可扩展HEVC中广义残差预测的探索

E. François, Christophe Gisquet, Jonathan Taquet, G. Laroche, P. Onno
{"title":"可扩展HEVC中广义残差预测的探索","authors":"E. François, Christophe Gisquet, Jonathan Taquet, G. Laroche, P. Onno","doi":"10.1109/VCIP.2013.6706449","DOIUrl":null,"url":null,"abstract":"After having issued the version 1 of the new video coding standard HEVC, ISO-MPEG and ITU-T VCEG groups are specifying its scalable extension. The candidate schemes are based on a multi-layer multi-loop coding framework, exploiting inter-layer texture and motion prediction and full base layer picture decoding. Several inter-layer prediction tools have been explored, implemented either using high-level syntax or block-level core HEVC design changes. One of these tools, Generalized Residual Prediction (GRP), has been extensively studied during several meeting cycles. It is based on second order residual prediction, exploiting motion compensation prediction residual in the base layer. This paper is focused on this new mode. The principle of GRP is described with an analysis of several implementation variants completed by a complexity analysis. Performance of these different implementations is provided, showing that noticeable gains can be obtained without significant complexity increase compared to a simple scalable design comprising only texture and motion inter-layer prediction.","PeriodicalId":407080,"journal":{"name":"2013 Visual Communications and Image Processing (VCIP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Exploration of Generalized Residual Prediction in scalable HEVC\",\"authors\":\"E. François, Christophe Gisquet, Jonathan Taquet, G. Laroche, P. Onno\",\"doi\":\"10.1109/VCIP.2013.6706449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"After having issued the version 1 of the new video coding standard HEVC, ISO-MPEG and ITU-T VCEG groups are specifying its scalable extension. The candidate schemes are based on a multi-layer multi-loop coding framework, exploiting inter-layer texture and motion prediction and full base layer picture decoding. Several inter-layer prediction tools have been explored, implemented either using high-level syntax or block-level core HEVC design changes. One of these tools, Generalized Residual Prediction (GRP), has been extensively studied during several meeting cycles. It is based on second order residual prediction, exploiting motion compensation prediction residual in the base layer. This paper is focused on this new mode. The principle of GRP is described with an analysis of several implementation variants completed by a complexity analysis. Performance of these different implementations is provided, showing that noticeable gains can be obtained without significant complexity increase compared to a simple scalable design comprising only texture and motion inter-layer prediction.\",\"PeriodicalId\":407080,\"journal\":{\"name\":\"2013 Visual Communications and Image Processing (VCIP)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Visual Communications and Image Processing (VCIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VCIP.2013.6706449\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Visual Communications and Image Processing (VCIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VCIP.2013.6706449","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

在发布了新的视频编码标准HEVC的第一版之后,ISO-MPEG和ITU-T VCEG组正在指定其可扩展的扩展。候选方案基于多层多环路编码框架,利用层间纹理和运动预测以及全基础层图像解码。已经探索了几个层间预测工具,使用高级语法或块级核心HEVC设计更改来实现。其中一个工具,广义残差预测(GRP),在几个会议周期中得到了广泛的研究。它是基于二阶残差预测,利用运动补偿预测残差在基础层。本文就是对这种新模式的研究。通过复杂性分析,对几种实现变量进行了分析,描述了GRP的原理。提供了这些不同实现的性能,表明与仅包含纹理和运动层间预测的简单可扩展设计相比,可以在不显著增加复杂性的情况下获得显着的增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Exploration of Generalized Residual Prediction in scalable HEVC
After having issued the version 1 of the new video coding standard HEVC, ISO-MPEG and ITU-T VCEG groups are specifying its scalable extension. The candidate schemes are based on a multi-layer multi-loop coding framework, exploiting inter-layer texture and motion prediction and full base layer picture decoding. Several inter-layer prediction tools have been explored, implemented either using high-level syntax or block-level core HEVC design changes. One of these tools, Generalized Residual Prediction (GRP), has been extensively studied during several meeting cycles. It is based on second order residual prediction, exploiting motion compensation prediction residual in the base layer. This paper is focused on this new mode. The principle of GRP is described with an analysis of several implementation variants completed by a complexity analysis. Performance of these different implementations is provided, showing that noticeable gains can be obtained without significant complexity increase compared to a simple scalable design comprising only texture and motion inter-layer prediction.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
New motherwavelet for pattern detection in IR image Improved disparity vector derivation in 3D-HEVC Learning non-negative locality-constrained Linear Coding for human action recognition Wavelet based smoke detection method with RGB Contrast-image and shape constrain Joint image denoising using self-similarity based low-rank approximations
×
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