Image quality assessment with manifold and machine learning

Christophe Charrier; Gilles Lebrun; Olivier Lezoray

doi:10.1117/12.810164

19 January 2009 Image quality assessment with manifold and machine learning

Christophe Charrier, Gilles Lebrun, Olivier Lezoray

Proceedings Volume 7242, Image Quality and System Performance VI; 72420P (2009) https://doi.org/10.1117/12.810164
Event: IS&T/SPIE Electronic Imaging, 2009, San Jose, California, United States

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.

Citation Download Citation

Christophe Charrier, Gilles Lebrun, and Olivier Lezoray "Image quality assessment with manifold and machine learning", Proc. SPIE 7242, Image Quality and System Performance VI, 72420P (19 January 2009); https://doi.org/10.1117/12.810164

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available