Feature Denoising Diffusion Model for Blind Image Quality Assessment

Abstract Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step toward exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, 1) we propose a Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual textual prompt conditions for the diffusion model. 2) we propose a Perceptual Conditional Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on textual prompt conditions. By incorporating a lightweight denoiser and requiring only a few feature denoising steps (e.g., just five iterations), our PFD-IQA framework achieves superior performance across eight standard BIQA datasets, validating its effectiveness.