International Journal of Modern Physics and Applications
Articles Information
International Journal of Modern Physics and Applications, Vol.1, No.4, Sep. 2015, Pub. Date: Jul. 23, 2015
No-Reference Quality Assessment Using the Entropy of First Derivative of Blurred Images in HSV Color Space
Pages: 175-180 Views: 2202 Downloads: 1084
[01] Ahmed Majeed Hameed, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq.
[02] Moaz H. Ali, Al-Safwa University College, Department of Computer Technics, Karbala, Iraq.
Quality assessment of No-Reference (NR) images is the process of finding a novel metric via comparable results with the results of Full-Reference (FR) metrics. Otherwise, it is the process of finding a computational model that can predict the human perceptual system. This research paper focused on the process of NR images quality assessment using the Entropy of First Derivative (EFD). Four color images are used as a sample in the Hue-Saturation-Value (HSV) system. The images were distorting manually with Gaussian blur, and the quality of distorted images was measured using the Normalize Mean Square Error (NMSE) as a FR metric. Then the EFD metric was used to assess the quality of distorted images. The results are compared with the results of the FR to find the efficiency of the NR metric. Therefore, it can contribute that EFD metric could be used in image quality assessment, and the HSV color space is an appropriate color space for this NR metric.
Blurring, EFD, No Reference, HSV, Gaussian Blurring, Quality Assessment, IQA, Blurred Images
[01] Y. Ke, X. Tang, and F. Jing, “The design of high-level features for photo quality assessment”, in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2006, vol. 1, pp. 419–426.
[02] C. Li and T. Chen, “Aesthetic visual quality assessment of paintings”, IEEE J. Sel. Topics Signal Process, vol. 3, no.2, pp. 236–252, Apr. 2009.
[03] Z. Wang and A. C. Bovik, "Modern Image Quality Assessment", San Rafael, CA: Morgan & Claypool, 2006.
[04] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity”, IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004.
[05] H. R. Sheikh, A. C. Bovik, and G. de Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics”, IEEE Trans. Image Process., vol. 14, no. 12, pp. 2117–2128, Dec. 2005.
[06] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms”, IEEE Trans. Image Process, vol. 15, no. 11, pp. 3440–3451, Nov. 2006.
[07] H. R. Sheikh and A. C. Bovik, “Image information and visual quality”, IEEE Trans. Image Process., vol. 15, no. 2, pp. 430–444, Feb. 2006.
[08] C. Li and A. C. Bovik, “Content-partitioned structural similarity index for image quality assessment”, Signal Process. Image Commun., vol. 25, no. 7, pp. 517–526, Aug. 2010.
[09] X. Gao, W. Lu, D. Tao, and X. Li, “Image quality assessment based on multiscale geometric analysis”, IEEE Trans. Image Process., vol. 18, no. 7, pp. 1409–1423, Jul. 2009.
[10] D. Tao, X. Li, W. Lu, and X. Gao, “Reduced-reference IQA in contourlet domain”, IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 6, pp. 1623–1627, Dec. 2009.
[11] Z. Wang, H. R. Sheikh, and A. C. Bovik, “No-reference perceptual quality assessment of JPEG compressed images”, in Proc. IEEE Int. Conf. Image Process., Rochester, NY, Sep. 2002, vol. 1, pp. I-477–I-480.
[12] P. Gastaldo and R. Zunino, “Neural networks for the no-reference assessment of perceived quality”, J. Electron. Image, vol. 14, no. 3, p. 033004, Aug. 2005.
[13] T. Brandão and M. P. Queluz, “No-reference image quality assessment based on DCT domain statistics”, Signal Process, vol. 88, no. 4, pp. 822–833, Apr. 2008.
[14] P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, “Perceptual blur and ringing metrics: Application to JPEG2000”, Signal Process. Image Commun., vol. 19, no. 2, pp. 163–172, Feb. 2004.
[15] 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, pp. 1918–1927, Nov. 2005.
[16] Haim Levkowitz, “Color theory and modeling for computer graphics, visualization, and multimedia applications”, Kluwer Academic Publishers, 1997.
[17] Bourne, “Fundamentals of Digital Imaging in Medicine", Springer, 2010.
[18] Marc Ebner, "Color Constancy", John Wiley & Sons, 2007.
[19] A. M. Eskicioglu and P. S. Fisher, “Image quality measures and their performance", IEEE Trans. Communication, vol. 43, pp. 2959–2965, Dec. 1995.
[20] Z. Wang and A. C. Bovik, “A universal image quality index", IEEE Signal Processing Letters, vol. 9, pp. 81–84, Mar. 2002.
[21] Z. Wang, P. Simoncelli, "Local Phase Coherence and the Perception of Blur in: Adv. Neural Information Processing Systems", pp. 786-792. 2003
[22] D. J. Jabson, Z. Rahman, G. A. Woodell, “Retinex processing for automatic image enhancement", Journal of Electronic Imaging, Vol. 13(1), PP.100–110, January 2004.
[23] 1. Rafael C. Gonzales, Richard E. Woods, "Digital Image Processing”, second edition, Prentice Hall, 2002.
[24] Yusra A. Y. Al-Najjar, Dr. Der Chen Soong, "Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI", International Journal of Scientific & Engineering Research, Volume 3, Issue 8, August-2012 1 ISSN 2229-5518.
[25] Z. Wang, and A. C. Bovik, “Why is Image Quality Assessment So Difficult?”, IEEE ICASSP, 02 International Conference on Acoustics, Speech and Signal Processing, Orlando, Florida, USA, pp. 3313-3316, 2002.
[26] Y. Horita, T. Miyata, P. I. Gunawan, T. Murai, and M. Ghanbari, "Evaluation Model Considering Static-temporal Quality Degradation and Human Memory for SSCQE Video Quality”, in Proc. SPIE, Lugano, Switzerland, PP. 1601-1611, 2003.
[27] X. Li, “Blind image quality assessment in Image Processing Proceedings”, International Conference on, vol. 1, pp. I–449, 2002
[28] T. T. Nguyen, X. D. Pham, D. Kim and J. W. Jeon, “Automatic Exposure Compensation for Line Detection Applications”, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems Seoul, Korea, 2008.
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