  In this paper, we propose a no-reference (NR) image quality assessment (IQA)
method via feature level pseudo-reference (PR) hallucination. The proposed
quality assessment framework is grounded on the prior models of natural image
statistical behaviors and rooted in the view that the perceptually meaningful
features could be well exploited to characterize the visual quality. Herein,
the PR features from the distorted images are learned by a mutual learning
scheme with the pristine reference as the supervision, and the discriminative
characteristics of PR features are further ensured with the triplet
constraints. Given a distorted image for quality inference, the feature level
disentanglement is performed with an invertible neural layer for final quality
prediction, leading to the PR and the corresponding distortion features for
comparison. The effectiveness of our proposed method is demonstrated on four
popular IQA databases, and superior performance on cross-database evaluation
also reveals the high generalization capability of our method. The
implementation of our method is publicly available on
https://github.com/Baoliang93/FPR.
