Image quality assessment is often performed with deep networks that are fine-tuned to
regress a human provided quality score of a given image. Usually, this approach may lack
generalization capabilities and, while being highly precise on similar image distribution, it
may yield lower correlation on unseen distortions. In particular, they show poor
performances, whereas images corrupted by noise, blur, or compression have been
restored by generative models. As a matter of fact, evaluation of these generative models is …

In the last years, models able to generate novel images by implicit sampling from the data
distribution have been proposed [16]. While these models are extremely appealing,
generating for example photo realistic faces [22] or landscapes [35], they are hard to be
evaluated. Often anecdotal qualitative examples are presented to the reader with little
quantitative and objective evidence, and evaluation of generative models is still undergoing
a debate regarding how to perform it. The idea of using a computer vision classiier to …