Publikation: Improving Per-title Encoding for HTTP A...
Stammdaten
Titel: | Improving Per-title Encoding for HTTP Adaptive Streaming by Utilizing Video Super-resolution |
Untertitel: | |
Kurzfassung: | In per-title encoding, to optimize a bitrate ladder over spatial resolution, each video segment is downscaled to a set of spatial resolutions, and they are all encoded at a given set of bitrates. To find the highest quality resolution for each bitrate, the low-resolution encoded videos are upscaled to the original resolution, and a convex hull is formed based on the scaled qualities. Deep learning-based video super-resolution (VSR) approaches show a significant gain over traditional upscaling approaches, and they are becoming more and more efficient over time. This paper improves the per-title encoding over the upscaling methods by using deep neural network-based VSR algorithms. Utilizing a VSR algorithm by improving the quality of low-resolution encodings can improve the convex hull. As a result, it will lead to an improved bitrate ladder. To avoid bandwidth wastage at perceptually lossless bitrates, a maximum threshold for the quality is set, and encodings beyond it are eliminated from the bitrate ladder. Similarly, a minimum threshold is set to avoid low-quality video delivery. The encodings between the maximum and minimum thresholds are selected based on one Just Noticeable Difference. Our experimental results show that the proposed per-title encoding results in a 24% bitrate reduction and 53% storage reduction compared to the state-of-the-art method. |
Schlagworte: | Image coding, Visual communication, Bit rate, Superresolution, Bandwidth, Streaming media, Spatial resolution, HAS, per-title,deep learning, compression, bi-trate ladder |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 05.12.2021 (Print) |
Erschienen in: |
2021 International Conference on Visual Communications and Image Processing (VCIP)
2021 International Conference on Visual Communications and Image Processing (VCIP)
(
IEEE;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 1 - 5 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 05.12.2021 |
ISBN: | - |
ISSN: | - |
Homepage: | - |
Erscheinungsdatum: | 20.01.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/vcip53242.2021.9675403 |
Homepage: | - |
Open Access |
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AutorInnen
Hadi Amirpourazarian (intern) |
Hannaneh Barahouei Pasandi (extern) |
Christian Timmerer (intern) |
Mohammad Ghanbari (intern) |
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
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Forschungscluster | Kein Forschungscluster ausgewählt |
Peer Reviewed |
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Klassifikationsraster der zugeordneten Organisationseinheiten:
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Kooperationen
Organisation | Adresse | ||||
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University of Essex
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GB - C04 3SQ Colchester |
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Virginia Commonwealth University
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US Richmond |
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(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
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