

Paper Details

Date Published: 9 July 1992
PDF: 20 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138240
Show Author Affiliations
John A. Saghri, Lockheed Palo Alto Research Lab. (United States)
Andrew G. Tescher, Lockheed Palo Alto Research Lab. (United States)


Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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