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Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator

2025-06 journal article Open access
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorMari, Daniele
dc.contributor.authorGuarda, André
dc.contributor.authorM. M. Rodrigues, Nuno
dc.contributor.authorMilani, Simone
dc.contributor.authorPereira, Fernando
dc.date.accessioned2025-07-15T15:05:58Z
dc.date.available2025-07-15T15:05:58Z
dc.date.issued2025-06
dc.date.updated2025-07-14T16:52:56Z
dc.descriptionEngineering controlled terms Binary sequences; Codes (symbols); Deep learning; Digital image storage; Learning systems; Multimedia systems; Scalability; Video signal processing
dc.descriptionEngineering uncontrolled terms 'current; Bitstreams; Cloud geometry; Deep learning-based codec; Geometry coding; JPEG pleno PCC; Point cloud geometry coding; Point-clouds; Probability estimator; Scalable coding
dc.descriptionEngineering main heading Scalable video coding
dc.descriptionInformation and data sciences: https://www.it.pt/Publications/PaperConference/41189
dc.description.abstractIn the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration.eng
dc.description.sponsorshipThis work was funded in part by the European Union (EU) through the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, with a partnership on ‘‘Telecommunications of the Future’’ Program ‘‘RESearch and innovation on future Telecommunications systems and networks (Restart)’’ under Grant PE00000001; in part by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, entitled ‘‘Deep Learning-Based Point Cloud Representation,’’ under Project PTDC/EEI-COM/1125/2021; and in part by FCT/Ministério da Educação, Ciência e Inovação (MECI) through National Funds and when applicable co-funded EU Funds: Instituto de Telecomunicações under Grant UID/50008. The work of Daniele Mari was supported by Fondazione CaRiPaRo under Grant ‘‘Dottorati di Ricerca’’ 2021/2022.
dc.description.versionN/A
dc.identifier.citationD. Mari, A. F. R. Guarda, N. M. M. Rodrigues, S. Milani and F. Pereira, "Point Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimator," in IEEE Access, vol. 13, pp. 108025-108042, 2025, doi: 10.1109/ACCESS.2025.3580680.
dc.identifier.doi10.1109/access.2025.3580680en_US
dc.identifier.issn2169-3536en_US
dc.identifier.slugcv-prod-4528492
dc.identifier.urihttp://hdl.handle.net/10400.8/13656
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationDeep learning-based Point Cloud Representation
dc.relationInstituto de Telecomunicações
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/11037781
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPoint cloud geometry coding
dc.subjectJPEG Pleno PCC
dc.subjectdeep learning-based codec
dc.subjectscalable coding
dc.titlePoint Cloud Geometry Scalable Coding Using a Resolution and Quality-Conditioned Latents Probability Estimatoreng
dc.type journal articleen_US
dspace.entity.typePublication
oaire.awardTitleDeep learning-based Point Cloud Representation
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEEI-COM%2F1125%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT
oaire.citation.endPage108042
oaire.citation.startPage108025
oaire.citation.titleIEEE Accessen_US
oaire.citation.volume13en_US
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGuarda
person.familyNameM. M. Rodrigues
person.givenNameAndré
person.givenNameNuno
person.identifier.ciencia-idF811-146F-4EE9
person.identifier.orcid0000-0001-5996-1074
person.identifier.orcid0000-0001-9536-1017
person.identifier.scopus-author-id7006052345
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid6917-B121-4E34 | NUNO MIGUEL MORAIS RODRIGUES
rcaap.rightsopenAccessen_US
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