Sonar Feature-Based Bandwidth Compression

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.