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Proceedings Paper

Image quality assessment with manifold and machine learning
Author(s): Christophe Charrier; Gilles Lebrun; Olivier Lezoray
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Paper Abstract

A crucial step in image compression is the evaluation of its performance, and more precisely the available way to measure the final quality of the compressed image. In this paper, a machine learning expert, providing a final class number is designed. The quality measure is based on a learned classification process in order to respect the one of human observers. Instead of computing a final note, our method classifies the quality using the quality scale recommended by the UIT. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). This was done constructing a vector containing many visual attributes. Finally, the final features vector contains more than 40 attibutes. Unfortunatley, no study about the existing interactions between the used visual attributes has been done. A feature selection algorithm could be interesting but the selection is highly related to the further used classifier. Therefore, we prefer to perform dimensionality reduction instead of feature selection. Manifold Learning methods are used to provide a low-dimensional new representation from the initial high dimensional feature space. The classification process is performed on this new low-dimensional representation of the images. Obtained results are compared to the one obtained without applying the dimension reduction process to judge the efficiency of the method.

Paper Details

Date Published: 19 January 2009
PDF: 11 pages
Proc. SPIE 7242, Image Quality and System Performance VI, 72420P (19 January 2009); doi: 10.1117/12.810164
Show Author Affiliations
Christophe Charrier, GREYC, CNRS, Univ. de Caen Basse-Normandie (France)
Gilles Lebrun, GREYC, CNRS, Univ. de Caen Basse-Normandie (France)
Olivier Lezoray, GREYC, CNRS, Univ. de Caen Basse-Normandie (France)


Published in SPIE Proceedings Vol. 7242:
Image Quality and System Performance VI
Susan P. Farnand; Frans Gaykema, Editor(s)

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