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In this paper, we present a ranking-based underwater image quality assessment(UIQA) method, abbreviated as URanker. The URanker is built on the efficientconv-attentional image Transformer. In terms of underwater images, we speciallydevise (1) the histogram prior that embeds the color distribution of anunderwater image as histogram token to attend global degradation and (2) thedynamic cross-scale correspondence to model local degradation. The finalprediction depends on the class tokens from different scales, whichcomprehensively considers multi-scale dependencies. With the margin rankingloss, our URanker can accurately rank the order of underwater images of thesame scene enhanced by different underwater image enhancement (UIE) algorithmsaccording to their visual quality. To achieve that, we also contribute adataset, URankerSet, containing sufficient results enhanced by different UIEalgorithms and the corresponding perceptual rankings, to train our URanker.Apart from the good performance of URanker, we found that a simple U-shape UIEnetwork can obtain promising performance when it is coupled with ourpre-trained URanker as additional supervision. In addition, we also propose anormalization tail that can significantly improve the performance of UIEnetworks. Extensive experiments demonstrate the state-of-the-art performance ofour method. The key designs of our method are discussed. We will release ourdataset and code.

Underwater Ranker: Learn Which Is Better and How to Be Better