Transcoding V-PCC Point Cloud Streams in Real-time

Author:

Rudolph Michael1ORCID,Schneegass Stefan2ORCID,Rizk Amr3ORCID

Affiliation:

1. University of Duisburg-Essen, Germany and Leibniz University Hannover, Germany

2. University of Duisburg-Essen, Germany

3. Leibniz University Hannover, Germany

Abstract

Dynamic Point Clouds are a representation for 3D immersive media that allows users to freely navigate a scene while consuming the content. However, this comes at the cost of substantial data size, requiring efficient compression techniques to make point cloud videos accessible. Addressing this, Video-based Point Cloud Compression (V-PCC) projects points into 2D patches to compress video frames, leveraging the high compression efficiency of legacy video codecs and exploiting temporal correlations in the 2D images. However, clustering and projecting points into meaningful 2D patches is computationally intensive, leading to high encoding latency in V-PCC. Applying adaptive streaming techniques, originating from traditional video streaming, multiplies the computational effort as multiple encodings of the same content are required. In this light, transcoding a compressed representation into lower qualities for dynamic adaptation to user requirements is gaining popularity. To address the high latency when employing the full decoder-encoder stack of V-PCC during transcoding, we propose RABBIT, a novel technique that only re-encodes the underlying video sub-streams. This is in contrast to slow V-PCC transcoding that reconstructs and re-encodes the raw point cloud at a new quality setting. By eliminating expensive overhead resulting from calculations based on the 3D space representation, the latency of RABBIT is bounded by the latency of transcoding the underlying video streams, allowing optimized video codec implementations to be used to meet the real time requirements of adaptive streaming systems. Our evaluations of RABBIT, using various optimized video codec implementations, shows on-par quality with the baseline V-PCC transcoding given a high-quality representation. Given unicast or multicast distribution of a point cloud stream and in-network or edge transcoders, our evaluations show the tradeoff between rate-distortion performance and the required network bandwidth.

Publisher

Association for Computing Machinery (ACM)

Reference51 articles.

1. ISO/IEC JTC 1/SC 29. 2021. ISO/IEC 23090-5:2021 Information technology — Coded representation of immersive media — Part 5: Visual volumetric video-based coding (V3C) and video-based point cloud compression (V-PCC). ISO/IEC.

2. ISO/IEC JTC 1/SC 29. 2022. ISO/IEC 23009-1:2022 Information technology — Dynamic adaptive streaming over HTTP (DASH) — Part 1: Media presentation description and segment formats. ISO/IEC.

3. FastTTPS: fast approach for video transcoding time prediction and scheduling for HTTP adaptive streaming videos

4. RATS

5. Divyashri Bhat, Amr Rizk, Michael Zink, and Ralf Steinmetz. 2017. Network Assisted Content Distribution for Adaptive Bitrate Video Streaming. In ACM on Multimedia Systems Conference (MMSys). 62–75.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3