Image quality assessment based on distortion identification

Aladine Chetouani; Azeddine Beghdadi

doi:10.1117/12.876308

24 January 2011 Image quality assessment based on distortion identification

Aladine Chetouani, Azeddine Beghdadi

Proceedings Volume 7867, Image Quality and System Performance VIII; 78670E (2011) https://doi.org/10.1117/12.876308
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

A New Global Full-Reference Image Quality System based on classification and fusion scheme is proposed. It consists of many steps. The first step is devoted to the identification of the type of degradation contained in a given image based a Linear Discriminant Analysis (LDA) classifier using some common Image Quality Metric (IQM) as feature inputs. An IQM per degradation (IQM-D) is then used to estimate the quality of the image. For a given degradation type, the appropriate IQM-D is derived by combining the top three best IQMs using an Artificial Neural Network model. The performance of the proposed scheme is evaluated first in terms of good degradation identification. Then, for each distortion type the image quality estimation is evaluated in terms of good correlation with the subjective judgments using the TID 2008 image database.

Citation Download Citation

Aladine Chetouani and Azeddine Beghdadi "Image quality assessment based on distortion identification", Proc. SPIE 7867, Image Quality and System Performance VIII, 78670E (24 January 2011); https://doi.org/10.1117/12.876308

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