Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/37055
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dc.contributor.authorRibeiro, Ricardopt_PT
dc.contributor.authorTrifan, Alinapt_PT
dc.contributor.authorNeves, António J. R.pt_PT
dc.date.accessioned2023-04-14T14:17:50Z-
dc.date.available2023-04-14T14:17:50Z-
dc.date.issued2022-12-
dc.identifier.urihttp://hdl.handle.net/10773/37055-
dc.description.abstractThe wide availability and small size of different types of sensors have allowed for the acquisition of a huge amount of data about a person’s life in real time. With these data, usually denoted as lifelog data, we can analyze and understand personal experiences and behaviors. Most of the lifelog research has explored the use of visual data. However, a considerable amount of these images or videos are affected by different types of degradation or noise due to the non-controlled acquisition process. Image Quality Assessment can plays an essential role in lifelog research to deal with these data. We present in this paper a twofold study on the topic of blind image quality assessment. On the one hand, we explore the replication of the training process of a state-of-the-art deep learning model for blind image quality assessment in the wild. On the other hand, we present evidence that blind image quality assessment is an important pre-processing step to be further explored in the context of information retrieval in lifelogging applications. We consider that our efforts have been successful in the replication of the model training process, achieving similar results of inference when compared to the original version, while acknowledging a fair number of assumptions that we had to consider. Moreover, these assumptions motivated an extensive additional analysis that led to significant insights on the influence of both batch size and loss functions when training deep learning models in this context. We include preliminary results of the replicated model on a lifelogging dataset, as a potential reproducibility aspect to be considered.pt_PT
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00127%2F2020/PTpt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectimage processingpt_PT
dc.subjectBlind image quality assessmentpt_PT
dc.subjectDeep learningpt_PT
dc.subjectReplicabilitypt_PT
dc.subjectReproducibilitypt_PT
dc.subjectImage retrievalpt_PT
dc.subjectLifeloggingpt_PT
dc.titleBlind image quality assessment with deep learning: a replicability study and its reproducibility in lifeloggingpt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.issue1pt_PT
degois.publication.titleApplied Sciencespt_PT
degois.publication.volume13pt_PT
dc.identifier.doi10.3390/app13010059pt_PT
dc.identifier.essn2076-3417pt_PT
dc.identifier.articlenumber59pt_PT
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