A Novel Contractive GAN Model for a Unified Approach Towards Blind Quality Assessment of Images from Heterogeneous Sources

The heterogeneous distributions of pixel intensities between natural scene and document images casts challenges for generalizing quality assessment models across these two types of images, where human perceptual scores and optical character recognition accuracy are the respective quality metrics. In this paper we propose a novel contractive generative adversarial model to learn a unified quality-aware representation of images from heterogeneous sources in a latent domain. We then build a unified image quality assessment framework by applying a regressor in the unveiled latent domain, where the regressor operates as if it is assessing the quality of a single type of images. Test results on blur distortion across three benchmarking datasets show that the proposed model achieves promising performance competitive to the state-of-the-art simultaneously for natural scene and document images.