Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels
The video-based point cloud compression Standard (V-PCC) uses file-based projection technology to map point cloud data in three-dimensional space to two-dimensional space, creating geometric graphs and attribute graphs, then, with the help of the currently very mature two-dimensional video image cod...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11021604/ |
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| author | Fengqin Wang Juanjuan Jia Qiuwen Zhang |
| author_facet | Fengqin Wang Juanjuan Jia Qiuwen Zhang |
| author_sort | Fengqin Wang |
| collection | DOAJ |
| description | The video-based point cloud compression Standard (V-PCC) uses file-based projection technology to map point cloud data in three-dimensional space to two-dimensional space, creating geometric graphs and attribute graphs, then, with the help of the currently very mature two-dimensional video image coding technologies, such as high-efficiency video coding (HEVC), Versatile Video Coding (VVC), etc., these generated images are encoded and processed. Compared with natural video, point cloud video has a larger amount of data, and V-PCC coding improves coding efficiency but is more complex. In order to achieve effective complexity reduction and improve coding performance in the encoding process, this paper proposes a fast decision making method of coding unit (CU) partition of point cloud video geometry based on occupied pixels. In this method, the occupancy information is represented by block based occupancy map, and the early termination partition of unoccupied blocks in geometric graph is realized by generating occupied pixel marker block. Then, an unsupervised hierarchical clustering method is used for different scales of fully occupied blocks and partial occupied blocks containing occupied pixels, so as to realize the fast decision of optimal CU partition. The experimental results show that when the proposed method is used to process different dynamic point clouds in All Intra (AI) configuration, the average time of geometric graph obtained after coding is saved by 62.76%, while the BD-TotalRate only increases by 0.04% and 0.10%. Compared with other methods, the coding complexity is reduced. A good balance is also obtained for coding losses. |
| format | Article |
| id | doaj-art-04f427d7cc6048ff8655ba4c3e2cd443 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-04f427d7cc6048ff8655ba4c3e2cd4432025-08-20T02:22:36ZengIEEEIEEE Access2169-35362025-01-0113980069801810.1109/ACCESS.2025.357616011021604Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied PixelsFengqin Wang0Juanjuan Jia1https://orcid.org/0009-0007-9483-0492Qiuwen Zhang2https://orcid.org/0000-0002-8533-7088School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaSchool of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, ChinaThe video-based point cloud compression Standard (V-PCC) uses file-based projection technology to map point cloud data in three-dimensional space to two-dimensional space, creating geometric graphs and attribute graphs, then, with the help of the currently very mature two-dimensional video image coding technologies, such as high-efficiency video coding (HEVC), Versatile Video Coding (VVC), etc., these generated images are encoded and processed. Compared with natural video, point cloud video has a larger amount of data, and V-PCC coding improves coding efficiency but is more complex. In order to achieve effective complexity reduction and improve coding performance in the encoding process, this paper proposes a fast decision making method of coding unit (CU) partition of point cloud video geometry based on occupied pixels. In this method, the occupancy information is represented by block based occupancy map, and the early termination partition of unoccupied blocks in geometric graph is realized by generating occupied pixel marker block. Then, an unsupervised hierarchical clustering method is used for different scales of fully occupied blocks and partial occupied blocks containing occupied pixels, so as to realize the fast decision of optimal CU partition. The experimental results show that when the proposed method is used to process different dynamic point clouds in All Intra (AI) configuration, the average time of geometric graph obtained after coding is saved by 62.76%, while the BD-TotalRate only increases by 0.04% and 0.10%. Compared with other methods, the coding complexity is reduced. A good balance is also obtained for coding losses.https://ieeexplore.ieee.org/document/11021604/V-PCCoccupancy mapgeometric graphCU divisionhierarchical clustering |
| spellingShingle | Fengqin Wang Juanjuan Jia Qiuwen Zhang Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels IEEE Access V-PCC occupancy map geometric graph CU division hierarchical clustering |
| title | Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels |
| title_full | Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels |
| title_fullStr | Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels |
| title_full_unstemmed | Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels |
| title_short | Fast Decision Making of Point Cloud Video Geometry CU Partition Based on Occupied Pixels |
| title_sort | fast decision making of point cloud video geometry cu partition based on occupied pixels |
| topic | V-PCC occupancy map geometric graph CU division hierarchical clustering |
| url | https://ieeexplore.ieee.org/document/11021604/ |
| work_keys_str_mv | AT fengqinwang fastdecisionmakingofpointcloudvideogeometrycupartitionbasedonoccupiedpixels AT juanjuanjia fastdecisionmakingofpointcloudvideogeometrycupartitionbasedonoccupiedpixels AT qiuwenzhang fastdecisionmakingofpointcloudvideogeometrycupartitionbasedonoccupiedpixels |