Selecting Low-level Features for Image Quality Assessment by Statistical Methods
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| Title | Selecting Low-level Features for Image Quality Assessment by Statistical Methods |
| Authors | |
| Abstract | Image quality assessment is animportant component in every image processingsystem where the last link of the chain is thehuman observer. This domain is of increasinginterest, in particular in the context of imagecompression where coding scheme optimizationis based on the distortion measure. Manyobjective image quality measures have beenproposed in the literature and validated bycomparing them to the Mean Opinion Score(MOS). We propose in this paper an empiricalstudy of several indicators and show how onecan improve the performances by combiningthem. We learn a regularized regression modeland apply variable selection techniques toautomatically find the most relevant indicators.Our technique enhances the state of the artresults on two publicly available databases. |
| Publisher | University of Zagreb, University Computing Centre - SRCE |
| Date | 2010-07-16 |
| Source | Journal of Computing and Information Technology Vol 18, No 2 (2010): The Overseas Contribution to the ITI Conference, including some 2009 Conferenc |
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