Publication
Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions
| dc.contributor.author | Prazeres, João | |
| dc.contributor.author | Rodrigues, Rafael | |
| dc.contributor.author | Pereira, Manuela | |
| dc.contributor.author | Pinheiro, Antonio M. G. | |
| dc.date.accessioned | 2023-05-24T08:30:36Z | |
| dc.date.available | 2023-05-24T08:30:36Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In this paper, a quality evaluation of three point cloud coding solutions based on machine learning technology is presented, notably, ADLPCC, PCC_GEO_CNN, and PCGC, as well as LUT_SR, which uses multi-resolution Look-Up Tables. Moreover, the MPEG G-PCC was used as an anchor. A set of six point clouds, representing both landscapes and objects were coded using the five encoders at different bit rates, and a subjective test, where the distorted and reference point clouds were rotated in a video sequence side by side, is carried out to assess their performance. Furthermore, the performance of point cloud objective quality metrics that usually provide a good representation of the coded content is analyzed against the subjective evaluation results. The obtained results suggest that some of these metrics fail to provide a good representation of the perceived quality, and thus are not suitable to evaluate some distortions created by machine learning-based solutions. A comparison between the analyzed metrics and the type of represented scene or codec is also presented. | pt_PT |
| dc.description.sponsorship | This research was funded by the Portuguese FCT-Fundação para a Ciência e Tecnologia under the project UIDB/50008/2020, PLive X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 - C4 - Centro de Competencias em Cloud Computing. | pt_PT |
| dc.description.version | info:eu-repo/semantics/acceptedVersion | pt_PT |
| dc.identifier.doi | 10.1145/3552457.3555730 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.6/13346 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation | Instituto de Telecomunicações | |
| dc.relation.publisherversion | https://dl.acm.org/doi/pdf/10.1145/3552457.3555730 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Point Clouds | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Quality evaluation | pt_PT |
| dc.subject | Coding | pt_PT |
| dc.title | Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Instituto de Telecomunicações | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT | |
| oaire.citation.endPage | 65 | pt_PT |
| oaire.citation.startPage | 57 | pt_PT |
| oaire.citation.title | Association for Computing Machinery | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Casanova Prazeres | |
| person.familyName | Mendes Rodrigues | |
| person.familyName | Pereira | |
| person.familyName | Pinheiro | |
| person.givenName | João Pedro | |
| person.givenName | Jorge Rafael | |
| person.givenName | Manuela | |
| person.givenName | Antonio | |
| person.identifier.ciencia-id | 441A-CABD-41E0 | |
| person.identifier.ciencia-id | D112-43CA-98E0 | |
| person.identifier.ciencia-id | 0515-7E9C-B97F | |
| person.identifier.ciencia-id | 2218-265E-17D2 | |
| person.identifier.orcid | 0000-0002-5553-0231 | |
| person.identifier.orcid | 0000-0002-9481-9601 | |
| person.identifier.orcid | 0000-0002-8648-6464 | |
| person.identifier.orcid | 0000-0002-5968-9901 | |
| person.identifier.rid | B-2723-2012 | |
| person.identifier.scopus-author-id | 35248984200 | |
| person.identifier.scopus-author-id | 8420644500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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