Performance of blind image quality assessment (BIQA) models has been
significantly boosted by end-to-end optimization of feature engineering and
quality regression. Nevertheless, due to the distributional shift between
images simulated in the laboratory and captured in the wild, models trained on
databases with synthetic distortions remain particularly weak at handling
realistic distortions (and vice versa). To confront the
cross-distortion-scenario challenge, we develop a \textit{unified} BIQA model
and an approach of training it for both synthetic and realistic distortions. We
first sample pairs of images from individual IQA databases, and compute a
probability that the first image of each pair is of higher quality. We then
employ the fidelity loss to optimize a deep neural network for BIQA over a
large number of such image pairs. We also explicitly enforce a hinge constraint
to regularize uncertainty estimation during optimization. Extensive experiments
on six IQA databases show the promise of the learned method in blindly
assessing image quality in the laboratory and wild. In addition, we demonstrate
the universality of the proposed training strategy by using it to improve
existing BIQA models.

通过端到端优化特征工程和质量回归，盲目的图像质量评估（BIQA）模型的性能得到了显著提高。然而，由于实验室模拟的图像和野外拍摄的图像之间的分布偏移，仅在具有合成失真的数据库上训练的模型在处理现实失真方面仍然特别薄弱（反之亦然）。为应对跨失真场景的挑战，我们开发了一个 “统一” 的 BIQA 模型，并采用一种同时用于合成和真实失真的训练方法。