Occupancy map-based low complexity motion prediction for video-based point cloud compression
This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). We propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we prop...
Gespeichert in:
| Veröffentlicht in: | Journal of visual communication and image representation Jg. 100; S. 104110 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.04.2024
|
| Schlagworte: | |
| ISSN: | 1047-3203, 1095-9076 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). We propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion region in common blocks.
[Display omitted]
•The attribute maps are divided into static, complex, and common blocks.•An early termination algorithm is proposed for static blocks.•An adaptive motion search range method is proposed for complex blocks.•An early inter prediction mode decision algorithm is proposed for common block.
This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). Firstly, we propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion regions in common blocks. Experiment results show that, in comparison to the test model category2 (TMC2) v15.0, called the anchor method, the average bitrate savings of Y, U, and V components of the proposed method achieve 24.27%, 32.64%, and 31.23% on 8i voxelized full bodies version 2 (8iVFBv2) dataset, respectively. Further, the time savings is 41.97% for attribute maps. Similarly, the proposed method also achieves consistent performance on Microsoft voxelized upper bodies (MVUB) dataset. |
|---|---|
| AbstractList | This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). We propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion region in common blocks.
[Display omitted]
•The attribute maps are divided into static, complex, and common blocks.•An early termination algorithm is proposed for static blocks.•An adaptive motion search range method is proposed for complex blocks.•An early inter prediction mode decision algorithm is proposed for common block.
This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). Firstly, we propose to utilize the occupancy map, direction gradient, and regional dispersion to divide the attribute maps into static, complex, and common blocks. Then, we propose an early termination method for static blocks, an adaptive motion search range method for complex blocks, and an early inter prediction mode decision algorithm for affine motion regions in common blocks. Experiment results show that, in comparison to the test model category2 (TMC2) v15.0, called the anchor method, the average bitrate savings of Y, U, and V components of the proposed method achieve 24.27%, 32.64%, and 31.23% on 8i voxelized full bodies version 2 (8iVFBv2) dataset, respectively. Further, the time savings is 41.97% for attribute maps. Similarly, the proposed method also achieves consistent performance on Microsoft voxelized upper bodies (MVUB) dataset. |
| ArticleNumber | 104110 |
| Author | Wang, Yihan Fang, Zhijun Wang, Yongfang Cui, Tengyao |
| Author_xml | – sequence: 1 givenname: Yihan orcidid: 0000-0002-3381-3142 surname: Wang fullname: Wang, Yihan email: wangyihan@shu.edu.cn organization: Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, China – sequence: 2 givenname: Yongfang surname: Wang fullname: Wang, Yongfang email: yfw@shu.edu.cn organization: Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, China – sequence: 3 givenname: Tengyao orcidid: 0000-0002-8075-4181 surname: Cui fullname: Cui, Tengyao email: tyaocui@shu.edu.cn organization: Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, China – sequence: 4 givenname: Zhijun surname: Fang fullname: Fang, Zhijun email: zjfang@dhu.edu.cn organization: School of Computer Science and Technology, Donghua University, China |
| BookMark | eNqFkM9OwzAMhyM0JLbBE3DpC3TETdcuBw5o4p-EtAvckKLUcaRUXVMl3WBvT7vtxAFOtmx_ln7fjE1a3xJjt8AXwKG4qxf1Hl1YZDzLh0kOwC_YFLhcppKXxWTs8zIVGRdXbBZjzTkXUuRT9rlB3HW6xUOy1V1a6UgmafxXgn7bNfTt-mHhe-fbpAtkHB5b60Oyd4b8Gei8a_sEG78zRzBQjMPdNbu0uol0c65z9vH0-L5-Sd82z6_rh7cUBRd9anWRgyg0IelCkFmRXcIKSJS6qmxZVksETjIzUoK2Bgq-yq0FENIYKAWIOROnvxh8jIGs6oLb6nBQwNUoSNXqKEiNgtRJ0EDJXxS6Xo_5-qBd8w97f2JpiLV3FFRERy0OhgJhr4x3f_I_4siHDQ |
| CitedBy_id | crossref_primary_10_1111_cgf_15278 crossref_primary_10_1016_j_jvcir_2025_104481 |
| Cites_doi | 10.3390/sym12071143 10.1109/JETCAS.2018.2885981 10.1017/ATSIP.2020.12 10.1109/ACCESS.2021.3077116 10.1109/TIP.2016.2529506 10.1109/TIP.2017.2707807 10.1109/ACCESS.2020.2991478 10.1109/TCSVT.2017.2699919 10.1145/3382506 10.1109/TCSVT.2016.2543039 10.1109/OJSP.2022.3160392 10.1016/j.cag.2018.09.012 10.1109/TIP.2019.2931621 10.1016/j.jvcir.2022.103683 10.1145/3607546.3616803 10.1145/3159170 10.1109/TCSVT.2021.3063501 |
| ContentType | Journal Article |
| Copyright | 2024 |
| Copyright_xml | – notice: 2024 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.jvcir.2024.104110 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Journalism & Communications Engineering |
| EISSN | 1095-9076 |
| ExternalDocumentID | 10_1016_j_jvcir_2024_104110 S1047320324000658 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 29L 4.4 457 4G. 53G 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABJNI ABMAC ABXDB ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADFGL ADJOM ADMHC ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CAG COF CS3 DM4 DU5 EBS EFBJH EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG5 LX9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SEW SPC SPCBC SST SSV SSZ T5K WH7 WUQ XPP YQT ZMT ZU3 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c303t-fa64136aecea63ed8ef5181e37abbf77b5c10e92d991afd16084ff1139dd17313 |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001202545900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1047-3203 |
| IngestDate | Sat Nov 29 04:56:38 EST 2025 Tue Nov 18 21:54:27 EST 2025 Tue Jun 18 08:51:45 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Low complexity Motion prediction Dynamic point cloud compression Occupancy map |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c303t-fa64136aecea63ed8ef5181e37abbf77b5c10e92d991afd16084ff1139dd17313 |
| ORCID | 0000-0002-3381-3142 0000-0002-8075-4181 |
| ParticipantIDs | crossref_primary_10_1016_j_jvcir_2024_104110 crossref_citationtrail_10_1016_j_jvcir_2024_104110 elsevier_sciencedirect_doi_10_1016_j_jvcir_2024_104110 |
| PublicationCentury | 2000 |
| PublicationDate | April 2024 2024-04-00 |
| PublicationDateYYYYMMDD | 2024-04-01 |
| PublicationDate_xml | – month: 04 year: 2024 text: April 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of visual communication and image representation |
| PublicationYear | 2024 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Li, Li, Liu, Li, Yang, Lin, Chen, Wu (b0145) 2018; 28 Lee, Chan, Siu (b0130) 2017 Mekuria, Blom, Cesar (b0020) 2016; 27 A.J. Hussain, L. Knight, D. Al-Jumeily, P. Fergus, H. Hamdan, Block matching algorithms for motion estimation-A comparison study. Zhou, Ma, Liao, Shi, Yao, Chang, Kuijper (b0160) 2018; 77 Association for Computing Machinery, New York, NY, USA, 2023, 21-28. A.L. Souto, R.L. de Queiroz, C. Dorea, A 3D motion vector database for dynamic point clouds. (2020). arXiv:2008.08438 10.48550/arXiv.2008.08438. He, Zhu, Xu (b0110) 2017 Pan, Lei, Zhang, Wang (b0125) 2018; 14 Liu, Yao, Tu, Cheng (b0045) 2019 Shi, Li (b0050) 2022; 3 Dorina, Chou, Frossard (b0080) 2016; 25 Springer, Cham, 264(2), 356-369 10.1007/978-3-319-04960-1_32. Li, Li, Zakharchenko, Chen, Li (b0090) 2019; 29 Kammerl, Blodow, Rusu, Gedikli, Beetz, Steinbach (b0005) 2012 Anis, Chou, Ortega (b0015) 2016 PCC Test Model Category 2 v0. In ISO/IEC JTC1/SC29 WG11 Doc N17248, Oct. 2017. Thanou, Chou, Frossard (b0010) 2016; 25 Thanou, Chou, Frossard (b0075) 2015 de Queiroz, Chou (b0070) 2017; 26 Schwarz, Hannuksela, Fakour-Sevom, Sheikhi-Pour (b0025) 2018 Shang, Li, Zhao, Zuo (b0150) 2023; 90 S. Schwarz, G. Martin-Cocher, D. Flynn, and M. Budagavi, Common test conditions for point cloud compression. Document ISO/IEC JTC1/SC29/WG11 w17766, Ljubljana, Slovenia, (2018). Li, Li, Zakharchenko, Chen (b0085) 2019 Ren, He, Cui (b0165) 2020; 12 C. Loop, Q. Cai, S.O. Escolano, P.A. Chou, Microsoft Voxelized Upper Bodies—A Voxelized Point Cloud Dataset, document ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG), (2016) 2016. Mercat, Mäkinen, Sainio, Lemmetti, Viitanen, Vanne (b0140) 2021; 9 E. d’Eon, B. Harrison, T. Myers, P.A. Chou, 8i Voxelized Full Bodies – A voxelized point cloud dataset. Document ISO/IEC JTC1/SC29/WG11 m40059, Geneva, Switzerland, 7 (8) (2017) 11. Costa, Dricot, Brites, Ascenso, Pereira (b0055) 2019 Chien, Liao, Yang (b0135) 2014 Bjontegaard (b0185) 2001 Hong, Pavez, Ortega, Watanabe, Nonaka (b0195) 2022 Kim, Im, Rhyu, Kim (b0095) 2020; 8 Zupancic, Bross, Hinz, Marpe (b0155) 2021 A. Singla, S. Wang, S. Göring, R. R. R. Rao, I. Viola, P. Cesar, A. Raake, Subjective quality evaluation of point clouds using remote testing. in: Proceedings of Point Cloud Compression Category 2 Reference Software, TMC2-15.0. [Online]. Available: https://github.com/MPEGGroup/mpeg-pcc-tmc2.git. Xiong, Gao, Wang, Li (b0115) 2022; 32 S. Schwarz, et al. 2017. Nokia’s response to cfp for point cloud compression (category 2). Document ISO/IEC JTC1/SC29/WG11 m41779, Macau, China (2017). He, Yang, Liu, Ding (b0190) 2020; 16 Huang, An, Zhang (b0210) 2017; 2017 Cui, Zhang, Gu, Zhang, Ma (b0120) 2020 D. Graziosi, O. Nakagami, S. Kuma, A. Zaghetto, T. Suzuki, A. Tabatabai, An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC). Schwarz (b0040) 2018; 9 9, e13. 10.1017/ATSIP.2019.20. Hoppe, DeRose, Duchamp, McDonald, Stuetzle (b0035) 1922 10.1016/j.jvcir.2024.104110_b0105 Anis (10.1016/j.jvcir.2024.104110_b0015) 2016 Costa (10.1016/j.jvcir.2024.104110_b0055) 2019 10.1016/j.jvcir.2024.104110_b0205 Schwarz (10.1016/j.jvcir.2024.104110_b0040) 2018; 9 Kim (10.1016/j.jvcir.2024.104110_b0095) 2020; 8 10.1016/j.jvcir.2024.104110_b0200 Kammerl (10.1016/j.jvcir.2024.104110_b0005) 2012 Dorina (10.1016/j.jvcir.2024.104110_b0080) 2016; 25 Huang (10.1016/j.jvcir.2024.104110_b0210) 2017; 2017 Liu (10.1016/j.jvcir.2024.104110_b0045) 2019 Thanou (10.1016/j.jvcir.2024.104110_b0075) 2015 He (10.1016/j.jvcir.2024.104110_b0190) 2020; 16 Li (10.1016/j.jvcir.2024.104110_b0085) 2019 Thanou (10.1016/j.jvcir.2024.104110_b0010) 2016; 25 Zhou (10.1016/j.jvcir.2024.104110_b0160) 2018; 77 10.1016/j.jvcir.2024.104110_b0175 de Queiroz (10.1016/j.jvcir.2024.104110_b0070) 2017; 26 Li (10.1016/j.jvcir.2024.104110_b0145) 2018; 28 10.1016/j.jvcir.2024.104110_b0170 Lee (10.1016/j.jvcir.2024.104110_b0130) 2017 Mekuria (10.1016/j.jvcir.2024.104110_b0020) 2016; 27 10.1016/j.jvcir.2024.104110_b0030 Shang (10.1016/j.jvcir.2024.104110_b0150) 2023; 90 Hoppe (10.1016/j.jvcir.2024.104110_b0035) 1922 Mercat (10.1016/j.jvcir.2024.104110_b0140) 2021; 9 Ren (10.1016/j.jvcir.2024.104110_b0165) 2020; 12 Cui (10.1016/j.jvcir.2024.104110_b0120) 2020 Pan (10.1016/j.jvcir.2024.104110_b0125) 2018; 14 Chien (10.1016/j.jvcir.2024.104110_b0135) 2014 Hong (10.1016/j.jvcir.2024.104110_b0195) 2022 Xiong (10.1016/j.jvcir.2024.104110_b0115) 2022; 32 Li (10.1016/j.jvcir.2024.104110_b0090) 2019; 29 Shi (10.1016/j.jvcir.2024.104110_b0050) 2022; 3 10.1016/j.jvcir.2024.104110_b0180 Zupancic (10.1016/j.jvcir.2024.104110_b0155) 2021 Bjontegaard (10.1016/j.jvcir.2024.104110_b0185) 2001 He (10.1016/j.jvcir.2024.104110_b0110) 2017 10.1016/j.jvcir.2024.104110_b0065 10.1016/j.jvcir.2024.104110_b0100 Schwarz (10.1016/j.jvcir.2024.104110_b0025) 2018 10.1016/j.jvcir.2024.104110_b0060 |
| References_xml | – start-page: 498 year: 2019 end-page: 507 ident: b0085 publication-title: Advanced 3D motion prediction for video based point cloud attributes compression – reference: 9, e13. 10.1017/ATSIP.2019.20. – start-page: 1 year: 2017 end-page: 6 ident: b0110 publication-title: Best-effort projection based attribute compression for 3D point cloud – volume: 9 start-page: 67813 year: 2021 end-page: 67828 ident: b0140 article-title: Comparative rate-distortion-complexity analysis of VVC and HEVC video codecs. publication-title: Access – start-page: 1 year: 2021 end-page: 5 ident: b0155 publication-title: Encoding complexity analysis and reduction for a practically-oriented VVC encoder implementation – reference: S. Schwarz, G. Martin-Cocher, D. Flynn, and M. Budagavi, Common test conditions for point cloud compression. Document ISO/IEC JTC1/SC29/WG11 w17766, Ljubljana, Slovenia, (2018). – volume: 25 start-page: 1765 year: 2016 end-page: 1778 ident: b0010 article-title: Graph-based compression of dynamic 3D point cloud sequences publication-title: IEEE Trans. Image Process. – reference: A.L. Souto, R.L. de Queiroz, C. Dorea, A 3D motion vector database for dynamic point clouds. (2020). arXiv:2008.08438 10.48550/arXiv.2008.08438. – start-page: 3696 year: 2014 end-page: 3699 ident: b0135 publication-title: Enhanced AMVP mechanism based adaptive motion search range decision algorithm for fast HEVC coding – volume: 27 start-page: 828 year: 2016 end-page: 842 ident: b0020 article-title: Design, implementation, and evaluation of a point cloud codec for tele-immersive video publication-title: IEEE Trans. Circuits Syst. Video Technol. – year: 2001 ident: b0185 article-title: Calculation of average PSNR differences between RD curves, document ITU-T SG16 Q6, VCEG-M33 – volume: 12 start-page: 1143 year: 2020 ident: b0165 article-title: An improved fast affine motion estimation based on edge detection algorithm for VVC publication-title: Symmetry – volume: 8 start-page: 83538 year: 2020 end-page: 83547 ident: b0095 article-title: 3D motion estimation and compensation method for video-based point cloud compression publication-title: IEEE Access – volume: 3 start-page: 155 year: 2022 end-page: 168 ident: b0050 article-title: Patch re-segmentation and packing for dynamic point cloud compression via back-and-forth structure publication-title: IEEE Open J. Signal Process. – reference: Springer, Cham, 264(2), 356-369 10.1007/978-3-319-04960-1_32. – reference: Point Cloud Compression Category 2 Reference Software, TMC2-15.0. [Online]. Available: https://github.com/MPEGGroup/mpeg-pcc-tmc2.git. – reference: C. Loop, Q. Cai, S.O. Escolano, P.A. Chou, Microsoft Voxelized Upper Bodies—A Voxelized Point Cloud Dataset, document ISO/IEC JTC1/SC29 Joint WG11/WG1 (MPEG/JPEG), (2016) 2016. – reference: Association for Computing Machinery, New York, NY, USA, 2023, 21-28. – start-page: 1 year: 2019 end-page: 6 ident: b0055 publication-title: Improved patch packing for the MPEG V-PCC standard – volume: 28 start-page: 1934 year: 2018 end-page: 1948 ident: b0145 article-title: An efficient four-parameter affine motion model for video coding publication-title: IEEE Trans. Circuits Syst. Video Technol. – volume: 90 year: 2023 ident: b0150 article-title: Low complexity inter coding scheme for versatile video coding (VVC) publication-title: J. vis. Commun. Image Represent. – volume: 25 start-page: 1765 year: 2016 end-page: 1778 ident: b0080 article-title: Graph-based compression of dynamic 3D point cloud sequences publication-title: IEEE Trans. Image Process. – volume: 29 start-page: 289 year: 2019 end-page: 302 ident: b0090 article-title: Advanced 3D motion prediction for video-based dynamic point cloud compression publication-title: IEEE Trans. Image Process. – start-page: 904 year: 2019 end-page: 909 ident: b0045 publication-title: Data-adaptive packing method for compression of dynamic point cloud sequences – volume: 16 start-page: 1 year: 2020 end-page: 18 ident: b0190 article-title: Spatio-temporal saliency-based motion vector refinement for frame rate up-conversion publication-title: ACM Trans. Multimedia Comput. Commun. Appl. – start-page: 6360 year: 2016 end-page: 6364 ident: b0015 publication-title: Compression of dynamic 3D point clouds using subdivisional meshes and graph wavelet transforms – start-page: 778 year: 2012 end-page: 785 ident: b0005 publication-title: Real-time compression of point cloud streams – start-page: 61 year: 2018 end-page: 65 ident: b0025 publication-title: 2D video coding of volumetric video data – volume: 77 start-page: 50 year: 2018 end-page: 64 ident: b0160 article-title: Feature fusion information statistics for feature matching in cluttered scenes publication-title: Comput. Graph. – volume: 2017 start-page: 1 year: 2017 end-page: 15 ident: b0210 article-title: Efficient AMP decision and search range adjustment algorithm for HEVC publication-title: J Image Video Process – reference: A.J. Hussain, L. Knight, D. Al-Jumeily, P. Fergus, H. Hamdan, Block matching algorithms for motion estimation-A comparison study. – reference: E. d’Eon, B. Harrison, T. Myers, P.A. Chou, 8i Voxelized Full Bodies – A voxelized point cloud dataset. Document ISO/IEC JTC1/SC29/WG11 m40059, Geneva, Switzerland, 7 (8) (2017) 11. – start-page: 71 year: 1922 end-page: 78 ident: b0035 publication-title: Surface reconstruction from unorganized points – volume: 26 start-page: 3886 year: 2017 end-page: 3895 ident: b0070 article-title: Motion-compensated compression of dynamic voxelized point clouds publication-title: IEEE Trans. Image Process. – start-page: 3235 year: 2015 end-page: 3239 ident: b0075 publication-title: Graph-based motion estimation and compensation for dynamic 3D point cloud compression – volume: 32 start-page: 813 year: 2022 end-page: 825 ident: b0115 article-title: Occupancy map guided fast video-based dynamic point cloud coding publication-title: IEEE Trans. Circuits Syst. Video Technol. – start-page: 369 year: 2022 end-page: 378 ident: b0195 publication-title: Fractional motion estimation for point cloud compression – volume: 9 start-page: 133 year: 2018 end-page: 148 ident: b0040 article-title: Emerging MPEG standards for point cloud compression, publication-title: J. Emerg. Sel. Topics Circuits Syst. – reference: A. Singla, S. Wang, S. Göring, R. R. R. Rao, I. Viola, P. Cesar, A. Raake, Subjective quality evaluation of point clouds using remote testing. in: Proceedings of – reference: D. Graziosi, O. Nakagami, S. Kuma, A. Zaghetto, T. Suzuki, A. Tabatabai, An overview of ongoing point cloud compression standardization activities: Video-based (V-PCC) and geometry-based (G-PCC). – reference: PCC Test Model Category 2 v0. In ISO/IEC JTC1/SC29 WG11 Doc N17248, Oct. 2017. – reference: S. Schwarz, et al. 2017. Nokia’s response to cfp for point cloud compression (category 2). Document ISO/IEC JTC1/SC29/WG11 m41779, Macau, China (2017). – start-page: 1249 year: 2017 end-page: 1254 ident: b0130 publication-title: Adaptive search range by depth variant decaying weights for HEVC inter texture coding – start-page: 103 year: 2020 end-page: 112 ident: b0120 publication-title: Gradient-based early termination of CU partition in VVC intra coding – volume: 14 start-page: 1 year: 2018 end-page: 19 ident: b0125 article-title: Adaptive fractional-pixel motion estimation skipped algorithm for efficient HEVC motion estimation publication-title: ACM Trans. Multimedia Comput. Commun. Appl. – start-page: 61 year: 2018 ident: 10.1016/j.jvcir.2024.104110_b0025 – volume: 12 start-page: 1143 issue: 7 year: 2020 ident: 10.1016/j.jvcir.2024.104110_b0165 article-title: An improved fast affine motion estimation based on edge detection algorithm for VVC publication-title: Symmetry doi: 10.3390/sym12071143 – volume: 9 start-page: 133 issue: 1 year: 2018 ident: 10.1016/j.jvcir.2024.104110_b0040 article-title: Emerging MPEG standards for point cloud compression, IEEE publication-title: J. Emerg. Sel. Topics Circuits Syst. doi: 10.1109/JETCAS.2018.2885981 – start-page: 904 year: 2019 ident: 10.1016/j.jvcir.2024.104110_b0045 – ident: 10.1016/j.jvcir.2024.104110_b0205 doi: 10.1017/ATSIP.2020.12 – ident: 10.1016/j.jvcir.2024.104110_b0175 – year: 2001 ident: 10.1016/j.jvcir.2024.104110_b0185 – volume: 2017 start-page: 1 issue: 2017 year: 2017 ident: 10.1016/j.jvcir.2024.104110_b0210 article-title: Efficient AMP decision and search range adjustment algorithm for HEVC publication-title: J Image Video Process – volume: 9 start-page: 67813 year: 2021 ident: 10.1016/j.jvcir.2024.104110_b0140 article-title: Comparative rate-distortion-complexity analysis of VVC and HEVC video codecs. IEEE publication-title: Access doi: 10.1109/ACCESS.2021.3077116 – start-page: 778 year: 2012 ident: 10.1016/j.jvcir.2024.104110_b0005 – volume: 25 start-page: 1765 issue: 4 year: 2016 ident: 10.1016/j.jvcir.2024.104110_b0010 article-title: Graph-based compression of dynamic 3D point cloud sequences publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2529506 – ident: 10.1016/j.jvcir.2024.104110_b0060 – volume: 26 start-page: 3886 issue: 8 year: 2017 ident: 10.1016/j.jvcir.2024.104110_b0070 article-title: Motion-compensated compression of dynamic voxelized point clouds publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2707807 – start-page: 6360 year: 2016 ident: 10.1016/j.jvcir.2024.104110_b0015 – ident: 10.1016/j.jvcir.2024.104110_b0100 – volume: 8 start-page: 83538 year: 2020 ident: 10.1016/j.jvcir.2024.104110_b0095 article-title: 3D motion estimation and compensation method for video-based point cloud compression publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2991478 – volume: 28 start-page: 1934 issue: 8 year: 2018 ident: 10.1016/j.jvcir.2024.104110_b0145 article-title: An efficient four-parameter affine motion model for video coding publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2017.2699919 – volume: 16 start-page: 1 issue: 2 year: 2020 ident: 10.1016/j.jvcir.2024.104110_b0190 article-title: Spatio-temporal saliency-based motion vector refinement for frame rate up-conversion publication-title: ACM Trans. Multimedia Comput. Commun. Appl. doi: 10.1145/3382506 – volume: 27 start-page: 828 issue: 4 year: 2016 ident: 10.1016/j.jvcir.2024.104110_b0020 article-title: Design, implementation, and evaluation of a point cloud codec for tele-immersive video publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2016.2543039 – start-page: 1 year: 2017 ident: 10.1016/j.jvcir.2024.104110_b0110 – ident: 10.1016/j.jvcir.2024.104110_b0180 – ident: 10.1016/j.jvcir.2024.104110_b0105 – volume: 3 start-page: 155 year: 2022 ident: 10.1016/j.jvcir.2024.104110_b0050 article-title: Patch re-segmentation and packing for dynamic point cloud compression via back-and-forth structure publication-title: IEEE Open J. Signal Process. doi: 10.1109/OJSP.2022.3160392 – start-page: 498 year: 2019 ident: 10.1016/j.jvcir.2024.104110_b0085 – start-page: 3696 year: 2014 ident: 10.1016/j.jvcir.2024.104110_b0135 – volume: 25 start-page: 1765 issue: 4 year: 2016 ident: 10.1016/j.jvcir.2024.104110_b0080 article-title: Graph-based compression of dynamic 3D point cloud sequences publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2016.2529506 – start-page: 1 year: 2021 ident: 10.1016/j.jvcir.2024.104110_b0155 – start-page: 71 year: 1922 ident: 10.1016/j.jvcir.2024.104110_b0035 – ident: 10.1016/j.jvcir.2024.104110_b0170 – start-page: 3235 year: 2015 ident: 10.1016/j.jvcir.2024.104110_b0075 – start-page: 1249 year: 2017 ident: 10.1016/j.jvcir.2024.104110_b0130 – volume: 77 start-page: 50 year: 2018 ident: 10.1016/j.jvcir.2024.104110_b0160 article-title: Feature fusion information statistics for feature matching in cluttered scenes publication-title: Comput. Graph. doi: 10.1016/j.cag.2018.09.012 – volume: 29 start-page: 289 year: 2019 ident: 10.1016/j.jvcir.2024.104110_b0090 article-title: Advanced 3D motion prediction for video-based dynamic point cloud compression publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2931621 – start-page: 103 year: 2020 ident: 10.1016/j.jvcir.2024.104110_b0120 – volume: 90 year: 2023 ident: 10.1016/j.jvcir.2024.104110_b0150 article-title: Low complexity inter coding scheme for versatile video coding (VVC) publication-title: J. vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2022.103683 – ident: 10.1016/j.jvcir.2024.104110_b0065 – ident: 10.1016/j.jvcir.2024.104110_b0030 – ident: 10.1016/j.jvcir.2024.104110_b0200 doi: 10.1145/3607546.3616803 – start-page: 1 year: 2019 ident: 10.1016/j.jvcir.2024.104110_b0055 – start-page: 369 year: 2022 ident: 10.1016/j.jvcir.2024.104110_b0195 – volume: 14 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.jvcir.2024.104110_b0125 article-title: Adaptive fractional-pixel motion estimation skipped algorithm for efficient HEVC motion estimation publication-title: ACM Trans. Multimedia Comput. Commun. Appl. doi: 10.1145/3159170 – volume: 32 start-page: 813 issue: 2 year: 2022 ident: 10.1016/j.jvcir.2024.104110_b0115 article-title: Occupancy map guided fast video-based dynamic point cloud coding publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2021.3063501 |
| SSID | ssj0003934 |
| Score | 2.4015558 |
| Snippet | This paper proposes an occupancy map-based low complexity motion prediction method for video-based point cloud compression (V-PCC). We propose to utilize the... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 104110 |
| SubjectTerms | Dynamic point cloud compression Low complexity Motion prediction Occupancy map |
| Title | Occupancy map-based low complexity motion prediction for video-based point cloud compression |
| URI | https://dx.doi.org/10.1016/j.jvcir.2024.104110 |
| Volume | 100 |
| WOSCitedRecordID | wos001202545900001&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 | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1095-9076 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003934 issn: 1047-3203 databaseCode: AIEXJ dateStart: 19950301 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9MwGLbKxgEO0xggxhjyAXEZmfLt5DihTYPDxKGIgpAixx9bqtap1g-6X8Lf5fVH0pRCxQ5cosqynajvE_ux87yPEXoDc2pCY156jDHhxUDZPUpgsUIzkVEGHCKhpTlsglxdZYNB_qnX-9nkwixGRKlsucwn_zXUUAbB1qmz9wh32ykUwG8IOlwh7HD9p8Bb32CdUDmmE0_PUvxkVP-w4nGx1KzbHt2j_QF4xVqxoc7Iq12DSV2p2Qkb1XOT9ebUsuovVHZRTefGZ6STbGK_Soy1JMgYZzZJTi0Yv7id6q_VDd0srNW1pG5WNQpuozroC3V9R-sWdK72t5tqOFfdDYywq3uxY642i4hCP1oblH2_M6xClcCqXzdGfLv5MDwdLlil_V3D-HRVe91f-7d5r1UjNkK3YWE6KXQnhe3kAdoNSZLDiL979uF88LGd5KPcChaaZ28MrYx0cONZ_kx6OkSmv4_2XNjwmUXOE9QT6gA97vhSHqAjV6majvFbvJZDNH2KvrcIwy3CMCAMrxCGLcLwCmEYEIY7CMMGYdggDHcQ9gx9vjjvv7_03CEdHgP2M_MkTYEHpVQwQdNI8EzIBFijiAgtS0lImbDAF3nIYSFCJQ9SP4ulDGDhwXlAoiB6jnZUrcQLhBOWCwmEUZZcxoSnFJbLETCoKMl4zKk8RGHzLxbMOdjrg1RGxZYIHqJ3baOJNXDZXj1twlM4Dmq5ZQGA29bw5f3uc4Qerd6FV2hndjsXx-ghW8yq6e1rh7Zfbl2vFw |
| linkProvider | Elsevier |
| 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%3Ajournal&rft.genre=article&rft.atitle=Occupancy+map-based+low+complexity+motion+prediction+for+video-based+point+cloud+compression&rft.jtitle=Journal+of+visual+communication+and+image+representation&rft.au=Wang%2C+Yihan&rft.au=Wang%2C+Yongfang&rft.au=Cui%2C+Tengyao&rft.au=Fang%2C+Zhijun&rft.date=2024-04-01&rft.issn=1047-3203&rft.volume=100&rft.spage=104110&rft_id=info:doi/10.1016%2Fj.jvcir.2024.104110&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jvcir_2024_104110 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1047-3203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1047-3203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1047-3203&client=summon |