Projects per year
Abstract
Rate control, which typically includes bit allocation and quantization parameter (QP) determination, plays an important role in real-world video coding applications. In this paper, we propose a novel rate control scheme for AOMedia Video 1 (AV1) which enjoys adaptive bit allocation and effective QP determination. In particular, two supporting vector regression (SVR) models are learned for the hierarchical bit allocation and frame-level parameter estimation. To train the models, the multi-pass coding strategy is utilized for training data acquisition. Compared to the default scheme in AV1 and the state-of-the-art method, the proposed rate control scheme achieves superior performance in terms of bitrate accuracy and coding efficiency. © 2023 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 16-25 |
| Journal | IEEE MultiMedia |
| Volume | 30 |
| Issue number | 4 |
| Online published | 10 Apr 2023 |
| DOIs | |
| Publication status | Published - Oct 2023 |
Research Keywords
- Adaptation models
- Bit rate
- Encoding
- Machine learning
- Parameter estimation
- Training
- Video coding
- AV1
- Rate Control
Fingerprint
Dive into the research topics of 'Learning From Coding Features: High Efficiency Rate Control for AOMedia Video 1'. Together they form a unique fingerprint.Projects
- 2 Finished
-
GRF: Intelligent Ultra High Definition Video Encoder Optimization for Future Versatile Video Coding
KWONG, T. W. S. (Principal Investigator / Project Coordinator), KUO, J. (Co-Investigator), WANG, S. (Co-Investigator) & ZHOU, M. (Co-Investigator)
1/01/20 → 5/09/23
Project: Research
-
GRF: The Impact of Social Media Use on Mass Polarization in Hong Kong: Putting Multiple Identities into Perspective
KOBAYASHI, T. (Principal Investigator / Project Coordinator) & WONG, S. H. W. (Co-Investigator)
1/01/18 → 18/11/20
Project: Research