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To improve the quality of underwater images, various kinds of underwaterimage enhancement (UIE) operators have been proposed during the past few years.However, the lack of effective objective evaluation methods limits the furtherdevelopment of UIE techniques. In this paper, we propose a novel rank learningguided no-reference quality assessment method for UIE. Our approach, termedTwice Mixing, is motivated by the observation that a mid-quality image can begenerated by mixing a high-quality image with its low-quality version. Typicalmixup algorithms linearly interpolate a given pair of input data. However, thehuman visual system is non-uniformity and non-linear in processing images.Therefore, instead of directly training a deep neural network based on themixed images and their absolute scores calculated by linear combinations, wepropose to train a Siamese Network to learn their quality rankings. TwiceMixing is trained based on an elaborately formulated self-supervisionmechanism. Specifically, before each iteration, we randomly generate two mixingratios which will be employed for both generating virtual images and guidingthe network training. In the test phase, a single branch of the network isextracted to predict the quality rankings of different UIE outputs. We conductextensive experiments on both synthetic and real-world datasets. Experimentalresults demonstrate that our approach outperforms the previous methodssignificantly.

Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement