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
- 网络出版日期：2022-02，
  
  纸质出版日期：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

ITU-T . ITU-T Recommendation H.266 and ISO/IEC 23090-3 VVC standard [S ] . 2020 .

ALBRECHT M , BARTNIK C . Description of SDR,HDR,and 360° video coding technology proposal by Fraunhofer HHI [R ] . JVET-J0014 , 2018 .

YE Y , BOYCE J M , HANHART P . Omnidirectional 360° video coding technology in responses to the joint call for proposals on video compression with capability beyond HEVC [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2020 , 30 ( 5 ): 1241 - 1252 .

FRANÇOIS E , SEGALL C A , TOURAPIS A M , et al . High dynamic range video coding technology in responses to the joint call for proposals on video compression with capability beyond HEVC [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2020 , 30 ( 5 ): 1253 - 1266 .

ITU-T . ITU-T Recommendation H.265 and ISO/IEC 23008-2 HEVC standard.High efficiency video coding [S ] . 2013 .

BROSS B , CHEN J L , OHM J R , et al . Developments in international video coding standardization after AVC,with an overview of versatile video coding (VVC) [J ] . Proceedings of the IEEE , 2021 , 109 ( 9 ): 1463 - 1493 .

朱秀昌 , 唐贵进 . H.266/VVC:新一代通用视频编码国际标准 [J ] . 南京邮电大学学报(自然科学版) , 2021 , 41 ( 2 ): 1 - 11 .

ZHU X C , TANG G J.H . 266/VVC:versatile video coding international standard [J ] . Journal of Nanjing University of Posts and Telecommunications (Natural Science) , 2021 , 41 ( 2 ): 1 - 11 .

HUANG Y W , HSU C W , CHEN C Y , et al . A VVC proposal with quaternary tree plus binary-ternary tree coding block structure and advanced coding techniques [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2020 , 30 ( 5 ): 1311 - 1325 .

SCHÄFER M , STALLENBERGER B , PFAFF J , et al . Efficient fixed-point implementation of matrix-based intra prediction [C ] // Proceedings of 2020 IEEE International Conference on Image Processing . Piscataway:IEEE Press , 2020 : 3364 - 3368 .

PFAFF J , SCHWARZ H , MARPE D , et al . Video compression using generalized binary partitioning,trellis coded quantization,perceptually optimized encoding,and advanced prediction and transform coding [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2019 , 30 ( 5 ): 1281 - 1295 .

LEE S H , CHO N I . Intra prediction method based on the linear relationship between the channels for YUV 4:2:0 intra coding [C ] // Proceedings of 2009 16th IEEE International Conference on Image Processing . Piscataway:IEEE Press , 2009 : 1037 - 1040 .

ZHANG K , CHEN Y W , ZHANG L , et al . An improved framework of affine motion compensation in video coding [J ] . IEEE Transactions on Image Processing , 2019 , 28 ( 3 ): 1456 - 1469 .

GAO H , ESENLIK S , ALSHINA E , et al . Geometric partitioning mode in versatile video coding:algorithm review and analysis [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2021 , 31 ( 9 ): 3603 - 3617 .

NASER K , POIRIER T , LEANNEC L F . Non-CE6:shape adaptive transform selection for ISP,SBT and MTS [R ] . JVET-N0388-v5 , 2019 .

KOO M , SALEHIFAR M , LIM J , et al . Low frequency non-separable transform (LFNST) [C ] // Proceedings of 2019 Picture Coding Symposium (PCS) . Piscataway:IEEE Press , 2019 : 1 - 5 .

TSAI C Y , CHEN C Y , YAMAKAGE T , et al . Adaptive loop filtering for video coding [J ] . IEEE Journal of Selected Topics in Signal Processing , 2013 , 7 ( 6 ): 934 - 945 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

KIM J , LEE J K , LEE K M . Accurate image super-resolution using very deep convolutional networks [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 1646 - 1654 .

LIU D , LI Y , LIN J P , et al . Deep learning-based video coding [J ] . ACM Computing Surveys , 2021 , 53 ( 1 ): 1 - 35 .

MA S W , ZHANG X F , JIA C M , et al . Image and video compression with neural networks:a review [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2020 , 30 ( 6 ): 1683 - 1698 .

MINNEN D , BALLÉ J , TODERICI G . Joint autoregressive and hierarchical priors for learned image compression [J ] . arXiv Preprint,arXiv:1809.02736 , 2018 .

CHEN J , YE Y , KIM S . Algorithm description for versatile video coding and test model 10 (VTM 10) [R ] . JVET-S2002 , 2020 .

CHIEN W J , BOYCE J , CHEN Y W , et al . JVET AHG report:tool reporting procedure (AHG13) [R ] . JVET-T0013 , 2020 .

ZHANG K , CHEN J , ZHANG L , et al . Enhanced cross-component linear model for chroma intra-prediction in video coding [J ] . IEEE Transactions on Image Processing , 2018 , 27 ( 8 ): 3983 - 3997 .

MA X , YANG H , CHEN J . Tests of cross-component linear model in BMS1.0 [R ] . JVET-K0190 , 2018 .

MA X , YANG H , CHEN J . CE3:CCLM/MDLM using simplified coefficients derivation method (Test 5.6.1,5.6.2 and 5.6.3) [R ] . JVET-L0340 , 2018 .

LAROCHE G , TAQUET J , GISQUET C , et al . CE3:cross-component linear model simplification (Test 5.1) [R ] . JVET-L0191 , 2018 .

HUO J Y , MA Y Z , WAN S , et al . CE3-1.5:CCLM derived from four neighbouring samples [R ] . JVET-N0271 , 2019 .

BLANCH M G , BLASI S , SMEATON A , et al . Chroma intra prediction with attention-based CNN architectures [C ] // Proceedings of 2020 IEEE International Conference on Image Processing . Piscataway:IEEE Press , 2020 : 783 - 787 .

ZHU L W , ZHANG Y , WANG S Q , et al . Deep learning-based chroma prediction for intra versatile video coding [J ] . IEEE Transactions on Circuits and Systems for Video Technology , 2021 , 31 ( 8 ): 3168 - 3181 .

LI Y , LI L , LI Z , et al . A hybrid neural network for chroma intra prediction [C ] // Proceedings of 2018 25th IEEE International Conference on Image Processing . Piscataway:IEEE Press , 2018 : 1797 - 1801 .

TIMOFTE R , AGUSTSSON E , GOOL L V , et al . NTIRE 2017 challenge on single image super-resolution:methods and results [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2017 : 1110 - 1121 .

BOYCE J , SUEHRING K , LI L , et al . JVET common test conditions and software reference configurations [R ] . JVET-J1010 , 2018 .

BOSSEN F . On reporting combined YUV BD rates [R ] . JVET-N0341 , 2019 .

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