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A Double Deep Learning-Based Solution for Efficient Event Data Coding and Classification

2025-03Journal 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.authorSeleem, Abdelrahman
dc.contributor.authorGuarda, André
dc.contributor.authorM. M. Rodrigues, Nuno
dc.contributor.authorPereira, Fernando
dc.date.accessioned2025-07-15T15:51:30Z
dc.date.available2025-07-15T15:51:30Z
dc.date.issued2025-03
dc.date.updated2025-07-14T16:51:56Z
dc.descriptionEngineering uncontrolled terms Classification performance; Coding standards; Data classification; Data coding; Deep learning; Event data; Event data classification; Event data coding; Point cloud coding; Point-clouds
dc.descriptionEngineering main heading Deep learning
dc.descriptionInformation and data sciences: https://www.it.pt/Publications/PaperJournal/34611
dc.description.abstractEvent cameras have the ability to capture asynchronous per-pixel brightness changes, usually called "events", offering advantages over traditional frame-based cameras for computer vision tasks. Efficiently coding event data is critical for practical transmission and storage, given the very significant number of events captured. This paper proposes a novel double deep learning-based solution for efficient event data coding and classification, using a point cloud-based representation for events. Moreover, since the conversions from events to point clouds and back to events are key steps in the proposed solution, novel tools are proposed and their impact is evaluated in terms of compression and classification performance. Experimental results show that it is possible to achieve a classification performance for decompressed events which is similar to the one for original events, even after applying a lossy point cloud codec, notably the recent deep learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate reduction. Experimental results also demonstrate that events coded using the JPEG standard achieve better classification performance than those coded using the conventional lossy MPEG Geometry-based Point Cloud Coding standard for the same rate. Furthermore, the adoption of deep learning-based coding offers future high potential for performing computer vision tasks in the compressed domain, which allows skipping the decoding stage, thus mitigating the impact of compression artifacteng
dc.description.sponsorshipThis work was funded by the Fundação para a Ciência e a Tecnologia (FCT, Portugal) through the research project PTDC/EEI-COM/1125/2021, entitled ‘‘Deep Learning-based Point Cloud Representation’’, and by FCT/Ministério da Educação, Ciência e Inovação (MECI) through national funds and when applicable co-funded European Union (EU) funds under UID/50008: Instituto de Telecomunicações.
dc.description.versionN/A
dc.identifier.citationA. Seleem, A. F. R. Guarda, N. M. M. Rodrigues and F. Pereira, "A Double Deep Learning-Based Solution for Efficient Event Data Coding and Classification," in IEEE Access, vol. 13, pp. 48703-48719, 2025, doi: 10.1109/ACCESS.2025.3551073. [cf. IEEE]
dc.identifier.doi10.1109/access.2025.3551073en_US
dc.identifier.issn2169-3536en_US
dc.identifier.slugcv-prod-4528490
dc.identifier.urihttp://hdl.handle.net/10400.8/13659
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/10925359
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learning
dc.subjectevent data
dc.subjectevent data classification
dc.subjectevent data coding
dc.subjectpoint cloud coding
dc.titleA Double Deep Learning-Based Solution for Efficient Event Data Coding and Classificationeng
dc.typejournal 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/UIDP%2F50008%2F2020/PT
oaire.citation.endPage48719
oaire.citation.startPage48703
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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relation.isAuthorOfPublicationb4ebe652-7f0e-4e67-adb0-d5ea29fc9e69
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relation.isProjectOfPublication91a8e212-cbb0-462f-b533-5ed3552e8067
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