Topics of this blog are related to multimedia communication. In particular, streaming of multimedia content within heterogeneous environments enabling Universal Multimedia Experience (UME).
Minh Nguyen, Ekrem Çetinkaya, Hermann Hellwagner, and Christian Timmerer Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt
Abstract:
The advancement of mobile hardware in recent years made it possible to
apply deep neural network (DNN) based approaches on mobile devices. This
paper introduces a lightweight super-resolution (SR) network, namely
SR-ABR Net, deployed at mobile devices to upgrade
low-resolution/low-quality videos and a novel adaptive bitrate (ABR)
algorithm, namely WISH-SR, that leverages SR networks at the client to
improve the video quality depending on the client’s context. WISH-SR
takes into account mobile device properties, video characteristics, and
user preferences. Experimental results show that the proposed SR-ABR Net
can improve the video quality compared to traditional SR approaches
while running in real-time. Moreover, the proposed WISH-SR can
significantly boost the visual quality of the delivered content while
reducing both bandwidth consumption and the number of stalling events.
Keywords: Super-resolution, Deep Neural Networks, Mobile Devices, ABR
Acknowledgments: The financial support of the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association, is gratefully acknowledged. Christian Doppler Laboratory ATHENA: https://athena.itec.aau.at/.
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