Subjective and Objective Quality Assessment of Mobile Gaming Video

TL;DR

This paper introduces a new dataset and a novel quality assessment model specifically designed for mobile gaming videos, addressing the gap in subjective and objective quality evaluation for this content type.

Contribution

It presents a new dataset of mobile gaming videos and proposes ERAQUE, a novel quality assessment framework with a unique ranking loss and model distillation strategy.

Findings

ERAQUE outperforms existing models in accuracy and robustness.

The dataset enables better evaluation of mobile gaming video quality.

Model compression maintains performance with reduced complexity.

Abstract

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.