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.

现代无参考图像质量评估（NR-IQA）模型可以有效量化感知图像质量，在固定测试集上模型预测与人类感知评分之间存在高相关性。然而，在从感知优化的角度比较 NR-IQA 模型方面几乎没有取得进展。本文首次证明 NR-IQA 模型可以被插入到最大后验估计（MAP）框架用于图像增强，通过在可微且双射的扩散隐变量而不是原始像素域中进行梯度运算。不同的 NR-IQA 模型可能导致不同的增强图像，最终需要经过感知测试。这为在合成分析框架内比较 NR-IQA 模型提供了一种新的计算方法。与传统基于相关性的度量方法相比，我们的方法在感知优化的背景下提供了互补的洞察力，用于评估竞争的 NR-IQA 模型的相对优点和缺陷。