Abstract
Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application-specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning-based quality estimation models for gaming video streaming, NR-GVSQI, and NR-GVSQE, using NR features, such as bitrate, resolution, and temporal information. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.
Highlights
Gaming video streaming has gained much popularity in recent years, due to the advances made in the field of both passive and interactive services
Since the ultimate goal of any Image Quality Assessment (IQA)/video quality assessment (VQA) metric is to be able to predict the subjective quality as presented in Section V, we evaluate the performance of the model presented here - developed based on VMAF scores in terms of correlation with respect to MOS scores from the subjective dataset
We will discuss the specificity of the model for gaming video and the comparison with other gaming video quality models
Summary
Gaming video streaming has gained much popularity in recent years, due to the advances made in the field of both passive and interactive services. Interactive gaming streaming applications or cloud gaming, as popularly known, refer to applications where the user’s gameplay is processed in the cloud. The passive scenario, on the other hand, refers to Over-The-Top (OTT) services, such as Twitch.tv and. The associate editor coordinating the review of this manuscript and approving it for publication was Hao Ji. YouTubeGaming, where a viewer can watch videos of the gameplay of other players. YouTubeGaming, where a viewer can watch videos of the gameplay of other players Such passive OTT gaming video streaming services have seen tremendous growth, in terms of both number of viewers and the number of streamers. Twitch.tv alone currently has over 15 million streamers and over nine million daily active users and is ranked 4th in terms of peak Internet traffic in the US, just behind Netflix, YouTube, and Apple [1]
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