Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | ICAIIC |
| Seiten | 21-25 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 978-1-7281-7638-3 |
| Publikationsstatus | Veröffentlicht - 2021 |
Abstract
In this paper, we propose an enhanced machine learning-based inter coding algorithm for VVC. Conceptually, the reference pictures from the decoded picture butter are processed using a recurrent neural network to generate an artificial reference picture at the time instance of the currently coded picture. The network is trained using a SATD cost function to minimize the bit rate cost for the prediction error rather than the pixel-wise difference. By this we achieved average weighted BD-rate gains of 0.94%. The coding time increased about 5% for the encoder and 300% for the decoder due to the use of a neural network.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Information systems
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
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- BibTex
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ICAIIC. 2021. S. 21-25 9415184.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Enhanced Machine Learning-based Inter Coding for VVC.
AU - Benjak, Martin
AU - Meuel, Holger
AU - Laude, Thorsten
AU - Ostermann, Jörn
PY - 2021
Y1 - 2021
N2 - In this paper, we propose an enhanced machine learning-based inter coding algorithm for VVC. Conceptually, the reference pictures from the decoded picture butter are processed using a recurrent neural network to generate an artificial reference picture at the time instance of the currently coded picture. The network is trained using a SATD cost function to minimize the bit rate cost for the prediction error rather than the pixel-wise difference. By this we achieved average weighted BD-rate gains of 0.94%. The coding time increased about 5% for the encoder and 300% for the decoder due to the use of a neural network.
AB - In this paper, we propose an enhanced machine learning-based inter coding algorithm for VVC. Conceptually, the reference pictures from the decoded picture butter are processed using a recurrent neural network to generate an artificial reference picture at the time instance of the currently coded picture. The network is trained using a SATD cost function to minimize the bit rate cost for the prediction error rather than the pixel-wise difference. By this we achieved average weighted BD-rate gains of 0.94%. The coding time increased about 5% for the encoder and 300% for the decoder due to the use of a neural network.
KW - inter coding
KW - machine learning
KW - recurrent neural networks
KW - video coding
KW - VVC
UR - http://www.scopus.com/inward/record.url?scp=85105441407&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC51459.2021.9415184
DO - 10.1109/ICAIIC51459.2021.9415184
M3 - Conference contribution
SN - 978-1-7281-7639-0
SP - 21
EP - 25
BT - ICAIIC
ER -