  Image quality assessment (IQA) plays a critical role in selecting
high-quality images and guiding compression and enhancement methods in a series
of applications. The blind IQA, which assesses the quality of in-the-wild
images containing complex authentic distortions without reference images, poses
greater challenges. Existing methods are limited to modeling a uniform
distribution with local patches and are bothered by the gap between low and
high-level visions (caused by widely adopted pre-trained classification
networks). In this paper, we propose a novel IQA method called diffusion
priors-based IQA (DP-IQA), which leverages the prior knowledge from the
pre-trained diffusion model with its excellent powers to bridge semantic gaps
in the perception of the visual quality of images. Specifically, we use
pre-trained stable diffusion as the backbone, extract multi-level features from
the denoising U-Net during the upsampling process at a specified timestep, and
decode them to estimate the image quality score. The text and image adapters
are adopted to mitigate the domain gap for downstream tasks and correct the
information loss caused by the variational autoencoder bottleneck. Finally, we
distill the knowledge in the above model into a CNN-based student model,
significantly reducing the parameter to enhance applicability, with the student
model performing similarly or even better than the teacher model surprisingly.
Experimental results demonstrate that our DP-IQA achieves state-of-the-art
results on various in-the-wild datasets with better generalization capability,
which shows the superiority of our method in global modeling and utilizing the
hierarchical feature clues of diffusion for evaluating image quality.
