Details
Original language | English |
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Title of host publication | Proceedings of the 16th ACM Multimedia Systems Conference |
Subtitle of host publication | MMSys 2025 |
Pages | 328-334 |
Number of pages | 7 |
ISBN (electronic) | 9798400714672 |
Publication status | Published - 31 Mar 2025 |
Event | 16th ACM Multimedia Systems Conference, MMSys 2025 - Stellenbosch, South Africa Duration: 31 Mar 2025 → 4 Apr 2025 |
Abstract
Learned point cloud compression methods have achieved rate-distortion performance, which is comparable to or higher than conventional approaches. However, this often comes at the cost of high hardware requirements and thus low throughput during encoding and decoding. In this paper, we present an adaptive bitrate point cloud streaming system utilizing learned compression. While other learned compression techniques require to split geometry and attributes, resulting in high encoding latency, we deploy a unified model to handle both modalities together, which drastically reduces the coding complexity. We explore the capabilities of the learned encoder to derive multiple quality representations with only re-running a fraction of the encoding steps, making it a suitable fit for adaptive bitrate streaming. Furthermore, we ablate the encoding latency of each component in the encoder and decoder stack, identifying bottlenecks in the process.
Keywords
- adaptive bitrate streaming, learned point cloud compression, point cloud streaming
ASJC Scopus subject areas
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
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Proceedings of the 16th ACM Multimedia Systems Conference: MMSys 2025. 2025. p. 328-334.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Learned Compression in Adaptive Point Cloud Streaming
T2 - 16th ACM Multimedia Systems Conference, MMSys 2025
AU - Rudolph, Michael
AU - Rizk, Amr
N1 - Publisher Copyright: © 2025 Copyright held by the owner/author(s).
PY - 2025/3/31
Y1 - 2025/3/31
N2 - Learned point cloud compression methods have achieved rate-distortion performance, which is comparable to or higher than conventional approaches. However, this often comes at the cost of high hardware requirements and thus low throughput during encoding and decoding. In this paper, we present an adaptive bitrate point cloud streaming system utilizing learned compression. While other learned compression techniques require to split geometry and attributes, resulting in high encoding latency, we deploy a unified model to handle both modalities together, which drastically reduces the coding complexity. We explore the capabilities of the learned encoder to derive multiple quality representations with only re-running a fraction of the encoding steps, making it a suitable fit for adaptive bitrate streaming. Furthermore, we ablate the encoding latency of each component in the encoder and decoder stack, identifying bottlenecks in the process.
AB - Learned point cloud compression methods have achieved rate-distortion performance, which is comparable to or higher than conventional approaches. However, this often comes at the cost of high hardware requirements and thus low throughput during encoding and decoding. In this paper, we present an adaptive bitrate point cloud streaming system utilizing learned compression. While other learned compression techniques require to split geometry and attributes, resulting in high encoding latency, we deploy a unified model to handle both modalities together, which drastically reduces the coding complexity. We explore the capabilities of the learned encoder to derive multiple quality representations with only re-running a fraction of the encoding steps, making it a suitable fit for adaptive bitrate streaming. Furthermore, we ablate the encoding latency of each component in the encoder and decoder stack, identifying bottlenecks in the process.
KW - adaptive bitrate streaming
KW - learned point cloud compression
KW - point cloud streaming
UR - http://www.scopus.com/inward/record.url?scp=105005026528&partnerID=8YFLogxK
U2 - 10.1145/3712676.3719266
DO - 10.1145/3712676.3719266
M3 - Conference contribution
AN - SCOPUS:105005026528
SP - 328
EP - 334
BT - Proceedings of the 16th ACM Multimedia Systems Conference
Y2 - 31 March 2025 through 4 April 2025
ER -