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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE International Symposium on Circuits and Systems proceedings pp. 1 - 5
Main Authors: Hao, Shidi, Wan, Shuai, Tian, Tengya, Zhang, Wei, Yang, Fuzheng
Format: Conference Proceeding
Language:English
Published: IEEE 19.05.2024
Subjects:
Adaptation models
CABAC
Circuits and systems
Entropy
G-PCC
Geometry
OBUF
Performance gain
Point cloud compression
Probabilistic logic
ISSN:2158-1525
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
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.
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
BookMark eNo1kM1OwkAURkejiYC8gYv7AsX56bQdd9AgNsFAgqzJlN7KmDLTtCNan8THtUZZfcm3OCc5Q3JlnUVCgNEJY1TdZ5t0upFJHIYTTnk4YVTKhDN1QcYqVomQVAiqFLskA85kEjDJ5Q0Ztu0bpZzSiA_Id3asG3cy9hVWtTdHXcHMWN2YL-2Ns_Bh_AG2daE9wsoG_oBBWXVgLCyCdZrC3PrG1R2krugZD7BuXK5zUxnfQWaNN7o6o7QtYFro3nJCmLl3W7SwQe9_3aVreoT1-Onh2RVYtbfkutRVi-P_HZHt4_wlfQqWq0WWTpeB4TT0QYlMKq4Voor2GHMeJUjLknKkWvK4QFaIvVD7SGidJ_1VMpbniu9FmMcqomJE7v64BhF3ddMnaLrduaT4AQB5bTE
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/ISCAS58744.2024.10558219
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Xplore Digital Libary (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore Digital Libary (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISBN 9798350330991
EISSN 2158-1525
EndPage 5
ExternalDocumentID 10558219
Genre orig-research
GroupedDBID -~X
6IE
6IF
6IH
6IK
6IL
6IM
6IN
AAJGR
AAWTH
ABLEC
ACGFS
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IJVOP
IPLJI
M43
OCL
RIE
RIL
RIO
ID FETCH-LOGICAL-i204t-fe1592a9ee96ce72268e0ff02e0a527de1d3c39c63aab8a52f11bb92c34b79603
IEDL.DBID RIE
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001268541102009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:04:49 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i204t-fe1592a9ee96ce72268e0ff02e0a527de1d3c39c63aab8a52f11bb92c34b79603
PageCount 5
ParticipantIDs ieee_primary_10558219
PublicationCentury 2000
PublicationDate 2024-May-19
PublicationDateYYYYMMDD 2024-05-19
PublicationDate_xml – month: 05
  year: 2024
  text: 2024-May-19
  day: 19
PublicationDecade 2020
PublicationTitle IEEE International Symposium on Circuits and Systems proceedings
PublicationTitleAbbrev ISCAS
PublicationYear 2024
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0020062
Score 2.2562141
Snippet Geometry-based point cloud compression (G-PCC) uses Context-based Adaptive Binary Arithmetic Coding to encode the geometry and attribute information. The...
SourceID ieee
SourceType Publisher
StartPage 1
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
URI https://ieeexplore.ieee.org/document/10558219
WOSCitedRecordID wos001268541102009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwELWg4gAXtiJ2zYFr2sRJnJhbG7VQCbWVSlFvyHYmUqWSVl0Q_RM-F0-6AAcO3CIryUhO7Fk87z3G7oyWoZ9q30mJ_tF6PLvmhLHrSgUmNpGXZQWR9stT1G7Hg4HsrsHqBRYGEYvmM6zQZXGWn47NgkplVRJzjDmRfO5GkViBtbbZFaEBN606rqy2ekmtFxK5u00CeVDZPPtLRaVwIs3Df5o_YuVvOB50t47mmO1gfsIOfjAJnrLPbXEAOnYTeFMjqBPSdo2yBCq3Qn9C6T10csdGfU42WsIwhwenmyTQoIb1yRKSMZm4J3N6xeC9hBb1F6nR5lUqT6GWqgntk1AnVaYZ9LBonwYbAUPBd_UxB1JZG83KrN9sPCePzlp0wRlyN5g7GdoAhyuJKIXByEZnMbpZ5nJ0VcijFL3UN740wldKx3Yo8zytJTd-oCObDvlnrJSPczxnIKQWaGTkqcAPBNcShRvb2z0Uaehm_IKVaZJfJytejdfN_F7-MX7F9ulT0tm9J69ZaT5d4A3bM-_z4Wx6W_wNX5tAuAY
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bT8IwFG4Mmqgv3jDePQ--DrfuWt9gASEikADGN9J2ZwkJDiJo5J_4c-0ZF_XBB9-WZluXdu259Hzfx9iNVsJ3E-VaCdE_Gotn1lygzbqSno506KRpTqT91Axbrej5WXSWYPUcC4OIefEZlugyP8tPxvqNUmW3JOYYcSL53PQ9j9sLuNY6viI84KpYxxa3jW5c7vpE727CQO6VVk__0lHJzUht758fsM-K34A86KxNzQHbwOyQ7f7gEjxin-v0ALTNNvAiR1AhrO0SZwmUcIX-hAJ8aGeW8fusdDSHYQb3VieOoUol65M5xGPq4o66UwsO7zk0qMJIjlavklkC5UROaKeECukyTaGLeQE1GB8YcsarjxmQztpoWmT9WrUX162l7II15LY3s1I0Lg6XAlEEGkPjn0Vop6nN0ZY-DxN0Ele7QgeulCoyTanjKCW4dj0VmoDIPWaFbJzhCYNAqAC1CB3puV7AlcDAjsztDgaJb6f8lBVpkAeTBbPGYDW-Z3-0X7Pteu-xOWg2Wg_nbIemlU7yHXHBCrPXN7xkW_p9Npy-XuV_xhdVMrtN
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=IEEE+International+Symposium+on+Circuits+and+Systems+proceedings&rft.atitle=Improving+Optimal+Binarization+with+Update+On-the-fly+in+G-PCC+Entropy+Coding%3A+Probability+Initialization+and+Adaptive+Bounds+Setting+for+Context+Models&rft.au=Hao%2C+Shidi&rft.au=Wan%2C+Shuai&rft.au=Tian%2C+Tengya&rft.au=Zhang%2C+Wei&rft.date=2024-05-19&rft.pub=IEEE&rft.eissn=2158-1525&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FISCAS58744.2024.10558219&rft.externalDocID=10558219