  No-reference image quality assessment (NR-IQA) is a fundamental yet
challenging task in low-level computer vision community. The difficulty is
particularly pronounced for the limited information, for which the
corresponding reference for comparison is typically absent. Although various
feature extraction mechanisms have been leveraged from natural scene statistics
to deep neural networks in previous methods, the performance bottleneck still
exists. In this work, we propose a hallucination-guided quality regression
network to address the issue. We firstly generate a hallucinated reference
constrained on the distorted image, to compensate the absence of the true
reference. Then, we pair the information of hallucinated reference with the
distorted image, and forward them to the regressor to learn the perceptual
discrepancy with the guidance of an implicit ranking relationship within the
generator, and therefore produce the precise quality prediction. To demonstrate
the effectiveness of our approach, comprehensive experiments are evaluated on
four popular image quality assessment benchmarks. Our method significantly
outperforms all the previous state-of-the-art methods by large margins. The
code and model will be publicly available on the project page
https://kwanyeelin.github.io/projects/HIQA/HIQA.html.
