Details
| Original language | English |
|---|---|
| Title of host publication | ICAIIC |
| Pages | 21-25 |
| Number of pages | 5 |
| ISBN (electronic) | 978-1-7281-7638-3 |
| Publication status | Published - 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.
Keywords
- inter coding, machine learning, recurrent neural networks, video coding, VVC
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
Cite this
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ICAIIC. 2021. p. 21-25 9415184.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -