  The statistical regularities of natural images, referred to as natural scene
statistics, play an important role in no-reference image quality assessment.
However, it has been widely acknowledged that screen content images (SCIs),
which are typically computer generated, do not hold such statistics. Here we
make the first attempt to learn the statistics of SCIs, based upon which the
quality of SCIs can be effectively determined. The underlying mechanism of the
proposed approach is based upon the mild assumption that the SCIs, which are
not physically acquired, still obey certain statistics that could be understood
in a learning fashion. We empirically show that the statistics deviation could
be effectively leveraged in quality assessment, and the proposed method is
superior when evaluated in different settings. Extensive experimental results
demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA)
model delivers promising performance compared with existing NR-IQA models and
shows a high generalization capability in the cross-dataset settings. The
implementation of our method is publicly available at
https://github.com/Baoliang93/DFSS-IQA.
