Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected geometry and texture video frames are then encoded using modern video coding standard like HEVC. However, HEVC encoder is unable to exploit the global commonality that exists within a geometry frame and between successive geometry frames to a greater extent. This is because in HEVC, the current frame partitioning starts from a rigid 64 × 64 pixels level without considering the structure of the scene need be coded. In this paper, an improved commonality modeling framework is proposed, by leveraging on cuboid-based frame partitioning, to encode point cloud geometry frames. The associated frame-partitioning scheme is based on statistical properties of the current geometry frame and therefore yields a flexible block partitioning structure composed of cuboids. Additionally, the proposed commonality modeling approach is computationally efficient and has a compact representation. Experimental results show that if the V-PCC reference encoder is augmented by the proposed commonality modeling technique, a bit rate savings of 2.71% and 4.25% are achieved for full body and upper body of human point clouds’ geometry sequences respectively.

Original languageEnglish
Title of host publication2021 IEEE International conference on image processing (ICIP) proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665441155
ISBN (Print)9781665431026
Publication statusPublished - 23 Aug 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sept 202122 Sept 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States


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