  No-Reference Image Quality Assessment (NR-IQA) focuses on designing methods
to measure image quality in alignment with human perception when a high-quality
reference image is unavailable. The reliance on annotated Mean Opinion Scores
(MOS) in the majority of state-of-the-art NR-IQA approaches limits their
scalability and broader applicability to real-world scenarios. To overcome this
limitation, we propose QualiCLIP (Quality-aware CLIP), a CLIP-based
self-supervised opinion-unaware method that does not require labeled MOS. In
particular, we introduce a quality-aware image-text alignment strategy to make
CLIP generate representations that correlate with the inherent quality of the
images. Starting from pristine images, we synthetically degrade them with
increasing levels of intensity. Then, we train CLIP to rank these degraded
images based on their similarity to quality-related antonym text prompts, while
guaranteeing consistent representations for images with comparable quality. Our
method achieves state-of-the-art performance on several datasets with authentic
distortions. Moreover, despite not requiring MOS, QualiCLIP outperforms
supervised methods when their training dataset differs from the testing one,
thus proving to be more suitable for real-world scenarios. Furthermore, our
approach demonstrates greater robustness and improved explainability than
competing methods. The code and the model are publicly available at
https://github.com/miccunifi/QualiCLIP.
