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• W2954037246 abstract "Popularity of streaming services for gaming videos has increased tremendously over the last years, e.g. Twitch and Youtube Gaming. Compared to classical video streaming applications, gaming videos have additional requirements. For example, it is important that videos are streamed live with only a small delay. In addition, users expect low stalling, waiting time and in general high video quality during streaming, e.g. using http-based adaptive streaming. These requirements lead to different challenges for quality prediction in case of streamed gaming videos. We describe newly developed features and a no-reference video quality machine learning model, that uses only the recorded video to predict video quality scores. In different evaluation experiments we compare our proposed model nofu with state-of-the-art reduced or full reference models and metrics. In addition, we trained a no-reference baseline model using brisque+niqe features. We show that our model has a similar or better performance than other models. Furthermore, nofu outperforms VMAF for subjective gaming QoE prediction, even though nofu does not require any reference video." @default.
• W2954037246 created "2019-07-12" @default.
• W2954037246 creator A5008559961 @default.
• W2954037246 creator A5037783940 @default.
• W2954037246 creator A5057002019 @default.
• W2954037246 date "2019-06-01" @default.
• W2954037246 modified "2023-10-18" @default.
• W2954037246 title "nofu — A Lightweight No-Reference Pixel Based Video Quality Model for Gaming Content" @default.
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• W2954037246 doi "https://doi.org/10.1109/qomex.2019.8743262" @default.
• W2954037246 hasPublicationYear "2019" @default.
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