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.
RAS ID
18453
Document Type
Journal Article
Date of Publication
1-1-2014
Faculty
Faculty of Health, Engineering and Science
School
School of Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
Science Publications
Comments
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. Available here