2023
DOI: 10.3390/s23083998
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Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment

Abstract: 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 th… Show more

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Cited by 5 publications
(1 citation statement)
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“…For visual features, the proposed method comprises PatchVQ [25] model, which contains multiple stages that are spatiotemporal feature extraction, feature pooling, and the regression of the temporal. The spatio-temporal feature extraction happens by considering the four scales for the sequence of every video: the full video, sv-patch, tv-patch, and stv-patch.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…For visual features, the proposed method comprises PatchVQ [25] model, which contains multiple stages that are spatiotemporal feature extraction, feature pooling, and the regression of the temporal. The spatio-temporal feature extraction happens by considering the four scales for the sequence of every video: the full video, sv-patch, tv-patch, and stv-patch.…”
Section: Proposed Methodsmentioning
confidence: 99%