Research Article Open Access

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

Asmhan Flieh Hassan1, Dong Cai-lin2 and Zahir M. Hussain3
  • 1 University of Kufa, Iraq
  • 2 HuaZhong Normal University, China
  • 3 Edith Cowan University, Australia

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.

Journal of Computer Science
Volume 10 No. 11, 2014, 2269-2283

DOI: https://doi.org/10.3844/jcssp.2014.2269.2283

Submitted On: 2 May 2014 Published On: 17 December 2014

How to Cite: Hassan, A. F., Cai-lin, D. & Hussain, Z. M. (2014). AN INFORMATION-THEORETIC IMAGE QUALITY MEASURE: COMPARISON WITH STATISTICAL SIMILARITY. Journal of Computer Science, 10(11), 2269-2283. https://doi.org/10.3844/jcssp.2014.2269.2283

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Keywords

  • Joint Histogram
  • Image Structural Similarity
  • Image Quality Assessment
  • Image Processing