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Statistics of fractional moments applied to 3D video streams

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dc.contributor.author Nigmatullin R.
dc.contributor.author Ceglie C.
dc.contributor.author Maione G.
dc.contributor.author Striccoli D.
dc.date.accessioned 2018-09-18T20:36:19Z
dc.date.available 2018-09-18T20:36:19Z
dc.date.issued 2014
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/141614
dc.description.abstract © 2014 IEEE. The interest in processing three-dimensional (3D) videos is ever increasing because of the exponential growth of sophisticated devices supporting 3D streams. However, transmitting compressed 3D videos on channels with relatively limited bandwidth resources is a challenging research problem, because of the high variability of 3D streams. A stable and robust characterization of the statistical properties of 3D videos could be very useful for several applications (bandwidth management and control by effective schedulers/controllers, call admission control schemes, etc.). This work proposes a straightforward characterization method, based on the statistics of fractional moments. The properties of long sequences of 3D videos are reduced to a very small set of fitting parameters, constituting the video 'fingerprint'. The method is applied to a set of videos, with different compression degrees. Moreover, possible similarities among different fingerprints are investigated for an effective 3D video classification.
dc.subject 3D video
dc.subject Fitting Parameters
dc.subject Fractional Moments
dc.subject Statistical Method
dc.title Statistics of fractional moments applied to 3D video streams
dc.type Conference Paper
dc.collection Публикации сотрудников КФУ
dc.source.id SCOPUS-2014-SID84918496239


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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