Details
| Original language | English |
|---|---|
| Title of host publication | MMSys '23 |
| Subtitle of host publication | Proceedings of the 14th ACM Multimedia Systems Conference |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 97-107 |
| Number of pages | 11 |
| ISBN (electronic) | 9798400701481 |
| Publication status | Published - 7 Jun 2023 |
| Externally published | Yes |
Abstract
Point clouds are a mature representation format for volumetric objects in 6 degrees-of-freedom multimedia streaming. To handle the massive size of point cloud data for visually satisfying immersive media, MPEG standardized Video-based Point Cloud Compression (V-PCC), leveraging existing video codecs to achieve high compression ratios. A major challenge of V-PCC is the high encoding latency, which results in fallback solutions that exchange the compression ratio for faster point cloud codecs. This encoding effort rises significantly in adaptive streaming systems, where heterogeneous user requirements translate into a set of quality representations of the media.
In this paper, we show that given one high quality media representation we can achieve live transcoding of video-based compressed point clouds to serve heterogeneous user quality requirements in real time. This stands in contrast to the slow, baseline transcoding that reconstructs and re-encodes the raw point cloud at a new quality setting. To address the high latency when employing the decoder-encoder stack of V-PCC during transcoding, we propose RABBIT, a novel technique that only re-encodes the underlying video sub-streams. This eliminates the overhead of the baseline decoding-encoding approach and decreases the latency further by applying optimized video codecs. We perform extensive evaluation of RABBIT in combination with different video codecs, showing on-par quality with the baseline V-PCC transcoding. Using a hardware-Accelerated video codec we demonstrate live transcoding performance of RABBIT and finally present a trade-off between rate, distortion and transcoding latency.
Keywords
- 6DOF, adaptive streaming, point cloud, virtual reality
ASJC Scopus subject areas
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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MMSys '23: Proceedings of the 14th ACM Multimedia Systems Conference. Association for Computing Machinery (ACM), 2023. p. 97-107.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - RABBIT
T2 - Live Transcoding of V-PCC Point Cloud Streams
AU - Rudolph, Michael
AU - Schneegass, Stefan
AU - Rizk, Amr
N1 - Publisher Copyright: © 2023 ACM.
PY - 2023/6/7
Y1 - 2023/6/7
N2 - Point clouds are a mature representation format for volumetric objects in 6 degrees-of-freedom multimedia streaming. To handle the massive size of point cloud data for visually satisfying immersive media, MPEG standardized Video-based Point Cloud Compression (V-PCC), leveraging existing video codecs to achieve high compression ratios. A major challenge of V-PCC is the high encoding latency, which results in fallback solutions that exchange the compression ratio for faster point cloud codecs. This encoding effort rises significantly in adaptive streaming systems, where heterogeneous user requirements translate into a set of quality representations of the media.In this paper, we show that given one high quality media representation we can achieve live transcoding of video-based compressed point clouds to serve heterogeneous user quality requirements in real time. This stands in contrast to the slow, baseline transcoding that reconstructs and re-encodes the raw point cloud at a new quality setting. To address the high latency when employing the decoder-encoder stack of V-PCC during transcoding, we propose RABBIT, a novel technique that only re-encodes the underlying video sub-streams. This eliminates the overhead of the baseline decoding-encoding approach and decreases the latency further by applying optimized video codecs. We perform extensive evaluation of RABBIT in combination with different video codecs, showing on-par quality with the baseline V-PCC transcoding. Using a hardware-Accelerated video codec we demonstrate live transcoding performance of RABBIT and finally present a trade-off between rate, distortion and transcoding latency.
AB - Point clouds are a mature representation format for volumetric objects in 6 degrees-of-freedom multimedia streaming. To handle the massive size of point cloud data for visually satisfying immersive media, MPEG standardized Video-based Point Cloud Compression (V-PCC), leveraging existing video codecs to achieve high compression ratios. A major challenge of V-PCC is the high encoding latency, which results in fallback solutions that exchange the compression ratio for faster point cloud codecs. This encoding effort rises significantly in adaptive streaming systems, where heterogeneous user requirements translate into a set of quality representations of the media.In this paper, we show that given one high quality media representation we can achieve live transcoding of video-based compressed point clouds to serve heterogeneous user quality requirements in real time. This stands in contrast to the slow, baseline transcoding that reconstructs and re-encodes the raw point cloud at a new quality setting. To address the high latency when employing the decoder-encoder stack of V-PCC during transcoding, we propose RABBIT, a novel technique that only re-encodes the underlying video sub-streams. This eliminates the overhead of the baseline decoding-encoding approach and decreases the latency further by applying optimized video codecs. We perform extensive evaluation of RABBIT in combination with different video codecs, showing on-par quality with the baseline V-PCC transcoding. Using a hardware-Accelerated video codec we demonstrate live transcoding performance of RABBIT and finally present a trade-off between rate, distortion and transcoding latency.
KW - 6DOF
KW - adaptive streaming
KW - point cloud
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85163679403&partnerID=8YFLogxK
U2 - 10.1145/3587819.3590978
DO - 10.1145/3587819.3590978
M3 - Conference contribution
SP - 97
EP - 107
BT - MMSys '23
PB - Association for Computing Machinery (ACM)
ER -