In sonar applications, important information such as distributions of minerals, underwater creatures has a high probability of being contained in sonar images. In many underwater applications such as underwater rescue and biometric tracking, it is necessary to send sonar images underwater for further analysis. Due to the bad conditions of underwater acoustic channel and current underwater acoustic communication technologies, sonar images very possibly suffer from several typical types of distortions. As far as we know, limited efforts have been made to gather meaningful sonar image databases and benchmark reliable objective quality model, so far. This paper develops a new objective sonar image quality predictor (SIQP), whose core is the combination of two features specific to a quality measure of sonar images. These two features, which come from statistical and structural information inspired by the characteristics of sonar images and the human visual system, reflect image quality from the global and detailed aspects. The performance comparison of the proposed metric with popular and prevailing quality evaluation models is conducted using a newly established sonar image quality database. The results of experiments show the superiority of our SIQP metric over the available quality evaluation models.

中文翻译：

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压缩声纳图像的统计和结构信息支持的全参考质量测量

在声纳应用中，矿物分布、水下生物等重要信息极有可能包含在声纳图像中。在许多水下应用中，例如水下救援和生物识别跟踪，有必要在水下发送声纳图像以进行进一步分析。由于水声信道条件和当前水声通信技术的恶劣，声纳图像极有可能出现几种典型的畸变。据我们所知，到目前为止，在收集有意义的声纳图像数据库和基准可靠的客观质量模型方面所做的努力有限。本文开发了一种新的客观声纳图像质量预测器 (SIQP)，其核心是结合特定于声纳图像质量度量的两个特征。这两个特点，受声纳图像特征和人类视觉系统启发，来自统计和结构信息，从全局和细节方面反映图像质量。使用新建立的声纳图像质量数据库对所提出的度量与流行和流行的质量评估模型进行性能比较。实验结果表明我们的 SIQP 指标优于可用的质量评估模型。