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A comparison of HAS behavior in the high definition and ultra high definition context

机译:高清晰度和超高清晰度上下文中的HAS行为比较

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Video content represents majority of the data available on the Internet. Streaming services like YouTube, Vimeo, NetFlix and others generate more than 60% of the overall Internet traffic, according to recent studies. This results in high demands on the quality of the broadband connection. The user usually wants to get the highest media quality, no matter the quality of the connection. This is where the HTTP Adaptive Streaming (HAS) comes into the game. This service enables to maintain the highest media quality possible to offer the user the best quality of experience (QoE). In this paper, we focus on the evaluation of streaming Ultra HD video content using HAS. We evaluate the difference of the QoE for HD and Ultra HD streaming with High Efficiency Video Coding. For this purpose, we created a database of HEVC coded videos of different contents. These were then used in five different HAS scenarios (sudden quality change at the end, sudden quality change in the middle of the sequence, moderate quality changes, gradual quality drop-down and constant high quality for benchmark). HAS sequences were then used in assessed subjective quality test. According to the results, the Ultra HD outperforms the high definition in the terms of perceived subjective quality when used in HAS.
机译:视频内容代表了Internet上可用的大多数数据。根据最近的研究,YouTube,Vimeo,NetFlix等流媒体服务产生了超过60%的整体Internet流量。这导致对宽带连接质量的高要求。无论连接的质量如何,用户通常都希望获得最高的媒体质量。这是HTTP自适应流(HAS)进入游戏的地方。该服务可以保持最高的媒体质量,从而为用户提供最佳的体验质量(QoE)。在本文中,我们专注于使用HAS评估流式超高清视频内容的评估。我们评估了采用高效视频编码的高清和超高清流的QoE的差异。为此,我们创建了一个包含不同内容的HEVC编码视频的数据库。然后将它们用于五种不同的HAS场景中(末尾突然发生质量变化,序列中间出现突然的质量变化,中等质量变化,逐渐下降的质量以及恒定的高质量基准)。然后将HAS序列用于评估的主观质量测试中。根据结果​​,在用于HAS时,Ultra HD在可感知的主观质量方面胜过高清。

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