Underwater image quality assessment (UIQA) is hindered by complex degradation and domain shifts across aquatic environments. Existing no-reference IQA methods rely on costly and subjective mean opinion scores (MOS), which limit their generalization to unseen domains. To overcome these challenges, we propose SCUIA, an unsupervised UIQA framework leveraging semantic contrastive learning for quality prediction without human annotations. Specifically, we introduce a vision-language contrastive learning strategy that aligns image features with textual embeddings in a unified semantic space, capturing implicit degradation-quality correlations. We further enhance quality discrimination with a hierarchical contrastive learning mechanism that combines image-specific statistical priors and semantic prompts. A triplet-based inter-group contrastive loss explicitly models relative quality relationships. To tackle cross-domain variations, we develop an unsupervised domain adaptation module that uses local statistical features to guide CLIP fine-tuning to disentangle domain-invariant quality representations from domain-specific noise. This enables zero-shot cross-domain quality prediction without labeled data. Extensive experiments on public UIQA benchmarks demonstrate significant improvements over existing methods, highlighting superior generalization and domain adaptability.
Zhou et al. (Thu,) studied this question.
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