Publication:

Predicting individual quality ratings of compressed images through deep CNNs-based artificial observers

Date

2023
Journal article
https://doi.org/10.1016/j.image.2022.116917
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dc.contributor.authorTiotsop, Lohic Fotio
dc.contributor.authorServetti, Antonio
dc.contributor.authorBarkowsky, Marcus
dc.contributor.authorPocta, Peter
dc.contributor.authorMizdos, Tomas
dc.contributor.authorVan Wallendael, Glenn
dc.contributor.authorMasala, Enrico
dc.contributor.imecauthorVan Wallendael, Glenn
dc.contributor.orcidimecVan Wallendael, Glenn::0000-0001-9530-3466
dc.date.accessioned2023-07-18T08:12:10Z
dc.date.available2023-06-08T20:31:10Z
dc.date.available2023-06-09T08:23:11Z
dc.date.available2023-07-18T08:12:10Z
dc.date.embargo9999-12-31
dc.date.issued2023
dc.description.wosFundingTextThis work presented in this paper has been supported in part by PIC4SeR (http://pic4ser.polito.it) . Some of the computational resources were provided by HPC@POLITO (http:// www.hpc.polito.it) .
dc.identifier.doi10.1016/j.image.2022.116917
dc.identifier.issn0923-5965
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/41697
dc.publisherELSEVIER
dc.source.beginpageArt. 116917
dc.source.endpagena
dc.source.issueMarch
dc.source.journalSIGNAL PROCESSING-IMAGE COMMUNICATION
dc.source.numberofpages15
dc.source.volume112
dc.subject.keywordsCONVOLUTIONAL NEURAL-NETWORK
dc.subject.keywordsCLASSIFICATION
dc.title

Predicting individual quality ratings of compressed images through deep CNNs-based artificial observers

dc.typeJournal article
dspace.entity.typePublication
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