No-Reference Image Quality Assessment Based on Edges
Keywords:Image Quality, Blurring, No-reference, Image Edge
AbstractImage quality assessment is a crucial topic in the field of image processing. In this paper, we propose an edge-based no-reference image quality assessment method. The following factor is applied to assess image quality, namely, improved blur measurement. In the improved blur measurement method, we propose an algorithm that improves the accuracy in measuring image blurs and attains effective execution speed in time complexity. Experimental results reveal that using the proposed approach helps attain satisfactory image quality assessment results.
(1) J. Caviedes and S. Gurbuz, “No-reference sharpness metric based on local edge kurtosis,” Proceedings of 2002 International Conference on Image Processing, vol. 3, pp. 53-56, 2002.
(2) D.M. Chandler, “Seven challenges in image quality assessment: past, present, and future research,” ISRN Signal Processing, 2013.
(3) C.C. Chang and C.C. Chang, “An improved method for no-reference image quality assessment,” International Conference on Information Technology and Industrial Application, April 2016.
(4) K. De and V.Masilamani, “A new no-reference image quality measure for blurred Images in spatial domain,” Journal of Image and Graphics, vol. 1, no.1, pp. 39-42, 2013.
(5) J. Dijk, M.van Ginkel, R.J. van Asselt, L.J. van Vliet, and P.W. Verbeek, “A new sharpness measure based on Gaussian lines and edges,” Proceedings of International Conference on Computer Analysis of Images and Patterns (CAIP), LNCS, vol. 2756, pp. 149-156, 2003.
(6) F.S. Frey and J.M. Reilly, Digital Imaging for Photographic Collections: Foundations for Technical Standards (Rochester, NY: Image Permanence Institute, Rochester Institute of Technology), pp.10, 1999.
(7) ITU-R Recommendation BT.500-10. Methodology for the subjective assessment of the quality of the television pictures, 2000.
(8) P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “A no-reference perceptual blur metric,” Proceedings of 2002 International Conference on Image Processing, pp. 57-60, NY, USA, 2002.
(9) N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, C.-C. Jay Kuo, Image database TID2013: Peculiarities, results and perspectives, Signal Processing: Image Communication, vol. 30, pp. 57-77, Jan. 2015.
(10) N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, C.-C. Jay Kuo, Color Image Database TID2013: Peculiarities and Preliminary Results, Proceedings of 4th Europian Workshop on Visual Information Processing EUVIP2013, Paris, France, June 10-12, pp. 106-111, 2013.
(11) N. Ponomarenko, O. Ieremeiev, V. Lukin, K. Egiazarian, L. Jin, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, C.-C. Jay Kuo, A New Color Image Database TID2013: Innovations and Results, Proceedings of ACIVS, Poznan, Poland, pp. 402-413, October 2013.
(12) J. Tang, E.Peli, and S. Acton, “Image enhancement using a contrast measure in the compressed domain,” IEEE Signal Processing Letters, vol. 10, no.10, pp. 289-292, 2003.
(13) H. Tong, M. Li, H. Zhang, C. Zhang, J. He, and W.Y. Ma, “Learning no-reference quality metric by examples,” Proceedings of the 11th International Multimedia Modelling Conference, pp. 247-254, January 2005.
(14) Z. Wang and E.P. Simoncelli, “Reduced-reference image quality assessment using a wavelet-domain natural image statistic model,” Proceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging X, vol. 5666, San Jose, CA, January 2005.