基于轻量级全连接网络的H.266/VVC分量间预测

霍俊彦; 王丹妮; 马彦卓; 万帅; 杨付正

doi:10.11959/j.issn.1000-436x.2022031

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基于轻量级全连接网络的H.266/VVC分量间预测

学术论文 | 更新时间：2024-06-05

- 基于轻量级全连接网络的H.266/VVC分量间预测
- Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks
- 通信学报 2022年43卷第2期页码：143-155
- 作者机构：
  
  1. 西安电子科技大学ISN国家重点实验室，陕西西安 710071
  2. 西北工业大学电子信息学院，陕西西安 710072
- 作者简介：
  
  [ "霍俊彦（1982-），女，山西晋中人，博士，西安电子科技大学副教授，主要研究方向为多媒体通信、视频编码、智能信息处理" ]
  [ "王丹妮（1996-），女，陕西西安人，西安电子科技大学硕士生，主要研究方向为视频压缩编码" ]
  [ "马彦卓（1980-），女，河北深州人，博士，西安电子科技大学副教授，主要研究方向为视频编码与视频传输" ]
  [ "万帅（1979-），女，河南洛阳人，博士，西北工业大学教授、博士生导师，主要研究方向为视频编码、点云压缩及多媒体通信" ]
  [ "杨付正（1977-），男，山东德州人，博士，西安电子科技大学教授、博士生导师，主要研究方向为新一代视频压缩标准、基于深度学习的视频处理和虚拟现实" ]
- 基金信息：
  
  国家自然科学基金资助项目(62101409);国家自然科学基金资助项目(62171353)
- DOI：10.11959/j.issn.1000-436x.2022031
  中图分类号： TN911.7
- 网络首发：2022-02，
  
  纸质出版：2022-02-25
- 稿件说明：
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霍俊彦, 王丹妮, 马彦卓, 等. 基于轻量级全连接网络的H.266/VVC分量间预测[J]. 通信学报, 2022,43(2):143-155.

Junyan HUO, Danni WANG, Yanzhuo MA, et al. Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks[J]. Journal on Communications, 2022, 43(2): 143-155.
霍俊彦, 王丹妮, 马彦卓, 等. 基于轻量级全连接网络的H.266/VVC分量间预测[J]. 通信学报, 2022,43(2):143-155. DOI： 10.11959/j.issn.1000-436x.2022031.

Junyan HUO, Danni WANG, Yanzhuo MA, et al. Efficient cross-component prediction for H.266/VVC based on lightweight fully connected networks[J]. Journal on Communications, 2022, 43(2): 143-155. DOI： 10.11959/j.issn.1000-436x.2022031.

摘要

新一代视频编码标准H.266/VVC引入分量间线性模型（CCLM）预测提高压缩效率。针对亮度色度分量存在相关性却难以建模的问题，提出基于神经网络的分量间预测算法。该算法根据待预测像素与参考像素的亮度差遴选出相关性强的参考像素构成参考子集，然后将参考子集送入轻量级全连接网络获得色度预测值。实验结果表明，与 H.266/VVC 测试模型版本 10.0（VTM10.0）相比，所提算法可提高色度预测准确度，在 Y、Cb 和 Cr上可分别节省0.27%、1.54%和1.84%的码率。所提算法具有不同块尺寸和编码参数均可使用统一网络结构的优点。

Abstract

Cross-component linear model (CCLM) prediction in H.266/versatile video coding (VVC) can improve the compression efficiency.There exists high correlation between luma and chroma components while the correlation is difficult to be modeled explicitly.An algorithm for neural network based cross-component prediction (NNCCP) was proposed where reference pixels with high correlation were selected according to the luma difference between the reference pixels and the pixel to be predicted.Based on the high-correlated reference pixels and the luma difference

the predicted chroma was obtained based on lightweight fully connected networks.Experimental results demonstrate that the proposed algorithm can achieve 0.27%

1.54%

and 1.84% bitrate savings for luma and chroma components

compared with the VVC test model 10.0 (VTM10.0).Besides

a unified network can be employed to blocks with different sizes and different quantization parameters.

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references

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