基于视频的点云编码中的表面光场支持

Deepa Naik, S. Schwarz, V. Vadakital, Kimmo Roimela
{"title":"基于视频的点云编码中的表面光场支持","authors":"Deepa Naik, S. Schwarz, V. Vadakital, Kimmo Roimela","doi":"10.1109/MMSP48831.2020.9287115","DOIUrl":null,"url":null,"abstract":"Surface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently.","PeriodicalId":188283,"journal":{"name":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Surface Lightfield Support in Video-based Point Cloud Coding\",\"authors\":\"Deepa Naik, S. Schwarz, V. Vadakital, Kimmo Roimela\",\"doi\":\"10.1109/MMSP48831.2020.9287115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Surface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently.\",\"PeriodicalId\":188283,\"journal\":{\"name\":\"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMSP48831.2020.9287115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMSP48831.2020.9287115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

表面光场(SLF)是一组颜色向量到一组光线向量的映射,这些光线向量起源于表面上的一点。它可以在扩展现实应用程序中渲染逼真的视点。但是,表示SLF所需的数据量要大得多。因此,存储和分发slf需要一种有效的压缩表示。电影专家组(MPEG)有一个正在进行的压缩点云的标准活动。直到最近,这个活动都是针对单一纹理信息的压缩,但现在正在研究视图依赖的纹理。在本文中,我们提出了在不影响视觉质量的情况下优化视图相关颜色编码的方法。我们的结果表明,与独立编码所有纹理相比,本文提供的优化方法在全帧内配置下将编码HEVC比特率降低了64%,在随机访问配置下降低了52%。
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
Surface Lightfield Support in Video-based Point Cloud Coding
Surface light-field (SLF) is a mapping of a set of color vectors to a set of ray vectors that originate at a point on a surface. It enables rendering photo-realistic view points in extended reality applications. However, the amount of data required to represent SLF is significantly more. Therefore, storing and distributing SLFs requires an efficient compressed representation. The Motion Pictures Experts Group (MPEG) has an on-going standard activity for the compression of point clouds. Until recently, this activity was targeting compression of single texture information, but is now investigating view dependent textures. In this paper, we propose methods to optimize coding of view dependent color without compromising on the visual quality. Our results show the optimizations provided in this paper reduce coded HEVC bit rate by 64% for the all-intra configuration and 52% for the random-access configuration, when compared to coding all texture independently.
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
Leveraging Active Perception for Improving Embedding-based Deep Face Recognition Subjective Test Dataset and Meta-data-based Models for 360° Streaming Video Quality The Suitability of Texture Vibrations Based on Visually Perceived Virtual Textures in Bimodal and Trimodal Conditions DEMI: Deep Video Quality Estimation Model using Perceptual Video Quality Dimensions Learned BRIEF – transferring the knowledge from hand-crafted to learning-based descriptors
×
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