Subjective and Objective Quality Assessment of Mobile Gaming Video
This work tackles video quality assessment for mobile gaming, an incremental improvement over traditional video streaming methods.
The paper addresses the lack of quality assessment methods for mobile gaming videos by introducing a new dataset of 1293 sequences and proposing an objective quality framework called ERAQUE, which achieves efficiency and robustness in experiments.
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.