  Existing free-energy guided No-Reference Image Quality Assessment (NR-IQA)
methods still suffer from finding a balance between learning feature
information at the pixel level of the image and capturing high-level feature
information and the efficient utilization of the obtained high-level feature
information remains a challenge. As a novel class of state-of-the-art (SOTA)
generative model, the diffusion model exhibits the capability to model
intricate relationships, enabling a comprehensive understanding of images and
possessing a better learning of both high-level and low-level visual features.
In view of these, we pioneer the exploration of the diffusion model into the
domain of NR-IQA. Firstly, we devise a new diffusion restoration network that
leverages the produced enhanced image and noise-containing images,
incorporating nonlinear features obtained during the denoising process of the
diffusion model, as high-level visual information. Secondly, two visual
evaluation branches are designed to comprehensively analyze the obtained
high-level feature information. These include the visual compensation guidance
branch, grounded in the transformer architecture and noise embedding strategy,
and the visual difference analysis branch, built on the ResNet architecture and
the residual transposed attention block. Extensive experiments are conducted on
seven public NR-IQA datasets, and the results demonstrate that the proposed
model outperforms SOTA methods for NR-IQA.
