Joint reference frame synthesis and post filter enhancement for Versatile Video Coding

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Visual Communication and Image Representation Pub Date : 2025-03-13 DOI:10.1016/j.jvcir.2025.104433
Weijie Bao , Yuantong Zhang , Jianghao Jia , Zhenzhong Chen , Shan Liu
{"title":"Joint reference frame synthesis and post filter enhancement for Versatile Video Coding","authors":"Weijie Bao ,&nbsp;Yuantong Zhang ,&nbsp;Jianghao Jia ,&nbsp;Zhenzhong Chen ,&nbsp;Shan Liu","doi":"10.1016/j.jvcir.2025.104433","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents the joint reference frame synthesis (RFS) and post-processing filter enhancement (PFE) for Versatile Video Coding (VVC), aiming to explore the combination of different neural network-based video coding (NNVC) tools to better utilize the hierarchical bi-directional coding structure of VVC. Both RFS and PFE utilize the Space–Time Enhancement Network (STENet), which receives two input frames with artifacts and produces two enhanced frames with suppressed artifacts, along with an intermediate synthesized frame. STENet comprises two pipelines, the synthesis pipeline and the enhancement pipeline, tailored for different purposes. During RFS, two reconstructed frames are sent into STENet’s synthesis pipeline to synthesize a virtual reference frame, similar to the current to-be-coded frame. The synthesized frame serves as an additional reference frame inserted into the reference picture list (RPL). During PFE, two reconstructed frames are fed into STENet’s enhancement pipeline to alleviate their artifacts and distortions, resulting in enhanced frames with reduced artifacts and distortions. To reduce inference complexity, we propose joint inference of RFS and PFE (JISE), achieved through a single execution of STENet. Integrated into the VVC reference software VTM-15.0, RFS, PFE, and JISE are coordinated within a novel Space–Time Enhancement Window (STEW) under Random Access (RA) configuration. The proposed method could achieve –7.34%/–17.21%/–16.65% BD-rate (PSNR) on average for three components under RA configuration.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"108 ","pages":"Article 104433"},"PeriodicalIF":2.6000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Communication and Image Representation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047320325000471","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper presents the joint reference frame synthesis (RFS) and post-processing filter enhancement (PFE) for Versatile Video Coding (VVC), aiming to explore the combination of different neural network-based video coding (NNVC) tools to better utilize the hierarchical bi-directional coding structure of VVC. Both RFS and PFE utilize the Space–Time Enhancement Network (STENet), which receives two input frames with artifacts and produces two enhanced frames with suppressed artifacts, along with an intermediate synthesized frame. STENet comprises two pipelines, the synthesis pipeline and the enhancement pipeline, tailored for different purposes. During RFS, two reconstructed frames are sent into STENet’s synthesis pipeline to synthesize a virtual reference frame, similar to the current to-be-coded frame. The synthesized frame serves as an additional reference frame inserted into the reference picture list (RPL). During PFE, two reconstructed frames are fed into STENet’s enhancement pipeline to alleviate their artifacts and distortions, resulting in enhanced frames with reduced artifacts and distortions. To reduce inference complexity, we propose joint inference of RFS and PFE (JISE), achieved through a single execution of STENet. Integrated into the VVC reference software VTM-15.0, RFS, PFE, and JISE are coordinated within a novel Space–Time Enhancement Window (STEW) under Random Access (RA) configuration. The proposed method could achieve –7.34%/–17.21%/–16.65% BD-rate (PSNR) on average for three components under RA configuration.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
本文介绍了用于多功能视频编码(VVC)的联合参考帧合成(RFS)和后处理滤波增强(PFE),旨在探索基于神经网络的不同视频编码(NNVC)工具的组合,以更好地利用 VVC 的分层双向编码结构。RFS 和 PFE 都利用了时空增强网络(STENet),该网络接收两个带有伪像的输入帧,并生成两个抑制了伪像的增强帧和一个中间合成帧。STENet 包括两个流水线,即合成流水线和增强流水线,分别用于不同的目的。在 RFS 过程中,两个重建帧被送入 STENet 的合成管道,合成一个虚拟参考帧,与当前待编码帧相似。合成的帧将作为额外的参考帧插入参考图像列表 (RPL)。在 PFE 过程中,两个重建帧被送入 STENet 的增强管道,以减少其伪影和失真,从而得到减少伪影和失真的增强帧。为了降低推理的复杂性,我们提出了 RFS 和 PFE 的联合推理(JISE),通过 STENet 的单次执行来实现。集成到 VVC 参考软件 VTM-15.0 中的 RFS、PFE 和 JISE 在随机存取(RA)配置下的新型时空增强窗口(STEW)内进行协调。在 RA 配置下,所提出的方法可使三个组件的 BD 速率(PSNR)平均达到-7.34%/-17.21%/-16.65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation 工程技术-计算机:软件工程
CiteScore
5.40
自引率
11.50%
发文量
188
审稿时长
9.9 months
期刊介绍: The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.
期刊最新文献
Cell tracking-by-detection using elliptical bounding boxes Transformer-based weakly supervised 3D human pose estimation Joint reference frame synthesis and post filter enhancement for Versatile Video Coding Two-tiered Spatio-temporal Feature Extraction for Micro-expression Classification A robust and adaptive framework with space–time memory networks for Visual Object Tracking
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1