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).

翻译：盲图像质量评估（BIQA）旨在无需参考基准的情况下，依据人类感知评估图像质量。当前，基于深度学习的BIQA方法通常依赖从高级任务中提取特征进行迁移学习。然而，BIQA与这些高级任务之间的固有差异会不可避免地引入质量感知特征的噪声。本文首次探索了扩散模型在BIQA特征去噪中的应用，即面向IQA的感知特征扩散（PFD-IQA），旨在去除质量感知特征中的噪声。具体而言：（i）我们提出感知先验发现与聚合模块，通过建立两个辅助任务来发现图像中潜在的低级特征，进而为扩散模型聚合感知文本条件；（ii）我们提出基于感知先验的特征精炼策略，将含噪特征匹配至预定义的去噪轨迹，并基于文本条件执行精确的特征去噪。在八个标准BIQA数据集上的大量实验表明，该方法性能优于当前最优的BIQA方法——在KADID数据集上PLCC值达0.935（对比0.905），在LIVEC数据集上达0.922（对比0.894）。