  In this paper, we quest the capability of transferring the quality of natural
scene images to the images that are not acquired by optical cameras (e.g.,
screen content images, SCIs), rooted in the widely accepted view that the human
visual system has adapted and evolved through the perception of natural
environment. Here, we develop the first unsupervised domain adaptation based no
reference quality assessment method for SCIs, leveraging rich subjective
ratings of the natural images (NIs). In general, it is a non-trivial task to
directly transfer the quality prediction model from NIs to a new type of
content (i.e., SCIs) that holds dramatically different statistical
characteristics. Inspired by the transferability of pair-wise relationship, the
proposed quality measure operates based on the philosophy of improving the
transferability and discriminability simultaneously. In particular, we
introduce three types of losses which complementarily and explicitly regularize
the feature space of ranking in a progressive manner. Regarding feature
discriminatory capability enhancement, we propose a center based loss to
rectify the classifier and improve its prediction capability not only for
source domain (NI) but also the target domain (SCI). For feature discrepancy
minimization, the maximum mean discrepancy (MMD) is imposed on the extracted
ranking features of NIs and SCIs. Furthermore, to further enhance the feature
diversity, we introduce the correlation penalization between different feature
dimensions, leading to the features with lower rank and higher diversity.
Experiments show that our method can achieve higher performance on different
source-target settings based on a light-weight convolution neural network. The
proposed method also sheds light on learning quality assessment measures for
unseen application-specific content without the cumbersome and costing
subjective evaluations.
