An image similarity measure using enhanced human visual system characteristics

Shahan Nercessian; Sos S. Agaian; Karen A. Panetta

doi:10.1117/12.883301

31 May 2011 An image similarity measure using enhanced human visual system characteristics

Shahan Nercessian, Sos S. Agaian, Karen A. Panetta

Proceedings Volume 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011; 806310 (2011) https://doi.org/10.1117/12.883301
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Image similarity measures are crucial for image processing applications which require comparisons to ideal reference images in order to assess performance. The Structural Similarity (SSIM), Gradient Structural Similarity (GSSIM), 4-component SSIM (4-SSIM) and 4-component GSSIM (4-GSSIM) indexes are motivated by the fact that the human visual system is adapted to extract local structural information. In this paper, we propose a new measure which enhances the gradient information used for quality assessment. An analysis of the proposed image similarity measure using the LIVE database of distorted images and their corresponding subjective evaluations of visual quality illustrate the improved performance of the proposed metric.

Citation Download Citation

Shahan Nercessian, Sos S. Agaian, and Karen A. Panetta "An image similarity measure using enhanced human visual system characteristics", Proc. SPIE 8063, Mobile Multimedia/Image Processing, Security, and Applications 2011, 806310 (31 May 2011); https://doi.org/10.1117/12.883301

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