An alternative image quality assessment method for blurred images

Year 2016, Volume: 4 Issue: 1, 46 - 50, 30.03.2016

Halime Boztoprak

Abstract

This study proposes a no-reference image quality assessment method for blurred images. In this approach, first, a discrete wavelet transform was applied to the sample images and then the results were decomposed into four subbands. This was followed by the calculation of the spatial frequencies of high-high (HH2) and low-low (LL2) subbands. Then the ratio of spatial frequencies of HH2 and LL2 subbands was calculated. Information about the image quality was obtained by using this ratio, with lower values indicative of better image quality. The study aims to investigate whether the proposed method is capable of measuring the image quality. The proposed technique was tested on the standard images. Three different images were used, of which each one was distorted with the same type and amount of noise. Motion noise, blurring and sharpening was applied to distort the images. The performance of the proposed method was evaluated and compared with eight representative image quality measures. This provides a meaningful comparison across different types of image distortions. Then, the cameraman image was also blurred with two different noises: Gaussian and disk-shaped blur. The varying amount of blur was compared with Universal Image Quality Index (UIQI) values of the cameraman image. The method gives good results in different resolutions as well. Its computation is easy, independent of viewing conditions.

Keywords

Blurring, Image Quality, Spatial Frequency, Wavelet Transform

References

• [1] Z. Wang, A. C. Bovik, & L. Lu, "Why is image quality assessment so difficult?" Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on. Vol. 4. IEEE, 2002. [2] A. Zaric, et al. “Image quality assessment-comparison of objective assessment with results of subjective test”, pp 113-118, Sep. 2010. [3] M. Jiang, Digital Image Processing. Department of Information Science, School of Mathematics, Peking University, 2003. [4] A. Havstad, Image quality assessment using artificial neural networks. MSc, Engineering and Mathematics, Edith Cowan University, 2004. [5] Z. Wang & A.C. Bovik, "A universal image quality index." Signal Processing Letters, IEEE 9.3 (2002): 81-84. [6] Al-Najjar, Yusra AY. Dr. Der Chen Soong, “Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI”." International Journal of Scientific & Engineering Research 3.8 (2012): 1. [7] N. Ramanaiah & S. Kumar, (2013). Removal of hıgh density salt and pepper noise in images and videos using denoising methods. [8]R. Arumugham, K. Vellingiri, W. F. Habeebrakuman, & K. Mohan, (2012). A New Denoising Approach for the Removal of Impulse Noise from Color Images and Video Sequences. Image Analysis & Stereology, 31(3), 185-191. [9] Om, H., & Biswas, M. (2015). A generalized image denoising method using neighbouring wavelet coefficients. Signal, Image and Video Processing, 9(1), 191-200. [10] De, Kanjar, and V. Masilamani. "Image Sharpness Measure for Blurred Images in Frequency Domain." Procedia Engineering 64 (2013): 149-158. [11] Ong EePing, et al. "A no-reference quality metric for measuring image blur." Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on. Vol. 1. Ieee, 2003. [12] N.K. Chern, N.P.A. Neow, M.H. Ang Jr, “Blur determination in the compressed domain using DCT information”, In Proc. IEEE Int. Conf. Robotics and Automation, 3, pp. 2791-96, 2001. [13] Y. Yildiray. "A histogram based image quality index." Przegl Ą Elektrotechniczny Electr. Rev. NR 7a 88 (2012): 126-129. [14] Nill, Norman B., and Brian Bouzas. "Objective image quality measure derived from digital image power spectra." Optical engineering 31.4 (1992): 813-825. [15] Ferzli, Rony, and Lina J. Karam. "No-reference objective wavelet based noise immune image sharpness metric." Image Processing, 2005. ICIP 2005. IEEE International Conference on. Vol. 1. IEEE, 2005. [16] Dumic, Emil, Sonja Grgic, and Mislav Grgic. "New image-quality measure based on wavelets." Journal of Electronic Imaging 19.1 (2010): 011018-011018. [17] Kerouh, F., A. Serir. "A No Reference Quality Metric for Measuring Image Blur In Wavelet Domain." International journal of Digital Information and Wireless Communication 1.4 (2012): 767-776. [18] Vu, Phong V., and Damon M. Chandler. "A fast wavelet-based algorithm for global and local image sharpness estimation." Signal Processing Letters, IEEE19.7 (2012): 423-426. [19] Avcybab, Y., Image quality statistics and their use in stange analysis and compression, Ph.D, Bogazici University, İstanbul, Turkey, 2001. [20] Rafael, C., Gonzalez Woods and Richard, E., Digital Image Processing, Addison-Wesley Publishing Company, 1992. [21] Lal, Shyam, and Rahul Kumar. "Enhancement of Hyperspectral Real World Images Using Hybrid Domain Approach." International Journal of Image, Graphics and Signal Processing (IJIGSP) 5.5 (2013): 29. [22] Planitz, B., and A. Maeder. "Medical image watermarking: A study on image degradation." Proc. Australian Pattern Recognition Society Workshop on Digital Image Computing, WDIC. 2005. [23] R.J.E. Merry, Steinbuch M., van de Molengraft M.J.G., Wavelet theory and applications literature study, Eindhoven University of Technology, Department of Mechanical Engineering, Control Systems Technology Group, 2005. [24] Dia, Dhaha, et al. "Multi-level discrete wavelet transform architecture design." Proceedings of the world congress on engineering. Vol. 1. 2009. [25] A. Eskicioglu, M., and Paul S. Fisher. "Image quality measures and their performance." Communications, IEEE Transactions on 43.12 (1995): 2959-2965. [26] Maddali R., Prasad K.S., Bindu C.H., Discrete wavelet transform based medical image fusion using spatial frequency technique, Int J of Sys Alg & App (IJSAA) 2012; 2: 2277-2677. [27] Jain, Atika, P. M. Kanjalkar, and J. V. Kulkarni. "Estimation of image focus measure and restoration by Wavelet." Intelligent Networks and Intelligent Systems (ICINIS), 2011 4th International Conference on. IEEE, 2011. [28] H. Boztoprak, Y. Özbay, A new method for segmentation of microscopic images on activated sludge. Turk J Elec Eng & Comp Sci, 23.Sup. 1 (2015): 2253-2266.