A VVC intra coding method based on fast partition for coding unit

ZHONG Hui; LU Yu; YIN Haibing; HUANG Xiaofeng

doi:10.11959/j.issn.1000-0801.2024196

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A VVC intra coding method based on fast partition for coding unit

Research and Development | 更新时间：2024-09-23

- A VVC intra coding method based on fast partition for coding unit
- Telecommunications Science Vol. 40, Issue 8, Pages: 23-33(2024)
- 作者机构：
  
  杭州电子科技大学通信工程学院，浙江杭州 310018
- 作者简介：
- 基金信息：
  
  The Research Project of Zhejiang Provincial Department of Education(Y202249588)
- DOI：10.11959/j.issn.1000-0801.2024196
  CLC： TP393
- Received：03 April 2024，
  
  Revised：2024-08-02，
  
  Published：20 August 2024
- 稿件说明：
移动端阅览
钟辉,陆宇,殷海兵等.一种基于编码单元快速划分的VVC帧内编码方法[J].电信科学,2024,40(08):23-33.

ZHONG Hui,LU Yu,YIN Haibing,et al.A VVC intra coding method based on fast partition for coding unit[J].Telecommunications Science,2024,40(08):23-33.
钟辉,陆宇,殷海兵等.一种基于编码单元快速划分的VVC帧内编码方法[J].电信科学,2024,40(08):23-33. DOI： 10.11959/j.issn.1000-0801.2024196.

ZHONG Hui,LU Yu,YIN Haibing,et al.A VVC intra coding method based on fast partition for coding unit[J].Telecommunications Science,2024,40(08):23-33. DOI： 10.11959/j.issn.1000-0801.2024196.

摘要

相比于高效视频编码（high efficiency video coding，HEVC）标准，新一代编码标准多功能视频编码（versatile video coding，VVC）引入了很多新的技术，其中包括四叉树（quadtree，QT）和多类型树（multi-type tree，MTT）划分，MTT划分由HEVC中的QT划分延伸而来。新划分方法提高了压缩效率，但导致编码时间急剧增加。为了降低编码复杂度，提出了一种结合深度学习方法和MTT方向早期判决的快速帧内编码算法。首先使用轻量级的卷积神经网络（convolutional neural network，CNN）对QT和部分MTT进行预测划分，其余MTT则采用提前预测MTT划分方向的方法作进一步的优化。实验结果表明，所提方法能够大幅降低编码复杂度，相比于原始编码器的编码时间减少了74.3%，且只有3.3%的码率损失，性能优于对比的方法。

Abstract

Compared to the high efficiency video coding (HEVC) standard

the latest generation coding standard

versatile video coding (VVC) has introduced many new technologies

including quadtree (QT) and multi-type tree (MTT) partitioning. MTT partition is extended from QT partition in HEVC. The new partition method increases encoding complexity

leading to a sharp increase in encoding time. To reduce encoding complexity

a fast intra coding method combining deep learning methods and early decision in the MTT direction was proposed. Firstly

a light-weight convolutional neural network (CNN) network was used to predict partition for QT and partial MTT. Then

an early prediction for MTT partition direction method was adopted for fur

ther optimization of residual MTT. Experimental results show that the proposed method can significantly reduce encoding complexity

with a 74.3% reduction in encoding time compared to the original encoder with only 3.3% rate loss. Moreover

the performance

of proposed method is superior to other comparative algorithms.

关键词

Keywords

references

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