Learning From Coding Features: High Efficiency Rate Control for AOMedia Video 1

Yi Chen, Yunhao Mao, Shiqi Wang*, Xianguo Zhang, Sam Kwong*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Citation (Scopus)

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 languageEnglish
Pages (from-to)16-25
JournalIEEE MultiMedia
Volume30
Issue number4
Online published10 Apr 2023
DOIs
Publication statusPublished - Oct 2023

Research Keywords

  • Adaptation models
  • Bit rate
  • Encoding
  • Machine learning
  • Parameter estimation
  • Training
  • Video coding
  • AV1
  • Rate Control

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