Subjective and Objective Quality Assessment of Mobile Gaming Video
Shaoguo Wen, Junle Wang, Ximing Chen, Yanqing Jing, Suiyi Ling, Patrick Le Callet
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Nowadays, with the vigorous expansion and development of gaming video streaming techniques and services, the expectation of users, especially mobile phone users, for a 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 the 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.