Scale Guided Hypernetwork for Blind Super-Resolution Image Quality
  Assessment
Jun Fu
With the emergence of image super-resolution (SR) algorithm, how to blindly
evaluate the quality of super-resolution images has become an urgent task.
However, existing blind SR image quality assessment (IQA) metrics merely focus
on visual characteristics of super-resolution images, ignoring the available
scale information. In this paper, we reveal that the scale factor has a
statistically significant impact on subjective quality scores of SR images,
indicating that the scale information can be used to guide the task of blind SR
IQA. Motivated by this, we propose a scale guided hypernetwork framework that
evaluates SR image quality in a scale-adaptive manner. Specifically, the blind
SR IQA procedure is divided into three stages, i.e., content perception,
evaluation rule generation, and quality prediction. After content perception, a
hypernetwork generates the evaluation rule used in quality prediction based on
the scale factor of the SR image. We apply the proposed scale guided
hypernetwork framework to existing representative blind IQA metrics, and
experimental results show that the proposed framework not only boosts the
performance of these IQA metrics but also enhances their generalization
abilities. Source code will be available at https://github.com/JunFu1995/SGH.