Multitopic underwater image quality assessment with visual attention factors

Abstract

Statistical information on underwater images related to subjective quality perception differs based on the environment. We initially propose whitish, yellowish, greenish, bluish topics for underwater images obtained by applying an integrated color distribution, that is, images under the same topic have the same hue. Inspired by the local multiscale, multidirectional statistical characteristics of the structure, contrast, and color of underwater images, we constructed visual attention factor sets for the above four typical underwater environment topics. Furthermore, a multitopic underwater image quality evaluation (MTUIQE) model is proposed to measure mix distortions in different underwater images. The method of measuring image quality based on image topic is proven to be more effective than directly fitting all the natural scene statistics features to the mean opinion scores. Experiments were conducted to show that the proposed MTUIQE approach is superior to existing objective underwater image quality evaluation methods. Compared with six commonly used blind image quality evaluation methods, the Pearson linear correlation coefficient and Spearman rank-order correlation coefficient are increased by more than 10%, which means that the quality assessment model adapted to specific degradation topics is more consistent with subjective underwater image quality perception, and it can effectively evaluate the performance of underwater image enhancement and restoration methods.