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Multimedia content classification metrics for content adaptation

dc.contributor.authorFernandes, Rui
dc.contributor.authorAndrade, M.T.
dc.date.accessioned2016-01-11T10:57:38Z
dc.date.available2016-01-11T10:57:38Z
dc.date.issued2015
dc.description.abstractMultimedia content consumption is very popular nowadays. However, not every content can be consumed in its original format: the combination of content, transport and access networks, consumption device and usage environment characteristics may all pose restrictions to that purpose. One way to provide the best possible quality to the user is to adapt the content according to these restrictions as well as user preferences. This adaptation stage can be best executed if knowledge about the content is known à- priori. In order to provide this knowledge we classify the content based on metrics to define its temporal and spatial complexity. The temporal complexity classification is based on the Motion Vectors of the predictive frames of the content, and the spatial complexity classification is based on an edge detection algorithm and an image activity measure.pt_PT
dc.identifier.citationFernandes, Rui; Andrade, M.T. (2015). Multimedia content classification metrics for content adaptation. In DCE2015. Portopt_PT
dc.identifier.urihttp://hdl.handle.net/10198/12582
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMultimedia classificationpt_PT
dc.subjectSpatial complexitypt_PT
dc.subjectTemporal complexitypt_PT
dc.subjectMultimedia adaptationpt_PT
dc.titleMultimedia content classification metrics for content adaptationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlacePortopt_PT
oaire.citation.titleDCE2015pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT

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