采用轻量级卷积神经网络的H.266/通用视频编码跨分量预测

邹承益; 万帅; 朱志伟; 尹宇杰

doi:10.7652/xjtuxb202502018

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采用轻量级卷积神经网络的H.266/通用视频编码跨分量预测

更新时间：2026-01-06

- 采用轻量级卷积神经网络的H.266/通用视频编码跨分量预测
- Cross-Component Prediction for H.266/Versatile Video Coding Based on Lightweight Convolutional Neural Network
- 西安交通大学学报 2025年59卷第2期页码：180-188
- 作者机构：
  
  1.西北工业大学电子信息学院， 710129，西安
  2.皇家墨尔本理工大学工程学院， VIC3001，澳大利亚墨尔本
- 作者简介：
  
  邹承益（1996—），男，博士生；
  万帅（通信作者），女，教授，博士生导师。
- 基金信息：
  
  陕西省自然科学基础研究计划资助项目(2024JC-YBMS-463);TCL科技创新基金资助项目
- DOI：10.7652/xjtuxb202502018
  中图分类号： TN919.8
- 收稿：2024-05-09，
  
  网络首发：2024-09-12，
  
  纸质出版：2025-02-10
- 稿件说明：
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邹承益, 万帅, 朱志伟, 等. 采用轻量级卷积神经网络的H.266/通用视频编码跨分量预测[J]. 西安交通大学学报, 2025,59(2):180-188.

ZOU Chengyi, WAN Shuai, ZHU Zhiwei, et al. Cross-Component Prediction for H.266/Versatile Video Coding Based on Lightweight Convolutional Neural Network[J]. Journal of Xi’an Jiaotong University, 2025, 59(2): 180-188.
邹承益, 万帅, 朱志伟, 等. 采用轻量级卷积神经网络的H.266/通用视频编码跨分量预测[J]. 西安交通大学学报, 2025,59(2):180-188. DOI： 10.7652/xjtuxb202502018.

ZOU Chengyi, WAN Shuai, ZHU Zhiwei, et al. Cross-Component Prediction for H.266/Versatile Video Coding Based on Lightweight Convolutional Neural Network[J]. Journal of Xi’an Jiaotong University, 2025, 59(2): 180-188. DOI： 10.7652/xjtuxb202502018.

摘要

为提高新一代通用视频编码标准（H.266/VVC）中色度帧内预测的准确度，提出了采用轻量级卷积神经网络的跨分量预测方法。设计了亮度模块和边界模块，从亮度和色度参考样本中提取特征。设计了注意力模块，构建当前亮度参考样本和边界亮度参考样本之间的空间关系，并应用于边界色度参考样本生成色度预测样本。为降低编解码复杂度，设计网络在二维完成特征融合和预测，优化了现有的同组参数处理不同块大小的训练策略。并且，引入宽度可变卷积，根据不同的块大小调整网络参数。实验结果表明：与H.266/VVC测试模型VTM18.0相比，所提网络在Y（亮度分量）、Cb（蓝色色度分量）、Cr（红色色度分量）上分别实现了0.30%、2.46%、2.25%的码率节省。与其他基于卷积神经网络的跨分量预测方法相比，有效地降低了网络参数和推理复杂度，分别节省了约10%的编码时间和19%的解码时间。

Abstract

To improve the accuracy of intra chroma prediction in H.266/versatile video coding (VVC)

a cross-component prediction method based on lightweight convolutional neural network was proposed in this paper. The luma module and chroma module were designed to extract features from luma and chroma reference samples

and the attention module was designed to leverage the attention mechanism to construct the spatial correlation between the current luma reference samples and the boundary luma reference samples. Finally

the attention mask was applied to the boundary chroma reference samples to generate chroma prediction value. To reduce the encoding and decoding complexity

the feature fusion and prediction in the network were achieved in two dimensions

the existing training strategy with shared parameters to handle variable block sizes was improved

and slimmable convolutions were introduced to adjust network parameters according to different block sizes. The experimental results show that the proposed algorithm achieved 0.30%/2.46%/2.25% BD-rate reduction on the Y/Cb/Cr component

respectively

compared with the H.266/VVC test model VTM18.0. Compared with other convolutional neural networks-based cross-component prediction methods

the proposed method effectively reduced the network parameters and inference complexity

saving 10% encoding time and 19% decoding time.

关键词

Keywords

references

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