  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 towards 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, (i) 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 text conditions for
the diffusion model. (ii) We propose a Perceptual Prior-based Feature
Refinement strategy, which matches noisy features to predefined denoising
trajectories and then performs exact feature denoising based on text
conditions. Extensive experiments on eight standard BIQA datasets demonstrate
the superior performance to the state-of-the-art BIQA methods, i.e., achieving
the PLCC values of 0.935 ( vs. 0.905 in KADID) and 0.922 ( vs. 0.894 in LIVEC).
