hrcak mascot   Srce   HID

Izvorni znanstveni članak
https://doi.org/10.2498/cit.1001822

Selecting Low-level Features for Image Quality Assessment by Statistical Methods

Atidel Lahouhou ; University Paris 13
Emmanuel Viennet
Azeddine Beghdadi ; University Paris 13

Puni tekst: engleski, pdf (476 KB) str. 183-189 preuzimanja: 674* citiraj
APA 6th Edition
Lahouhou, A., Viennet, E. i Beghdadi, A. (2010). Selecting Low-level Features for Image Quality Assessment by Statistical Methods. Journal of computing and information technology, 18 (2), 183-189. https://doi.org/10.2498/cit.1001822
MLA 8th Edition
Lahouhou, Atidel, et al. "Selecting Low-level Features for Image Quality Assessment by Statistical Methods." Journal of computing and information technology, vol. 18, br. 2, 2010, str. 183-189. https://doi.org/10.2498/cit.1001822. Citirano 25.06.2019.
Chicago 17th Edition
Lahouhou, Atidel, Emmanuel Viennet i Azeddine Beghdadi. "Selecting Low-level Features for Image Quality Assessment by Statistical Methods." Journal of computing and information technology 18, br. 2 (2010): 183-189. https://doi.org/10.2498/cit.1001822
Harvard
Lahouhou, A., Viennet, E., i Beghdadi, A. (2010). 'Selecting Low-level Features for Image Quality Assessment by Statistical Methods', Journal of computing and information technology, 18(2), str. 183-189. doi: https://doi.org/10.2498/cit.1001822
Vancouver
Lahouhou A, Viennet E, Beghdadi A. Selecting Low-level Features for Image Quality Assessment by Statistical Methods. Journal of computing and information technology [Internet]. 2010 [pristupljeno 25.06.2019.];18(2):183-189. doi: https://doi.org/10.2498/cit.1001822
IEEE
A. Lahouhou, E. Viennet i A. Beghdadi, "Selecting Low-level Features for Image Quality Assessment by Statistical Methods", Journal of computing and information technology, vol.18, br. 2, str. 183-189, 2010. [Online]. doi: https://doi.org/10.2498/cit.1001822

Sažetak
Image quality assessment is an
important component in every image processing
system where the last link of the chain is the
human observer. This domain is of increasing
interest, in particular in the context of image
compression where coding scheme optimization
is based on the distortion measure. Many
objective image quality measures have been
proposed in the literature and validated by
comparing them to the Mean Opinion Score
(MOS). We propose in this paper an empirical
study of several indicators and show how one
can improve the performances by combining
them. We learn a regularized regression model
and apply variable selection techniques to
automatically find the most relevant indicators.
Our technique enhances the state of the art
results on two publicly available databases.

Ključne riječi
Image quality assessment; perceptual

Hrčak ID: 59530

URI
https://hrcak.srce.hr/59530

Posjeta: 794 *