  Nowadays, with the vigorous expansion and development of gaming video
streaming techniques and services, the expectation of users, especially the
mobile phone users, for higher quality of experience is also growing swiftly.
As most of the existing research focuses on traditional video streaming, there
is a clear lack of both subjective study and objective quality models that are
tailored for quality assessment of mobile gaming content. To this end, in this
study, we first present a brand new Tencent Gaming Video dataset containing
1293 mobile gaming sequences encoded with three different codecs. Second, we
propose an objective quality framework, namely Efficient hard-RAnk Quality
Estimator (ERAQUE), that is equipped with (1) a novel hard pairwise ranking
loss, which forces the model to put more emphasis on differentiating similar
pairs; (2) an adapted model distillation strategy, which could be utilized to
compress the proposed model efficiently without causing significant performance
drop. Extensive experiments demonstrate the efficiency and robustness of our
model.
