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Quality Assessment of Screen Content Videos
Oral Presentation
Authors
1Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2High Performance Computing Laboratory, School of Computer Science, Institute for Research in Fundamental Sciences, Tehran, Iran
3دانشگاه شهید بهشتی، پژوهشکده فضای مجازی
Abstract
Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human
visual system. With the diversity in the contents of multimedia
signals, the conventional methods for traditional media seems
no longer satisfying. One of these emerging media, is the screen
content images/videos (SCI/Vs). Containing texts and computer
generated graphics, SCVs cannot be sufficiently expressed with
features designed for natural sceneries. Therefore, new researches
tried to devise objective quality assessment metrics, specificly for
screen contents. Recently, a dataset was proposed for quality
assessment of screen content videos. Since screen contents are
full of structures that spread in cardinal directions, we were
motivated to employ the horizontal and vertical subbands of the
wavelet transform to characterize these types of visual contents.
The features were incorporated in a full-reference method that
showed promising results on the publicly available dataset for
SCV quality assessment. The method can bo accessed via:
https://github.com/motamedNia/QASCV.
visual system. With the diversity in the contents of multimedia
signals, the conventional methods for traditional media seems
no longer satisfying. One of these emerging media, is the screen
content images/videos (SCI/Vs). Containing texts and computer
generated graphics, SCVs cannot be sufficiently expressed with
features designed for natural sceneries. Therefore, new researches
tried to devise objective quality assessment metrics, specificly for
screen contents. Recently, a dataset was proposed for quality
assessment of screen content videos. Since screen contents are
full of structures that spread in cardinal directions, we were
motivated to employ the horizontal and vertical subbands of the
wavelet transform to characterize these types of visual contents.
The features were incorporated in a full-reference method that
showed promising results on the publicly available dataset for
SCV quality assessment. The method can bo accessed via:
https://github.com/motamedNia/QASCV.
Keywords
Proceeding Title [Persian]
Quality Assessment of Screen Content Videos
Authors [Persian]
احمد محمودی ازناوه
Abstract [Persian]
Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human
visual system. With the diversity in the contents of multimedia
signals, the conventional methods for traditional media seems
no longer satisfying. One of these emerging media, is the screen
content images/videos (SCI/Vs). Containing texts and computer
generated graphics, SCVs cannot be sufficiently expressed with
features designed for natural sceneries. Therefore, new researches
tried to devise objective quality assessment metrics, specificly for
screen contents. Recently, a dataset was proposed for quality
assessment of screen content videos. Since screen contents are
full of structures that spread in cardinal directions, we were
motivated to employ the horizontal and vertical subbands of the
wavelet transform to characterize these types of visual contents.
The features were incorporated in a full-reference method that
showed promising results on the publicly available dataset for
SCV quality assessment. The method can bo accessed via:
https://github.com/motamedNia/QASCV.
visual system. With the diversity in the contents of multimedia
signals, the conventional methods for traditional media seems
no longer satisfying. One of these emerging media, is the screen
content images/videos (SCI/Vs). Containing texts and computer
generated graphics, SCVs cannot be sufficiently expressed with
features designed for natural sceneries. Therefore, new researches
tried to devise objective quality assessment metrics, specificly for
screen contents. Recently, a dataset was proposed for quality
assessment of screen content videos. Since screen contents are
full of structures that spread in cardinal directions, we were
motivated to employ the horizontal and vertical subbands of the
wavelet transform to characterize these types of visual contents.
The features were incorporated in a full-reference method that
showed promising results on the publicly available dataset for
SCV quality assessment. The method can bo accessed via:
https://github.com/motamedNia/QASCV.
Keywords [Persian]
video quality assessment، full reference، human visual system، wavelet، screen content