一种基于图神经网络和统计分析的VVC帧内编码快速算法

黎天送; 刘昊坤; 崔少国; 刘姝岑; 陈艳; 王鸿奎

doi:10.11959/j.issn.1000-0801.2024213

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一种基于图神经网络和统计分析的VVC帧内编码快速算法

研究与开发 | 更新时间：2024-11-25

- 一种基于图神经网络和统计分析的VVC帧内编码快速算法
- A fast VVC intra-coding algorithm based on graph neural network and statistical analysis
- 电信科学 2024年40卷第9期页码：109-122
- 作者机构：
  
  1.重庆师范大学，重庆 401331
  2.杭州电子科技大学，浙江杭州 310018
- 作者简介：
  
  [ "黎天送（1987- ），男，博士，重庆师范大学讲师，主要研究方向为图像/视频编码、多视点视频编码、多媒体信号处理、人工智能。" ]
  [ "崔少国（1974- ），男，重庆师范大学教授，主要研究方向为大数据与人工智能、计算机视觉（CV）与自然语言处理（NLP）、医学影像智能分析。" ]
  [ "刘姝岑（1999- ），女，重庆师范大学硕士生，主要研究方向为3D-HEVC 视频编码、深度学习。" ]
  [ "陈艳（2000- ），女，重庆师范大学硕士生，主要研究方向为H.266/VVC视频编码、360°全景视频编码、深度学习。" ]
  [ "王鸿奎（1990- ），男，现就职于杭州电子科技大学丽水研究院，主要研究方向为标准视频压缩、感知视频压缩以及智能视频压缩。" ]
- 基金信息：
  
  重庆市科技局自然基金项目;The Natural Science Foundation Project of Chongqing Science and Technology Bureau(CSTB2022NSCQ-MSX1231);重庆市教委青年项目;The Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202200519);重庆师范大学人才基金项目;The Talents Fund Project of Chongqing Normal University(21XLB031)
- DOI：10.11959/j.issn.1000-0801.2024213
  中图分类号： TP391
- 收稿日期：2024-03-27，
  
  修回日期：2024-09-11，
  
  纸质出版日期：2024-09-20
- 稿件说明：
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黎天送,刘昊坤,崔少国等.一种基于图神经网络和统计分析的VVC帧内编码快速算法[J].电信科学,2024,40(09):109-122.

LI Tiansong,LIU Haokun,CUI Shaoguo,et al.A fast VVC intra-coding algorithm based on graph neural network and statistical analysis[J].Telecommunications Science,2024,40(09):109-122.
黎天送,刘昊坤,崔少国等.一种基于图神经网络和统计分析的VVC帧内编码快速算法[J].电信科学,2024,40(09):109-122. DOI： 10.11959/j.issn.1000-0801.2024213.

LI Tiansong,LIU Haokun,CUI Shaoguo,et al.A fast VVC intra-coding algorithm based on graph neural network and statistical analysis[J].Telecommunications Science,2024,40(09):109-122. DOI： 10.11959/j.issn.1000-0801.2024213.

摘要

多功能视频编码（versatile video coding，VVC）作为最新一代的视频编码标准，通过引入多种高效的编码工具进一步提升了视频编码性能。然而，VVC标准引入了四叉树加多类型树（quadtree plus multi-type tree，QTMT）划分结构，并将帧内预测模式从35种扩展到67种，导致编码复杂度急剧上升。为降低VVC的帧内编码复杂度，首先，提出了一种基于图神经网络的帧内编码单元（coding unit，CU）划分快速算法，该算法利用高效的图神经网络模型直接预测CU的最优划分模式，从而跳过冗余的CU划分遍历。其次，提出了一种基于空间相关性和纹理特征的帧内模式选择快速算法，该算法利用平均方向方差和Sobel梯度算子确定纹理方向，并跳过部分角度预测模式，同时结合预测模式间的相关性精简率失真模式列表。实验结果表明，该算法能够在BDBR（bjontegaard delta bit rate）上升2.29%的代价下，节省64.04%的编码时间。

Abstract

VVC as the latest generation of video coding standards

further improves video compression quality by introducing a variety of efficient coding tools. However

the VVC standard introduces the QTMT division structure and expands the intra prediction modes from 35 to 67

resulting in a sharp increase in coding complexity. Firstly

a fast algorithm for intra-frame coding unit (CU) division based on graph neural network was proposed

in order to reduce the complexity of intra-frame coding of VVC. An efficient graph neural network model was used to directly predict the optimal partition mode of CU

thus skipping redundant CU partition traversal. Secondly

a fast algorithm for intra-frame mode selection based on spatial correlation and texture features was proposed. The average direction variance and Sobel gradient operator were used to determine the texture direction

some angle prediction modes were skipped

and the correlation between prediction modes to streamline the rate-distortion mode list were combined. Experimental results show that this algorithm can save 64.04% of encoding time at the cost of increasing BDBR by 2.29%.

关键词

Keywords

references

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