Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment

Abstract

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 human-annotated Mean Opinion Score (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 MOS. In particular, we introduce a quality-aware image-text alignment strategy to make CLIP generate quality-aware image representations. 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. At the same time, we force CLIP to generate consistent representations for images with similar content and the same level of degradation. Our method significantly outperforms other opinion-unaware approaches on several datasets with authentic distortions. Moreover, despite not requiring MOS, QualiCLIP achieves state-of-the-art performance even when compared with supervised methods in cross-dataset experiments, thus proving to be suitable for application in real-world scenarios. The code and the model are publicly available at https://github.com/miccunifi/QualiCLIP.