Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression
The video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-the-art international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic poin...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9442761/ |
| _version_ | 1818610980705271808 |
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| author | Jieon Kim Yong-Hwan Kim |
| author_facet | Jieon Kim Yong-Hwan Kim |
| author_sort | Jieon Kim |
| collection | DOAJ |
| description | The video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-the-art international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic point cloud compression. As a brief look, V-PCC first converts the 3D input point cloud into a set of 2D patches followed by a packing process. The packing process then maps the patches into a 2D grid. Such a way allows compressing the patches utilizing the existing video coding standards. Besides the RD performance, complexity is another vital factor to consider in performance evaluations. In the V-PCC encoder, the self-time accounts for on average 15.9% and a maximum of 48.2% of the total-time, which can be a hindrance to realizing real-time V-PCC applications. One of the most computationally intensive modules of V-PCC is the grid-based refining segmentation (G-RS). Thus this paper proposes a fast G-RS method that can adaptively select voxels that need the refining segmentation. More concretely, the proposed method classifies the voxels based on the projection plane indices of 3D points and only applies the refining process to the selected voxels. Experimental results demonstrate that the proposed method reduces the complexity of the refining steps in G-RS, on average, by 60.7% and 62.5% without coding efficiency loss compared to the test model for category 2 (TMC2) version 12.0 reference software under the random access (RA) and all-intra (AI) configurations, respectively. |
| first_indexed | 2024-12-16T15:23:03Z |
| format | Article |
| id | doaj.art-d60840bf53db4a20b810790815e98d80 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T15:23:03Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d60840bf53db4a20b810790815e98d802022-12-21T22:26:34ZengIEEEIEEE Access2169-35362021-01-019800888009910.1109/ACCESS.2021.30841809442761Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud CompressionJieon Kim0https://orcid.org/0000-0002-8234-1532Yong-Hwan Kim1Korea Electronics Technology Institute, Seongnam-si, South KoreaKorea Electronics Technology Institute, Seongnam-si, South KoreaThe video-based point cloud compression (V-PCC, ISO/IEC 23090-5) is the state-of-the-art international standard for compressing dynamic point clouds developed by the moving picture experts group (MPEG). It has been achieved good rate-distortion (RD) performance by employing the 2D-based dynamic point cloud compression. As a brief look, V-PCC first converts the 3D input point cloud into a set of 2D patches followed by a packing process. The packing process then maps the patches into a 2D grid. Such a way allows compressing the patches utilizing the existing video coding standards. Besides the RD performance, complexity is another vital factor to consider in performance evaluations. In the V-PCC encoder, the self-time accounts for on average 15.9% and a maximum of 48.2% of the total-time, which can be a hindrance to realizing real-time V-PCC applications. One of the most computationally intensive modules of V-PCC is the grid-based refining segmentation (G-RS). Thus this paper proposes a fast G-RS method that can adaptively select voxels that need the refining segmentation. More concretely, the proposed method classifies the voxels based on the projection plane indices of 3D points and only applies the refining process to the selected voxels. Experimental results demonstrate that the proposed method reduces the complexity of the refining steps in G-RS, on average, by 60.7% and 62.5% without coding efficiency loss compared to the test model for category 2 (TMC2) version 12.0 reference software under the random access (RA) and all-intra (AI) configurations, respectively.https://ieeexplore.ieee.org/document/9442761/Video-based point cloud compressiondynamic point cloudfast algorithmslow-complex video encodersvoxel-based refining segmentationencoder optimization |
| spellingShingle | Jieon Kim Yong-Hwan Kim Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression IEEE Access Video-based point cloud compression dynamic point cloud fast algorithms low-complex video encoders voxel-based refining segmentation encoder optimization |
| title | Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression |
| title_full | Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression |
| title_fullStr | Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression |
| title_full_unstemmed | Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression |
| title_short | Fast Grid-Based Refining Segmentation Method in Video-Based Point Cloud Compression |
| title_sort | fast grid based refining segmentation method in video based point cloud compression |
| topic | Video-based point cloud compression dynamic point cloud fast algorithms low-complex video encoders voxel-based refining segmentation encoder optimization |
| url | https://ieeexplore.ieee.org/document/9442761/ |
| work_keys_str_mv | AT jieonkim fastgridbasedrefiningsegmentationmethodinvideobasedpointcloudcompression AT yonghwankim fastgridbasedrefiningsegmentationmethodinvideobasedpointcloudcompression |