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Sebastian Möller

Researcher at Technical University of Berlin

Publications -  531
Citations -  7103

Sebastian Möller is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Quality (business) & Quality of experience. The author has an hindex of 34, co-authored 491 publications receiving 5830 citations. Previous affiliations of Sebastian Möller include German Research Centre for Artificial Intelligence & University of Oslo.

Papers
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Proceedings ArticleDOI

Predicting video call quality in future mobile networks

Blazej Lewcio, +1 more
TL;DR: This paper analyses the applicability of speech, video, and call quality prediction models for video telephony in heterogeneous wireless networks and shows how accurately the quality of video calls in mobile networks can be predicted with the existing approaches, and discloses the major limitations of the individual models.
Proceedings ArticleDOI

Towards the Influence of Audio Quality on Gaming Quality of Experience

Steven Schmidt, +3 more
TL;DR: In this paper, the impact of audio quality on gaming experience under different bitrate and packet loss conditions using two popular games is investigated, and the results show a significant impact of packet loss on audio quality and the overall gaming QoE.
Proceedings ArticleDOI

A next step towards measuring perceived quality of speech through physiology.

Sebastian Arndt, +5 more
TL;DR: To validate the selected stimulus corpus, a quality predication algorithm is used which calculates quality scores based on the speech signal, and a subjective quality judgment is obtained after each presentation which is summarized as the mean opinion score (MOS).
Journal ArticleDOI

Working With Environmental Noise and Noise-Cancelation: A Workload Assessment With EEG and Subjective Measures.

Kerstin Pieper, +6 more
- 01 Nov 2021 - 
TL;DR: In this paper, the authors used EEG and subjective measures to investigate if noise-canceling technologies can fade out external distractions and free up mental resources and found that the mean P300 activation at Cz resulted in a significant differentiation between the no noise and the other two test conditions.
Proceedings ArticleDOI

Deep-BVQM: A Deep-learning Bitstream-based Video Quality Model

Nasim Jamshidi Avanaki, +4 more
TL;DR: A new bitstream-based model named Deep-BVQM is proposed, which outperforms the standard models on the tested datasets and offers a frame-level quality prediction which is essential diagnostic information for some video streaming services such as cloud gaming.