Progressive Coding for Deep Learning based Point Cloud Attribute Compression

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Autorschaft

  • Michael Rudolph
  • Aron Riemenschneider
  • Amr Rizk
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Details

OriginalspracheEnglisch
Titel des SammelwerksMMVE 2024 - Proceedings of the 2024 16th International Workshop on Immersive Mixed and Virtual Environment Systems
Seiten78 - 84
Seitenumfang7
ISBN (elektronisch)9798400706189
PublikationsstatusVeröffentlicht - 15 Apr. 2024

Abstract

Progressive coding is a valuable technique for networked immersive media. As users approach objects in an immersive environment, progressive coding enables a gradual improvement of content quality. This effectively reduces bandwidth consumption compared to non-progressive methods that require to fully exchange a content representation by an independent, new representation. In this work, we introduce an approach to progressively code point cloud attributes in a learned manner by compressing quantization residuals of each preceding representation through a learned, lightweight transformation in the entropy bottleneck. This allows to progressively reduce quantization errors using a single model in an end-to-end learning manner given the quantization residuals. In contrast to the state of the art that conditions the compression on a fixed rate-distortion, i.e. it requires an ensemble of models to build an adaptive streaming system, our approach requires only a single model during compression and decompression. We present preliminary results of our method, showing bandwidth savings for the scenario of a user approaching an object and gradually transitioning from low to high quality representations.

ASJC Scopus Sachgebiete

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Progressive Coding for Deep Learning based Point Cloud Attribute Compression. / Rudolph, Michael; Riemenschneider, Aron; Rizk, Amr.
MMVE 2024 - Proceedings of the 2024 16th International Workshop on Immersive Mixed and Virtual Environment Systems. 2024. S. 78 - 84.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Rudolph, M, Riemenschneider, A & Rizk, A 2024, Progressive Coding for Deep Learning based Point Cloud Attribute Compression. in MMVE 2024 - Proceedings of the 2024 16th International Workshop on Immersive Mixed and Virtual Environment Systems. S. 78 - 84. https://doi.org/10.1145/3652212.3652217
Rudolph, M., Riemenschneider, A., & Rizk, A. (2024). Progressive Coding for Deep Learning based Point Cloud Attribute Compression. In MMVE 2024 - Proceedings of the 2024 16th International Workshop on Immersive Mixed and Virtual Environment Systems (S. 78 - 84) https://doi.org/10.1145/3652212.3652217
Rudolph M, Riemenschneider A, Rizk A. Progressive Coding for Deep Learning based Point Cloud Attribute Compression. in MMVE 2024 - Proceedings of the 2024 16th International Workshop on Immersive Mixed and Virtual Environment Systems. 2024. S. 78 - 84 doi: 10.1145/3652212.3652217
Rudolph, Michael ; Riemenschneider, Aron ; Rizk, Amr. / Progressive Coding for Deep Learning based Point Cloud Attribute Compression. MMVE 2024 - Proceedings of the 2024 16th International Workshop on Immersive Mixed and Virtual Environment Systems. 2024. S. 78 - 84
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