  In recent years, deep neural networks have been utilized in a wide variety of
applications including image generation. In particular, generative adversarial
networks (GANs) are able to produce highly realistic pictures as part of tasks
such as image compression. As with standard compression, it is desirable to be
able to automatically assess the perceptual quality of generative images to
monitor and control the encode process. However, existing image quality
algorithms are ineffective on GAN generated content, especially on textured
regions and at high compressions. Here we propose a new naturalness-based image
quality predictor for generative images. Our new GAN picture quality predictor
is built using a multi-stage parallel boosting system based on structural
similarity features and measurements of statistical similarity. To enable model
development and testing, we also constructed a subjective GAN image quality
database containing (distorted) GAN images and collected human opinions of
them. Our experimental results indicate that our proposed GAN IQA model
delivers superior quality predictions on the generative image datasets, as well
as on traditional image quality datasets.
