  The design of no-reference (NR) image quality assessment (IQA) algorithms is
extremely important to benchmark and calibrate user experiences in modern
visual systems. A major drawback of state-of-the-art NR-IQA methods is their
limited ability to generalize across diverse IQA settings with reasonable
distribution shifts. Recent text-to-image generative models such as latent
diffusion models generate meaningful visual concepts with fine details related
to text concepts. In this work, we leverage the denoising process of such
diffusion models for generalized IQA by understanding the degree of alignment
between learnable quality-aware text prompts and images. In particular, we
learn cross-attention maps from intermediate layers of the denoiser of latent
diffusion models to capture quality-aware representations of images. In
addition, we also introduce learnable quality-aware text prompts that enable
the cross-attention features to be better quality-aware. Our extensive cross
database experiments across various user-generated, synthetic, and low-light
content-based benchmarking databases show that latent diffusion models can
achieve superior generalization in IQA when compared to other methods in the
literature.
