Journal of Computer Science

AN INFORMATION-THEORETIC IMAGE QUALITY MEASURE: COMPARISON WITH STATISTICAL SIMILARITY

Asmhan Flieh Hassan, Dong Cai-lin and Zahir M. Hussain

DOI : 10.3844/jcssp.2014.2269.2283

Journal of Computer Science

Volume 10, Issue 11

Pages 2269-2283

Abstract

We present an information-theoretic approach for structural similarity for assessing gray scale image quality. The structural similarity measure SSIM, proposed in 2004, has been successflly used and verfied. SSIM is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivial amount of similarity for two different images. Also, SSIM cannot perform well (in detecting similarity or dissimilarity) at low peak signal to noise ratio (PSNR). In this study, we present a novel image similarity measure, HSSIM, by using information-theoretic technique based on joint histogram. The proposed method has been tested under Gaussian noise. Simulation results show that the proposed measure HSSIM outperforms statistical similarity SSIM by ability to detect similarity under very low PSNR. The average difference is about 20dB.

Copyright

© 2014 Asmhan Flieh Hassan, Dong Cai-lin and Zahir M. Hussain. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.