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
Statistics of Fractional Moments Applied to 3D Video Streams / Nigmatullin, R; Ceglie, C; Maione, G; Striccoli, D. - ELETTRONICO. - (2014). (Intervento presentato al convegno International Conference on Fractional Differentiation and Its Applications, ICFDA 2014 tenutosi a Catania, Italy nel June 23-25 , 2014) [10.1109/ICFDA.2014.6967368].
Statistics of Fractional Moments Applied to 3D Video Streams
Ceglie C;Maione G;Striccoli D
2014-01-01
Abstract
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 classificationI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
