No-reference quality assessment of underwater image enhancement

Abstract

Due to the attenuation and scattering of light in the water medium, real-world underwater images usually suffer from diverse quality defects, such as color casts, low contrast, and reduced visibility, etc. These quality defects accordingly cause adverse effects on underwater images in practical applications. To tackle the problem, many underwater image enhancement (UIE) techniques have been proposed for improving the quality of raw underwater images, showing heterogeneous performances regarding the enhanced results. Therefore, designing an objective quality metric that can effectively predict the visual quality of enhanced underwater images is desirable. In this paper, we propose a highly efficient yet accurate no-reference quality assessment method to evaluate different UIE results by analyzing the statistics of underwater images. Specifically, we first extract quality-aware features in terms of three key aspects: (1) colorfulness; (2) contrast; (3) visibility. Then, a quality regression model is trained to map the extracted features to subjective scores for enhanced underwater images. Given a testing underwater image, we also first extract its corresponding quality-aware feature vector and feed it into the trained quality regression model for quality prediction. We conduct extensive experiments on two databases to demonstrate the superiority of our proposed approach. The code is available at https://github.com/yia-yuese/NR-UIQA.