The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine
Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this...
Uloženo v:
| Vydáno v: | IEEE access Ročník 13; s. 43289 - 43315 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2169-3536, 2169-3536 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing. |
|---|---|
| AbstractList | Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing. |
| Author | Pereira, Fernando Rodrigues, Nuno M. M. Guarda, Andre F. R. |
| Author_xml | – sequence: 1 givenname: Andre F. R. orcidid: 0000-0001-5996-1074 surname: Guarda fullname: Guarda, Andre F. R. email: andre.guarda@lx.it.pt organization: Instituto de Telecomunicações, Lisbon, Portugal – sequence: 2 givenname: Nuno M. M. orcidid: 0000-0001-9536-1017 surname: Rodrigues fullname: Rodrigues, Nuno M. M. organization: Instituto de Telecomunicações, Lisbon, Portugal – sequence: 3 givenname: Fernando orcidid: 0000-0001-6100-947X surname: Pereira fullname: Pereira, Fernando organization: Instituto de Telecomunicações, Lisbon, Portugal |
| BookMark | eNpNUV1rGzEQFCWBukl-Qfsg6PM5-jhJp765h5ukOMTEybPQSSvnjCu5unOg_75yL5TsyyyzO7ML8wmdxRQBoc-UzCkl-nrRtsvNZs4IE3Muas2p_IBmjEpdccHl2bv-I7oahh0p1RRKqBl6fHoB_HO9vMHrPcSEV2Bz7OO2-m4H8Hid-jjidp-OHrfJlwHejDZ6m_03vIH8emLubcSFK-he-giX6DzY_QBXb3iBnn8sn9rbavVwc9cuVpXjjR6rQGolnW4s0YJa7XXXeBZ4J8ErIjvSBGm9pKoDX_ugnHWi5p6yRokgWQP8At1Nvj7ZnTnk_pfNf0yyvflHpLw1No-924NRoqkF15wpIerOKU1rx7rgfU1lF4IqXl8nr0NOv48wjGaXjjmW9w2nSjFKy89li09bLqdhyBD-X6XEnLIwUxbmlIV5y6KovkyqHgDeKTSVkin-F52ohKQ |
| CODEN | IAECCG |
| Cites_doi | 10.1109/ICIP40778.2020.9191180 10.1109/ICIP.2019.8803413 10.1109/TCSVT.2023.3240391 10.1109/icip.2017.8296925 10.1145/3552457.3555727 10.1109/TPAMI.2022.3225816 10.1186/s13640-024-00631-6 10.1109/MMUL.2023.3245919 10.1109/MMSP48831.2020.9287077 10.1109/TMM.2023.3338081 10.1109/access.2023.3332599 10.1109/ISCAS46773.2023.10182229 10.1109/TMM.2021.3068580 10.1109/MIPR54900.2022.00018 10.1109/PCS.2015.7170048 10.1109/jproc.2021.3085957 10.1109/VCIP56404.2022.10008892 10.1109/TCSVT.2021.3051377 10.1109/vcip53242.2021.9675369 10.1007/s00530-022-01026-1 10.1109/icip42928.2021.9506712 10.1109/VCIP49819.2020.9301804 10.1117/12.2597814 10.1109/EUVIP53989.2022.9922784 10.1186/s13640-024-00629-0 10.1109/jstsp.2017.2747126 10.1109/TPAMI.2024.3367293 10.5555/3298023.3298188 10.1109/mmul.2016.64 10.1109/qomex48832.2020.9123147 10.1109/3DV62453.2024.00112 10.1109/ICASSP48485.2024.10448359 10.1007/978-3-319-46723-8_49 10.1109/DCC50243.2021.00015 10.1109/ICCV.2017.324 10.1109/MMSP59012.2023.10337709 10.1109/CVPR.2019.00319 10.1142/IMS 10.1109/ICPR56361.2022.9956532 10.1109/TCSVT.2021.3101953 10.1109/ICASSP43922.2022.9747496 10.1017/atsip.2020.12 10.1117/12.2569115 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2025 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2025.3549316 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access (Activated by CARLI) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 43315 |
| ExternalDocumentID | oai_doaj_org_article_7584539327554bc7914c2bfdd416bff7 10_1109_ACCESS_2025_3549316 10916627 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Fundação para a Ciência e Tecnologia, I.P. (FCT, Funder ID = 50110000187), under the project with reference UIDB/50008/2020 [Digital Object Identifier (DOI): 10.54499/UIDB/50008/2020] and the project with reference PTDC/EEI-COM/1125/2021 entitled “Deep Learning-based Point Cloud Representation.” |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c389t-f0476c98a0951a9d9b8d2f3b6ed706b08f6ad617bed4df7cac543d12875f628e3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001445065100004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2169-3536 |
| IngestDate | Fri Oct 03 12:50:30 EDT 2025 Mon Jun 30 12:10:07 EDT 2025 Sat Nov 29 08:09:51 EST 2025 Wed Aug 27 01:42:30 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c389t-f0476c98a0951a9d9b8d2f3b6ed706b08f6ad617bed4df7cac543d12875f628e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-5996-1074 0000-0001-6100-947X 0000-0001-9536-1017 |
| OpenAccessLink | https://doaj.org/article/7584539327554bc7914c2bfdd416bff7 |
| PQID | 3177211389 |
| PQPubID | 4845423 |
| PageCount | 27 |
| ParticipantIDs | proquest_journals_3177211389 crossref_primary_10_1109_ACCESS_2025_3549316 ieee_primary_10916627 doaj_primary_oai_doaj_org_article_7584539327554bc7914c2bfdd416bff7 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-01-01 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2025 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 Kingma (ref51) ref11 ref55 ref17 ref16 ref19 ref18 ref50 (ref5) 2020 ref45 ref47 ref42 (ref24) 2022 ref41 ref44 ref43 ref49 ref7 ref6 ref40 (ref53) 2022 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 (ref48) 2023 ref2 ref1 (ref21) 2022 ref39 ref38 Ballé (ref9) Astola (ref3) 2020; 3 Toderici (ref8) (ref4) 2017 (ref22) 2022 (ref52) 2023 ref23 ref26 ref25 (ref54) 2023 ref28 ref27 ref29 Minnen (ref10) (ref60) 2019 (ref20) 2020 (ref46) 2024 |
| References_xml | – ident: ref38 doi: 10.1109/ICIP40778.2020.9191180 – ident: ref28 doi: 10.1109/ICIP.2019.8803413 – volume-title: Final Call for Proposals on JPEG Pleno Point Cloud Coding year: 2022 ident: ref24 – ident: ref15 doi: 10.1109/TCSVT.2023.3240391 – volume-title: JPEG AI Common Training and Test Conditions year: 2023 ident: ref52 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. ident: ref8 article-title: Variable rate image compression with recurrent neural networks – volume: 3 issue: 1 year: 2020 ident: ref3 article-title: JPEG Pleno: Standardizing a coding framework and tools for plenoptic imaging modalities publication-title: ITU J., ICT Discoveries – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent ident: ref9 article-title: Variational image compression with a scale hyperprior – volume-title: JPEG AI Verification Model V1.0 Description year: 2022 ident: ref53 – volume-title: [AI-3DGC/G-PCC][New] Objective Point Cloud Classification According to Density Distribution year: 2023 ident: ref54 – ident: ref55 doi: 10.1109/icip.2017.8296925 – ident: ref27 doi: 10.1145/3552457.3555727 – ident: ref32 doi: 10.1109/TPAMI.2022.3225816 – ident: ref58 doi: 10.1186/s13640-024-00631-6 – ident: ref19 doi: 10.1109/MMUL.2023.3245919 – ident: ref29 doi: 10.1109/MMSP48831.2020.9287077 – ident: ref40 doi: 10.1109/TMM.2023.3338081 – ident: ref57 doi: 10.1109/access.2023.3332599 – volume-title: JPEG Pleno Call for Proposals on Light Field Coding year: 2017 ident: ref4 – ident: ref36 doi: 10.1109/ISCAS46773.2023.10182229 – ident: ref17 doi: 10.1109/TMM.2021.3068580 – ident: ref39 doi: 10.1109/MIPR54900.2022.00018 – ident: ref42 doi: 10.1109/PCS.2015.7170048 – volume-title: JPEG Pleno Point Cloud Coding Common Training and Test Conditions V2.1 year: 2024 ident: ref46 – volume-title: Performance Evaluation of Learning Based Image Coding Solutions and Quality Metrics year: 2019 ident: ref60 – ident: ref7 doi: 10.1109/jproc.2021.3085957 – volume-title: Call for Evidence on Learning-Based Image Coding Technologies year: 2020 ident: ref20 – ident: ref26 doi: 10.1109/VCIP56404.2022.10008892 – volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref10 article-title: Joint global and local hierarchical priors for learned image compression – ident: ref30 doi: 10.1109/TCSVT.2021.3051377 – volume-title: JPEG AI Overview Slides year: 2023 ident: ref48 – ident: ref12 doi: 10.1109/vcip53242.2021.9675369 – ident: ref14 doi: 10.1007/s00530-022-01026-1 – ident: ref11 doi: 10.1109/icip42928.2021.9506712 – ident: ref45 doi: 10.1109/VCIP49819.2020.9301804 – ident: ref33 doi: 10.1117/12.2597814 – ident: ref41 doi: 10.1109/EUVIP53989.2022.9922784 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Represent. ident: ref51 article-title: Adam: A method for stochastic optimization – ident: ref59 doi: 10.1186/s13640-024-00629-0 – ident: ref2 doi: 10.1109/jstsp.2017.2747126 – ident: ref18 doi: 10.1109/TPAMI.2024.3367293 – ident: ref31 doi: 10.5555/3298023.3298188 – ident: ref1 doi: 10.1109/mmul.2016.64 – ident: ref56 doi: 10.1109/qomex48832.2020.9123147 – ident: ref35 doi: 10.1109/3DV62453.2024.00112 – ident: ref43 doi: 10.1109/ICASSP48485.2024.10448359 – volume-title: Final Call for Proposals for JPEG AI year: 2022 ident: ref21 – ident: ref44 doi: 10.1007/978-3-319-46723-8_49 – ident: ref25 doi: 10.1109/DCC50243.2021.00015 – ident: ref49 doi: 10.1109/ICCV.2017.324 – ident: ref16 doi: 10.1109/MMSP59012.2023.10337709 – ident: ref50 doi: 10.1109/CVPR.2019.00319 – volume-title: Final Call for Evidence on JPEG Pleno Point Cloud Coding year: 2020 ident: ref5 – volume-title: Report on the JPEG AI Call for Proposals Results year: 2022 ident: ref22 – ident: ref47 doi: 10.1142/IMS – ident: ref13 doi: 10.1109/ICPR56361.2022.9956532 – ident: ref23 doi: 10.1109/TCSVT.2021.3101953 – ident: ref34 doi: 10.1109/ICASSP43922.2022.9747496 – ident: ref6 doi: 10.1017/atsip.2020.12 – ident: ref37 doi: 10.1117/12.2569115 |
| SSID | ssj0000816957 |
| Score | 2.347695 |
| Snippet | Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems,... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Index Database Publisher |
| StartPage | 43289 |
| SubjectTerms | Artificial intelligence Artificial neural networks Codecs Coding standards Color coding Compressing Computer vision Deep learning Digital twins Encoding Geometry Image coding Image color analysis Image compression JPEG Pleno standard learning-based coding Machine learning man and machine point cloud coding Point cloud compression Representations Three-dimensional displays Transform coding Virtual reality |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB508aAHnyuuL3LwaLX2ldbbWnwgKosv9haalyxIK_vw9zuTRlkQD17aElKaziSdb6aTbwCO4koTTKA8HTygxyCD3KgKl3uchdxYaV1M9_WOPzzkw2Ex8JvV3V4YY4xLPjMndOn-5etGzShUdkoklkRYvgiLnGftZq2fgApVkChS7pmFsOtpvyzxJdAHjNKTGP2gmIqaz1kfR9Lvq6r8-hQ7-3K19s-RrcOqB5Ks32p-AxZMvQkrc_SCW_CIc4DdDi6vGWW8N8xzqb4FF2i6NBs0o3rKyvdmplnZkA1jTz6wcM7cNwRb7quaYRueKenSdOHl6vK5vAl8DYVAIRSZBjZMeKaKvCIoVRW6kLmObCwzo3mYyTC3WaURxUijE225qlSaxBqNFk9tFuUm3oZO3dRmB5hWhO7MmbUIwqRSRRWlKlNK2lTJyOQ9OP6WrfhoqTKEczHCQrSqEKQK4VXRgwuS_09X4rl2DShY4ZeNQG8mSWPEmBxhj1S8OEtUJK3WiCOltbwHXVLG3PNaPfRg_1udwi_KiUCoRP4uymX3j9v2YJmG2IZY9qEzHc_MASypz-loMj508-0LExPSUQ priority: 102 providerName: IEEE |
| Title | The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine |
| URI | https://ieeexplore.ieee.org/document/10916627 https://www.proquest.com/docview/3177211389 https://doaj.org/article/7584539327554bc7914c2bfdd416bff7 |
| Volume | 13 |
| WOSCitedRecordID | wos001445065100004&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEA4iHvQgPrFaSw4eXbvdR7Lx1i5VEVuKL3oLm5cUZFf68Ohvd5JNZcGDFy9ZGBay-WaT-WaYzCB0ERfK0gSbpwMDeAwiyLQsYLvHJKTaCONiuq8PdDzOplM2abT6sjlhdXngGrgu8NkkjYFlUDB8QlLWS2QkjFLAJIQx7h55SFnDmXJncNYjLKW-zFAvZN1-nsOKwCGM0qsYnKLYdjhvmCJXsd-3WPl1Ljtjc7OHdj1LxP366_bRhi4P0E6jduAhegQF4_vJ8BbbdPYK-0Kpb8EA7JLCk2pWLnH-Xq0UzitroPCTjxpcY3dAgGRUlBhk8LQZlfoIvdwMn_O7wDdICCTwjGVgwoQSybLC8qSCKSYyFZlYEK1oSESYGVIooChCq0QZKguZJrECi0RTQ6JMx8dos6xKfYKwkpa66Z4xwLCElKyIUkmkFCaVItJZC12useIfdR0M7vyHkPEaWm6h5R7aFhpYPH9etUWsnQBUy71q-V-qbaEjq43GfMBlSQTy9lo93O-4BQceZJ1ZwOX0P-Y-Q9t2PXWwpY02l_OVPkdb8nM5W8w77meDcfQ17Lgrg9-mRNZU |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JS8UwEB7cQD24i881B49Wa7e03rS4Px8PN7yFZhNBWtH3_P3OpFEeiAcvbQkpTWeSzjfTyTcAu3GlCSZQng4e0GOQQW5Uhcs9zkJurLQupvvY5b1e_vRU9P1mdbcXxhjjks_MPl26f_m6UUMKlR0QiSURlo_DZJokUdhu1_oJqVANiSLlnlsIOx8clyW-BnqBUbofoycUU1nzEfvjaPp9XZVfH2NnYc7m_zm2BZjzUJIdt7pfhDFTL8HsCMHgMtziLGBX_dNzRjnvDfNsqs_BCRovzfrNSz1g5Wsz1KxsyIqxOx9aOGLuK4ItN1XNsA3PlHZpVuDh7PS-vAh8FYVAIRgZBDZMeKaKvCIwVRW6kLmObCwzo3mYyTC3WaURx0ijE225qlSaxBrNFk9tFuUmXoWJuqnNGjCtCN-ZQ2sRhkmliipKVaaUtKmSkck7sPctW_HWkmUI52SEhWhVIUgVwquiAyck_5-uxHTtGlCwwi8cgf5MksaIMjkCH6l4cZioSFqtEUlKa3kHVkgZI89r9dCBzW91Cr8sPwSCJfJ4US7rf9y2A9MX9zdd0b3sXW_ADA23DbhswsTgfWi2YEp9Dl4-3rfd3PsCLB7VmA |
| 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=The+JPEG+Pleno+Learning-Based+Point+Cloud+Coding+Standard%3A+Serving+Man+and+Machine&rft.jtitle=IEEE+access&rft.au=Andre+F.+R.+Guarda&rft.au=Nuno+M.+M.+Rodrigues&rft.au=Fernando+Pereira&rft.date=2025-01-01&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=13&rft.spage=43289&rft.epage=43315&rft_id=info:doi/10.1109%2FACCESS.2025.3549316&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_7584539327554bc7914c2bfdd416bff7 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |