Improving Optimal Binarization with Update On-the-fly in G-PCC Entropy Coding: Probability Initialization and Adaptive Bounds Setting for Context Models
Geometry-based point cloud compression (G-PCC) uses Context-based Adaptive Binary Arithmetic Coding to encode the geometry and attribute information. The context information is built in context models for entropy coding. G-PCC adopts the Optimal Binarization with Update On-the-fly (OBUF) to reduce t...
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| Veröffentlicht in: | IEEE International Symposium on Circuits and Systems proceedings S. 1 - 5 |
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19.05.2024
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| ISSN: | 2158-1525 |
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| Abstract | Geometry-based point cloud compression (G-PCC) uses Context-based Adaptive Binary Arithmetic Coding to encode the geometry and attribute information. The context information is built in context models for entropy coding. G-PCC adopts the Optimal Binarization with Update On-the-fly (OBUF) to reduce the number of context models. In the current design, however, the probability initialization for both fine-and coarse-grained contexts does not follow the principle of entropy continuation. Moreover, the mapping process to produce coarse-grained contexts is a combination of several fine-grained contexts, leading to an unstable update of probability for coarse-grained contexts, which affects the accuracy of the fine-grained context model in probability estimation.To address the underlying problems, we propose two approaches to improve OBUF: initializing the probabilities for fine-grained and coarse-grained contexts according to entropy continuation and setting the probability update upper and lower bounds for coarse-grained contexts adaptively. The experimental results demonstrate that the proposed technique is more consistent with the underlying principles of OBUF and significantly improves the performance of both octree-based and Trisoup-based geometry coding. Due to the theoretical consistency and outstanding performance, the proposed methods have been adopted into the state-of-the-art G-PCC. |
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| AbstractList | Geometry-based point cloud compression (G-PCC) uses Context-based Adaptive Binary Arithmetic Coding to encode the geometry and attribute information. The context information is built in context models for entropy coding. G-PCC adopts the Optimal Binarization with Update On-the-fly (OBUF) to reduce the number of context models. In the current design, however, the probability initialization for both fine-and coarse-grained contexts does not follow the principle of entropy continuation. Moreover, the mapping process to produce coarse-grained contexts is a combination of several fine-grained contexts, leading to an unstable update of probability for coarse-grained contexts, which affects the accuracy of the fine-grained context model in probability estimation.To address the underlying problems, we propose two approaches to improve OBUF: initializing the probabilities for fine-grained and coarse-grained contexts according to entropy continuation and setting the probability update upper and lower bounds for coarse-grained contexts adaptively. The experimental results demonstrate that the proposed technique is more consistent with the underlying principles of OBUF and significantly improves the performance of both octree-based and Trisoup-based geometry coding. Due to the theoretical consistency and outstanding performance, the proposed methods have been adopted into the state-of-the-art G-PCC. |
| Author | Tian, Tengya Wan, Shuai Zhang, Wei Yang, Fuzheng Hao, Shidi |
| Author_xml | – sequence: 1 givenname: Shidi surname: Hao fullname: Hao, Shidi email: haoshidi@mail.nwpu.edu.cn organization: Northwestern Polytechnical University,School of Electronics and Information,Xi'an,China – sequence: 2 givenname: Shuai surname: Wan fullname: Wan, Shuai email: swan@nwpu.edu.cn organization: Northwestern Polytechnical University,School of Electronics and Information,Xi'an,China – sequence: 3 givenname: Tengya surname: Tian fullname: Tian, Tengya email: tiantengya@stu.xidian.edu.cn organization: Xidian University,School of Telecommunication Engineering,Xi'an,China – sequence: 4 givenname: Wei surname: Zhang fullname: Zhang, Wei email: wzhang@xidian.edu.cn organization: Xidian University,School of Telecommunication Engineering,Xi'an,China – sequence: 5 givenname: Fuzheng surname: Yang fullname: Yang, Fuzheng email: fzhyang@mail.xidian.edu.cn organization: Xidian University,School of Telecommunication Engineering,Xi'an,China |
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| Snippet | Geometry-based point cloud compression (G-PCC) uses Context-based Adaptive Binary Arithmetic Coding to encode the geometry and attribute information. The... |
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| SubjectTerms | Adaptation models CABAC Circuits and systems Entropy G-PCC Geometry OBUF Performance gain Point cloud compression Probabilistic logic |
| Title | Improving Optimal Binarization with Update On-the-fly in G-PCC Entropy Coding: Probability Initialization and Adaptive Bounds Setting for Context Models |
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