Publication: CODA: Content-aware Frame Dropping Algo...
Master data
Title: | CODA: Content-aware Frame Dropping Algorithm for High Frame-rate Video Streaming |
Subtitle: | |
Abstract: | Ultra High Definition Television (UHDTV) offers a better immersive audiovisual experience than HDTV by improving the aesthetic sense of the content [1]. How-ever, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, a low-latency pre-processing algorithm named COntent-aware frame Dropping Algorithm (CODA) is proposed to predict the optimized framerate per video segment in streaming scenarios. The optimized framerate (f^) for every video segment at each target bitrate is modelled as an exponential decay (increasing) function whose decay rate is directly proportional to the temporal characteristics (h) [2] [3] of the video and the target bitrate (b), and inversely proportional to the spatial characteristics (E) of the video. The encoding is carried out with the predicted framerate, saving encoding time and improving visual quality at lower bitrates. At the decoder side, the video is upscaled in the temporal domain to the original framerate (fmax) for display. |
Keywords: | UHD streaming, Framerate prediction, High Framerate video |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 03.2022 (Print) |
Published by: |
DCC '22 Proceedings of the Data Compression Conference
DCC '22 Proceedings of the Data Compression Conference
(
IEEE;
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to publication |
Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 475 - 475 |
Versionen
Keine Version vorhanden |
Publication date: | 03.2022 |
ISBN: |
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ISSN: | - |
Homepage: | https://ieeexplore.ieee.org/document/9810708 |
Publication date: | 04.07.2022 |
ISBN (e-book): |
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eISSN: | 2375-0359 |
DOI: | http://dx.doi.org/10.1109/dcc52660.2022.00086 |
Homepage: | https://ieeexplore.ieee.org/document/9810708 |
Open access |
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Authors
Vignesh Menon (internal) |
Hadi Amirpourazarian (internal) |
Mohammad Ghanbari (internal) |
Christian Timmerer (internal) |
Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Research Cluster | No research Research Cluster selected |
Peer reviewed |
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