As an acoustic communications medium, water is characterized by frequency dependent attenuation, short range, very low bandwidth, scattering, and multi-path. It is generally difficult to acoustically communicate even terse messages underwater much less images. For the naval mine counter-measures mission, there is value in transmitting images of mine-like objects, acquired by side-scan sonar on-board unmanned underwater vehicles, to the above-water operator for review. The contribution of this paper is a methodology and implementation, based on vector quantization, to compress and transmit snippets of side-scan sonar images from underway unmanned underwater vehicles to an operator. The work has been validated through controlled indoor tank tests and several at-sea trials. The fidelity of the received images is such that trained operators can recognize targets in the received images as well as they would have in the original images. Future work investigates machine learning to improve the compression basis and psycho-visual studies for the specialized skill of feature recognition in sonar images.

中文翻译：

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无人水下航行器传输图像

作为一种声学通信介质，水的特征在于依赖于频率的衰减，短距离，非常低的带宽，散射和多径。通常很难在水下用声音传达甚至是简短的消息，也要传递少得多的图像。对于海军的地雷对策任务，有价值的是将由侧面扫描声纳车载无人水下航行器获取的类地雷的图像传输给水上操作员进行审查。本文的贡献是一种基于矢量量化的方法和实现，可以将侧面扫描声纳图像的摘要从正在行驶的无人水下航行器压缩并传输给操作员。该工作已通过受控室内储罐测试和几次海上试验得到了验证。接收到的图像的保真度使得受过训练的操作员可以识别接收到的图像中的目标以及原始图像中的目标。未来的工作将研究机器学习以改善压缩基础，并进行心理视觉研究，以获取声纳图像中特征识别的专业技能。