Fast Run-Length Compression of Point Cloud Geometry
Tzamarias, Dion Eustathios Olivier (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Chow, Kevin (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Blanes Garcia, Ian (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)

Data:	2022
Resum:	The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1. 8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.
Ajuts:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-463 Agencia Estatal de Investigación RTI2018-095287-B-I00 Agencia Estatal de Investigación PID2021-125258OB-I00
Nota:	Altres ajuts: Universitat Autònoma de Barcelona grant 472-02-1/2017
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Context-adaptive encoder ; Point cloud geometry compression ; Run-length encoding
Publicat a:	IEEE transactions on image processing, Vol. 31 (June 2022) , p. 4490-4501, ISSN 1057-7149

DOI: 10.1109/TIP.2022.3185541
PMID: 35763478

Disponible a partir de: 2024-06-31 Postprint

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