Comparison of No-Reference Image Quality Models via MAP Estimation in
  Diffusion Latents
Weixia Zhang Dingquan Li Guangtao Zhai Xiaokang Yang Kede Ma
Contemporary no-reference image quality assessment (NR-IQA) models can
effectively quantify the perceived image quality, with high correlations
between model predictions and human perceptual scores on fixed test sets.
However, little progress has been made in comparing NR-IQA models from a
perceptual optimization perspective. Here, for the first time, we demonstrate
that NR-IQA models can be plugged into the maximum a posteriori (MAP)
estimation framework for image enhancement. This is achieved by taking the
gradients in differentiable and bijective diffusion latents rather than in the
raw pixel domain. Different NR-IQA models are likely to induce different
enhanced images, which are ultimately subject to psychophysical testing. This
leads to a new computational method for comparing NR-IQA models within the
analysis-by-synthesis framework. Compared to conventional correlation-based
metrics, our method provides complementary insights into the relative strengths
and weaknesses of the competing NR-IQA models in the context of perceptual
optimization.