Image Quality Assessment Based on Region of Interest

Article Preview

Abstract:

In this paper a new No-Reference (NR) image quality assessment (IQA) method based on the point wise statistics of local normalized luminance signals using region of interest (ROI) processing is proposed. This algorithm firstly extracts the ROI which is relative to human subjectivity by using the image gradient and phase congruency, and then extracts the image quality feature in spatial domain. Particularly, most of the present IQA methods mainly focus on predicting the image quality with respect to human perception, yet, in some other image domains, the final receiver of a digital image may not a human. Thus, we propose a method which can assess the image quality relative to edge detection algorithm. In addition, experimental results on LIVE database are provided to justify the superior compared to the significant image quality metrics.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

350-354

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] H. R. Sheikh, A. C. Bovik, and L. Cormack, No-reference quality assessment using natural scene statistics: JPEG2000, IEEE Trans. Image Process., vol. 14, no. 11, p.1918–1927, Nov. (2005).

DOI: 10.1109/tip.2005.854492

Google Scholar

[2] Gastaldo, P., Neural networks for the no-reference assessment of perceived quality. Journal of Electronic Imaging, 2005. 14(3): p.033004.

DOI: 10.1117/1.1988313

Google Scholar

[3] Marziliano P., Dufaux F., Winkler S., Ebrahimi, T.: A no-reference perceptual blur metric. In: Proceedings of Image Processing, 2002 International Conference on Image Processing, Lausanne, Switzerland, vol. 3, p.57–60 (2002).

DOI: 10.1109/icip.2002.1038902

Google Scholar

[4] Moorthy, A.K. and A.C. Bovik. A Two-Step Framework for Constructing Blind Image Quality Indices. IEEE Signal Processing Letters, 2010. 17(5): pp.513-516.

DOI: 10.1109/lsp.2010.2043888

Google Scholar

[5] Ye P, Doermann D.  2012.  No-Reference Image Quality Assessment using Visual Codebooks. Image Processing, IEEE Transactions on PP (99): 1-1.

DOI: 10.1109/tip.2012.2190086

Google Scholar

[6] H. R. Sheikh, Image Quality Assessment Using Natural Scene Statistics, Ph.D. dissertation, University of Texas at Austin, May (2004).

Google Scholar

[7] L. Zhang, L. Zhang, X. Mou, and D. Zhang, FSIM: A feature similarity index for image quality assessment, IEEE Trans. Image Process., vol. 20, no. 8, p.2378–2386, Aug. (2011).

DOI: 10.1109/tip.2011.2109730

Google Scholar

[8] Lu, T., Y. Zhang and H. Li, An image quality assessment algorithm based on feature selection, in Intelligent Science and Intelligent Data Engineering. 2013, Springer. P. 289-297.

DOI: 10.1007/978-3-642-36669-7_36

Google Scholar

[9] P. Kovesi, Image features from phase congruency,J. Comput. Vis. Res., vol. 1, no. 3, p.1–26, (1999).

Google Scholar

[10] Mittal, A., A.K. Moorthy and A.C. Bovik. No-reference image quality assessment in the spatial domain. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2012. 21(12): pp.4695-4708.

DOI: 10.1109/tip.2012.2214050

Google Scholar

[11] M. Narwaria and W. Lin. Objective image quality assessment based on support vector algorithms, Neutral compute, vol. 12 no. 5 pp.1207-1245, (2010).

Google Scholar