JND-based multi-module cooperative perceptual optimization for HEVC

IF 3.7 2区 工程技术 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Displays Pub Date : 2024-06-22 DOI:10.1016/j.displa.2024.102783
Hongkui Wang , Yin Chen , Qi Ye , Zhun Li , Antong Pan , Haibing Yin , Liutao Wang , Jun Yin , Heng Jin , Li Yu , Wenyao Zhu , Xianghong Tang
{"title":"JND-based multi-module cooperative perceptual optimization for HEVC","authors":"Hongkui Wang ,&nbsp;Yin Chen ,&nbsp;Qi Ye ,&nbsp;Zhun Li ,&nbsp;Antong Pan ,&nbsp;Haibing Yin ,&nbsp;Liutao Wang ,&nbsp;Jun Yin ,&nbsp;Heng Jin ,&nbsp;Li Yu ,&nbsp;Wenyao Zhu ,&nbsp;Xianghong Tang","doi":"10.1016/j.displa.2024.102783","DOIUrl":null,"url":null,"abstract":"<div><p>Since the just noticeable distortion (JND) reflects the tolerance limit of human visual system (HVS) to coding distortion directly, JND-based perceptual video coding (PVC) plays an increasingly significant role in video compression with the developing explosion of video data. In this paper, we focus on the coupling effect among coding modules and provide a JND-based multi-module cooperative perceptual optimization (JMCPO) scheme for HEVC. The main contribution of the proposed JMCPO scheme includes the following three aspects. (1) Based on quantization distortion estimation, an adaptive perceptual quantization scheme is proposed using the binary search approach on the premise of that the quantization distortion is infinitely close to the estimated JND threshold. (2) The coupling effect among coding modules is first analyzed and a novel perceptual residual filtering scheme is presented based on statistic analysis of coupling strength. (3) The JMCPO scheme is finally developed through collaborative optimization of residual filtering, quantization and rate–distortion optimization. Experimental results show that the proposed JMCPO scheme saves more bitrate with better subjective and objective qualities.</p></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"84 ","pages":"Article 102783"},"PeriodicalIF":3.7000,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Displays","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0141938224001471","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

Abstract

Since the just noticeable distortion (JND) reflects the tolerance limit of human visual system (HVS) to coding distortion directly, JND-based perceptual video coding (PVC) plays an increasingly significant role in video compression with the developing explosion of video data. In this paper, we focus on the coupling effect among coding modules and provide a JND-based multi-module cooperative perceptual optimization (JMCPO) scheme for HEVC. The main contribution of the proposed JMCPO scheme includes the following three aspects. (1) Based on quantization distortion estimation, an adaptive perceptual quantization scheme is proposed using the binary search approach on the premise of that the quantization distortion is infinitely close to the estimated JND threshold. (2) The coupling effect among coding modules is first analyzed and a novel perceptual residual filtering scheme is presented based on statistic analysis of coupling strength. (3) The JMCPO scheme is finally developed through collaborative optimization of residual filtering, quantization and rate–distortion optimization. Experimental results show that the proposed JMCPO scheme saves more bitrate with better subjective and objective qualities.

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
基于 JND 的多模块协同感知优化 HEVC
由于刚察觉失真(JND)直接反映了人类视觉系统(HVS)对编码失真的容忍极限,随着视频数据的爆炸式增长,基于 JND 的感知视频编码(PVC)在视频压缩中发挥着越来越重要的作用。本文重点研究了编码模块之间的耦合效应,并为 HEVC 提供了一种基于 JND 的多模块协同感知优化(JMCPO)方案。所提 JMCPO 方案的主要贡献包括以下三个方面。(1) 基于量化失真估计,在量化失真无限接近估计的 JND 门限的前提下,使用二进制搜索方法提出了自适应感知量化方案。(2) 首先分析了编码模块之间的耦合效应,并在耦合强度统计分析的基础上提出了一种新的感知残差滤波方案。(3) 通过协同优化残差滤波、量化和速率失真优化,最终开发出 JMCPO 方案。实验结果表明,所提出的 JMCPO 方案能节省更多比特率,并具有更好的主观和客观质量。
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
Displays
Displays 工程技术-工程:电子与电气
CiteScore
4.60
自引率
25.60%
发文量
138
审稿时长
92 days
期刊介绍: Displays is the international journal covering the research and development of display technology, its effective presentation and perception of information, and applications and systems including display-human interface. Technical papers on practical developments in Displays technology provide an effective channel to promote greater understanding and cross-fertilization across the diverse disciplines of the Displays community. Original research papers solving ergonomics issues at the display-human interface advance effective presentation of information. Tutorial papers covering fundamentals intended for display technologies and human factor engineers new to the field will also occasionally featured.
期刊最新文献
Mambav3d: A mamba-based virtual 3D module stringing semantic information between layers of medical image slices Luminance decomposition and Transformer based no-reference tone-mapped image quality assessment GLDBF: Global and local dual-branch fusion network for no-reference point cloud quality assessment Virtual reality in medical education: Effectiveness of Immersive Virtual Anatomy Laboratory (IVAL) compared to traditional learning approaches Weighted ensemble deep learning approach for classification of gastrointestinal diseases in colonoscopy images aided by explainable AI
×
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