An objective and subjective quality assessment study of passive gaming video streaming,International Journal of Network Management

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An objective and subjective quality assessment study of passive gaming video streaming
International Journal of Network Management ( IF 1.5 ) Pub Date : 2018-11-22 , DOI: 10.1002/nem.2054
Nabajeet Barman ₁ , Saman Zadtootaghaj ₂ , Steven Schmidt ₃ , Maria G. Martini ₁ , Sebastian Möller ₃

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Passive gaming video‐streaming applications have recently gained much attention as evident with the rising popularity of many Over The Top (OTT) providers such as Twitch.tv and YouTube Gaming. For the continued success of such services, it is imperative that the user Quality of Experience (QoE) remains high, which is usually assessed using subjective and objective video quality assessment methods. Recent years have seen tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. A study on the performance of objective VQA on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos, is still missing. Towards this end, we present in this paper an objective and subjective quality assessment study on gaming videos considering passive streaming applications. Subjective ratings are obtained for 90 stimuli generated by encoding six different video games in multiple resolution‐bitrate pairs. Objective quality performance evaluation considering eight widely used VQA metrics is performed using the subjective test results and on a data set of 24 reference videos and 576 compressed sequences obtained by encoding them in 24 resolution‐bitrate pairs. Our results indicate that Video Multimethod Assessment Fusion (VMAF) predicts subjective video quality ratings the best, while Naturalness Image Quality Evaluator (NIQE) turns out to be a promising alternative as a no‐reference metric in some scenarios.

中文翻译：

被动游戏视频流的主观和主观质量评估研究

被动游戏视频流应用程序最近引起了很多关注，这明显体现在Twitch.tv和YouTube Gaming等许多Over the Top（OTT）提供商的日益普及。为了使此类服务不断取得成功，用户体验质量（QoE）保持较高水平势在必行，通常使用主观和客观视频质量评估方法对其进行评估。近年来，随着可预测通过互联网流式传输的视频质量的模型的发展，客观视频质量评估（VQA）指标领域取得了巨大进步。仍然缺乏对目标VQA在游戏视频上的性能的研究，这些视频是人工合成的，并且具有与传统流视频不同的流要求。为此，我们在本文中介绍了考虑被动流应用程序的游戏视频的客观和主观质量评估研究。通过将六个不同的视频游戏编码成多个分辨率比特率对，可获得90种刺激的主观评分。考虑到八个广泛使用的VQA指标的客观质量性能评估是使用主观测试结果，对24个参考视频和576个压缩序列（通过将它们以24个分辨率-比特率对编码）获得的数据集进行的。我们的结果表明，视频多方法评估融合（VMAF）可以预测主观视频质量等级，而在某些情况下，自然图像质量评估器（NIQE）可以作为无参考指标的替代方案。

更新日期：2018-11-22

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