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dc.contributor.authorRodríguez-Fernández, Nereida
dc.contributor.authorSantos, Iria
dc.contributor.authorTorrente-Patiño, Álvaro
dc.contributor.authorCarballal, Adrián
dc.date.accessioned2020-10-21T17:16:28Z
dc.date.available2020-10-21T17:16:28Z
dc.date.issued2020-08-19
dc.identifier.citationRodriguez-Fernandez, N.; Santos, I.; Torrente-Patiño, A.; Carballal, A. Digital Image Quality Prediction System. Proceedings 2020, 54, 15.es_ES
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/26500
dc.description.abstract[Abstract] “A picture is worth a thousand words.” Based on this well-known adage, we can say that images are important in our society, and increasingly so. Currently, the Internet is the main channel of socialization and marketing, where we seek to communicate in the most efficient way possible. People receive a large amount of information daily and that is where the need to attract attention with quality content and good presentation arises. Social networks, for example, are becoming more visual every day. Only on Facebook can you see that the success of a publication increases up to 180% if it is accompanied by an image. That is why it is not surprising that platforms such as Pinterest and Instagram have grown so much, and have positioned themselves thanks to their power to communicate with images. In a world where more and more relationships and transactions are made through computer applications, many decisions are made based on the quality, aesthetic value or impact of digital images. In the present work, a quality prediction system for digital images was developed, trained from the quality perception of a group of humans.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 201716es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 201849es_ES
dc.language.isoenges_ES
dc.publisherMDPI AGes_ES
dc.relation.urihttps://doi.org/10.3390/proceedings2020054015es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine learninges_ES
dc.subjectGenetic algorithmes_ES
dc.subjectQualityes_ES
dc.subjectImagees_ES
dc.subjectPredictiones_ES
dc.subjectDatasetes_ES
dc.titleDigital Image Quality Prediction Systemes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedingses_ES
UDC.volume54es_ES
UDC.issue1es_ES
UDC.startPage15es_ES
dc.identifier.doi10.3390/proceedings2020054015
UDC.conferenceTitle3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020es_ES


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