Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | IEEE International Symposium on Circuits and Systems, ISCAS 2022 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 511-515 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9781665484855 |
| ISBN (Print) | 978-1-6654-8486-2 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Veranstaltung | 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022 - Austin, USA / Vereinigte Staaten Dauer: 27 Mai 2022 → 1 Juni 2022 |
Publikationsreihe
| Name | Proceedings - IEEE International Symposium on Circuits and Systems |
|---|---|
| Band | 2022-May |
| ISSN (Print) | 0271-4310 |
Abstract
In this paper we introduce an error concealment method for VVC that error-conceals B-frames based on the neural frame interpolation network RIFE. The network is trained using the BVI-DVC dataset to infer even full-HD frames. We integrate our proposed model in the VVC reference software VTM for its evaluation. The average error of a whole GOP with a single corrupted frame is decreased by 15% and 24% in terms of PSNR measurement compared to block matching and frame copy, respectively. To our knowledge, our approach is currently the best performing error concealment algorithm for single slice per B-frame settings.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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IEEE International Symposium on Circuits and Systems, ISCAS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. S. 511-515 (Proceedings - IEEE International Symposium on Circuits and Systems; Band 2022-May).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Neural Network-based Error Concealment for B-Frames in VVC
AU - Benjak, Martin
AU - Aust, Niklas
AU - Samayoa, Yasser
AU - Ostermann, Jorn
PY - 2022
Y1 - 2022
N2 - In this paper we introduce an error concealment method for VVC that error-conceals B-frames based on the neural frame interpolation network RIFE. The network is trained using the BVI-DVC dataset to infer even full-HD frames. We integrate our proposed model in the VVC reference software VTM for its evaluation. The average error of a whole GOP with a single corrupted frame is decreased by 15% and 24% in terms of PSNR measurement compared to block matching and frame copy, respectively. To our knowledge, our approach is currently the best performing error concealment algorithm for single slice per B-frame settings.
AB - In this paper we introduce an error concealment method for VVC that error-conceals B-frames based on the neural frame interpolation network RIFE. The network is trained using the BVI-DVC dataset to infer even full-HD frames. We integrate our proposed model in the VVC reference software VTM for its evaluation. The average error of a whole GOP with a single corrupted frame is decreased by 15% and 24% in terms of PSNR measurement compared to block matching and frame copy, respectively. To our knowledge, our approach is currently the best performing error concealment algorithm for single slice per B-frame settings.
KW - error concealment
KW - video coding
KW - VVC
UR - http://www.scopus.com/inward/record.url?scp=85142529092&partnerID=8YFLogxK
U2 - 10.1109/ISCAS48785.2022.9937956
DO - 10.1109/ISCAS48785.2022.9937956
M3 - Conference contribution
AN - SCOPUS:85142529092
SN - 978-1-6654-8486-2
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 511
EP - 515
BT - IEEE International Symposium on Circuits and Systems, ISCAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022
Y2 - 27 May 2022 through 1 June 2022
ER -