Potential Gains of Reactive Video QoE Enhancement by App Agnostic QoE Deduction

Szabó, Géza; Rácz, Sándor; Malomsoky, Szabolcs; Bolle, Aldo

doi:10.1109/glocom.2016.7842155

Cited by 3 publications

(1 citation statement)

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“…They also compare machine learning-based and non-machine learning-based models, proving that the former exhibit better performance. This knowledge can help service and network providers deliver high-quality and cost-effective services while efficiently managing network operations [32].…”

Section: Introductionmentioning

confidence: 99%

Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment

Margetis

Tsagkatakis

Stamou

et al. 2023

Sensors

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Existing video Quality-of-Experience (QoE) metrics rely on the decoded video for the estimation. In this work, we explore how the overall viewer experience, quantified via the QoE score, can be automatically derived using only information available before and during the transmission of videos, on the server side. To validate the merits of the proposed scheme, we consider a dataset of videos encoded and streamed under different conditions and train a novel deep learning architecture for estimating the QoE of the decoded video. The major novelty of our work is the exploitation and demonstration of cutting-edge deep learning techniques in automatically estimating video QoE scores. Our work significantly extends the existing approach for estimating the QoE in video streaming services by combining visual information and network conditions.

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Section: Introductionmentioning

confidence: 99%