Assessment of full color image quality with singular value decomposition

Aleksandr Shnayderman; Ahmet M. Eskicioglu

doi:10.1117/12.584550

17 January 2005 Assessment of full color image quality with singular value decomposition

Aleksandr Shnayderman, Ahmet M. Eskicioglu

Proceedings Volume 5668, Image Quality and System Performance II; (2005) https://doi.org/10.1117/12.584550
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

In subjective evaluation of distorted images, human observers usually consider the type of distortion, the amount of distortion, and the distribution of error. We recently proposed an image quality measure, M-SVD, for gray-scale images that can be used as a graphical tool to predict the distortion based on these three factors, and also as a numerical tool to assess the overall visual quality of the distorted image. It performs better than two state-of-the-art metrics, Q and MSSIM, especially when we compute the correlation with mean opinion score across different types of noise. The test image was degraded using six types of noise (JPEG, JPEG 2000, Gaussian blur, Gaussian noise, sharpening and DC-shifting), each with five different levels of intensity. In this paper, we extend M-SVD to full color images using a color model which decouples the color and gray-scale information in an image. Our experiments show that using only the luminance component, the measure outperforms Q and MSSIM. When we also use the two chrominance layers, the performance of M-SVD becomes slightly higher whereas the performance of Q and MSSIM is degraded. This indicates that the color components may also contribute to the performance of the proposed measure.

Citation Download Citation

Aleksandr Shnayderman and Ahmet M. Eskicioglu "Assessment of full color image quality with singular value decomposition", Proc. SPIE 5668, Image Quality and System Performance II, (17 January 2005); https://doi.org/10.1117/12.584550

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