Underwater Image Quality Assessment method basedon Color Space Multi-feature Fusion

Abstract

The complexity and challenging underwater environment leading to degradation in underwater image. Measuring the qualityof underwater image is a significant step for the subsequent image processing step. Existing Image Quality Assessment(IQA) methods do not fully consider the characteristics of degradation in underwater images, which limits their performance inunderwater image assessment. To address this problem, an Underwater IQA (UIQA) method based on color space multi-featurefusion is proposed to focus on underwater image. The proposed method converts underwater images from RGB color space toCIELab color space, which has a higher correlation to human subjective perception of underwater visual quality. The proposedmethod extract histogram features, morphological features, and moment statistics from luminance and color components anduse multi-feature fusion to better quantify the degradation in underwater image quality. After features extraction, support vectorregression(SVR) is employed to learn the relationship between fusion features and image quality scores, and gain the qualityprediction model. Experimental results on the SAUD dataset and UIED dataset show that our proposed method can performwell in underwater image quality assessment. The performance comparisons on LIVE dataset, TID2013 dataset and SIQADdataset demonstrate the applicability of the proposed method.