Statistical and structural information backed full-reference quality measure of compressed sonar images

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.This work was supported in part by the National Natural Science Foundation of China under Grant 61571377, Grant 61871336, Grant 61527804, and Grant 61703009, in part by the Beijing Advanced Innovation Center for Future Internet Technology under Grant 110000546619001, in part by the Young Elite Scientist Sponsorship Program by China Association for Science and Technology under Grant 2017QNRC001, and in part by the Nova Programme Interdisciplinary Cooperation Project under Grant Z161100004916041