Denoised Semantic Features for Local Consistent No-Reference Image Quality Assessment

Multi-dataset no-reference image quality assessment (NR-IQA) aims to deliver consistent image quality evaluation across a variety of contexts, empowering platform developers to optimize image processing pipelines while maintaining acceptable visual quality. Human vision, when observing images, tends to prioritize local semantics, for example, a blurry sky is perceived differently than a blurry face. This insight forms the basis of many multi-dataset NR-IQA models, which commonly rely on pretrained deep networks to extract semantic information that is crucial for assessing perceptual quality. Vision Transformer-based pre-trained models often exhibit persistent noise artifacts, as demonstrated by previous studies such as Denoising Vision Transformers; many existing IQA approaches fail to appropriately address these local semantic artifacts, leading to inconsistent local IQA score maps, even when overall performance appears satisfactory. To tackle this, we introduce DINO-IQA, a novel dual-branch network architecture designed for NR-IQA to multi-dataset. The first branch focuses on extracting local distortion features, effectively capturing image degradation, while the second branch utilizes denoised DINOv2 from ViT decomposition to extract refined semantic features, free from local artifacts. By enabling visual interaction between distortion and semantic features, our method generates locally consistent quality maps that align more closely with human perception. This approach achieves remarkable accuracy and sets a new benchmark for state-of-the-art multi-dataset NR-IQA performance. Our findings underscore the critical need to address semantic noise in pretrained networks for enhancing NR-IQA, demonstrating that our dual-branch framework offers a robust solution to this previously underexplored challenge.