Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques

H. T. R. Kurmasha; A. F. H. Alharan; C. S. Der; N. H. Azami

doi:10.48084/etasr.1625

Authors

H. T. R. Kurmasha Computer Department, College of Education for Girls, Kufa University, Najaf, Iraq
A. F. H. Alharan Computer Department, College of Education for Girls, Kufa University, Najaf, Iraq
C. S. Der Department of Graphics and Multimedia, College of Information Technology, University Tenaga Nasional, Selangor D.E., Malaysia
N. H. Azami Department of Graphics and Multimedia, College of Information Technology, University Tenaga Nasional, Selangor D.E., Malaysia

DOI:

https://doi.org/10.48084/etasr.1625

Keywords:

Histogram Equalization, Contrast enhancement, Image Quality measures, Distortions, Human Visual perception

Abstract

An Edge-based image quality measure (IQM) technique for the assessment of histogram equalization (HE)-based contrast enhancement techniques has been proposed that outperforms the Absolute Mean Brightness Error (AMBE) and Entropy which are the most commonly used IQMs to evaluate Histogram Equalization based techniques, and also the two prominent fidelity-based IQMs which are Multi-Scale Structural Similarity (MSSIM) and Information Fidelity Criterion-based (IFC) measures. The statistical evaluation results show that the Edge-based IQM, which was designed for detecting noise artifacts distortion, has a Person Correlation Coefficient (PCC) > 0.86 while the others have poor or fair correlation to human opinion, considering the Human Visual Perception (HVP). Based on HVP, this paper propose an enhancement to classic Edge-based IQM by taking into account the brightness saturation distortion which is the most prominent distortion in HE-based contrast enhancement techniques. It is tested and found to have significantly well correlation (PCC > 0.87, Spearman rank order correlation coefficient (SROCC) > 0.92, Root Mean Squared Error (RMSE) < 0.1054, and Outlier Ratio (OR) = 0%).

References

Y.-T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization”, IEEE Transactions on Consumer Electronics, Vol. 43, No.1, pp.1-8, 1997

K. Wongsritong, K. Kittayaruasiriwat, F. Cheevasuvit, K. Dejhan, A. Somboonkaew, “Contrast enhancement using multipeak histogram equalization with brightness preserving”, IEEE Asia-Pacific Conference on Circuits and Systems, pp. 455-458, 1998

Y. Wang, Q. Chen, B. Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method”, IEEE Transactions on Consumer Electronics, Vol. 45, No. 1, pp. 68-75, 1999

S. D. Chen, A. Rahman Ramli, “Preserving brightness in histogram equalization based contrast enhancement techniques”, Digital Signal Processing, Vol. 14, No. 5, pp. 413-428, 2004

C. Wang, Z. Ye, “Brightness preserving histogram equalization with maximum entropy: a variational perspective”, IEEE Transactions on Consumer Electronics, Vol. 51, No. 4, pp. 1326-1334, 2005

H. Ibrahim, N. S. Pik Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement”, IEEE Transactions on Consumer Electronics, Vol. 53, No. 4, pp. 1752-1758, 2007

D. Menotti, L. Najman, J. Facon, A. De A. Araujo, “Multi-histogram equalization methods for contrast enhancement and brightness preserving”, IEEE Transactions on Consumer Electronics , Vol. 53, No. 3, pp. 1186–1194, 2007

M. Kim, M. Gyo Chung, “Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement”, IEEE Transactions on Consumer Electronis, Vol. 54, No. 3, pp. 1389-1397, 2008

C. H. Ooi, N. S .Pik Kong, H. Ibrahim, “Bi-histogram equalization with a plateau limit for digital image enhancement, IEEE Transactions on Consumer Electronis, Vol. 55, No. 4, pp. 2072–2080, 2009

C. H. Ooi, N. A. M. Isa, “Adaptive contrast enhancement methods with brightness preserving, IEEE Transactions on Consumer Electronis”, Vol. 56, No. 4, pp. 2543–2551, 2010

S.-H. Yun, J. H. Kim, S. Kim, “Image enhancement using a fusion framework of histogram equalization and Laplacian pyramid”, IEEE Transactions on Consumer Electronis, Vol. 56, No. 4, pp. 2763–2771, 2010

N. Sengee, A. Sengee, H. K. Choi, “Image contrast enhancement using bi-histogram equalization with neighborhood metrics”, IEEE Transactions on Consumer Electronics, Vol. 56, No. 4, pp. 2727-2734, 2010

S. D. Chen, “A new image quality measure for assessment of histogram equalization-based contrast enhancement techniques”, Digital Signal Processing, Vol. 22, No. 4, pp. 640-647, 2012

S. D. Chen, Y. Al-Najja,N. H. Azami, K. S. Beh, “Measuring Image Quality for Assessment of Contrast Enhancement Techniques”, Australian Journal of Basic and Applied Sciences, Vol. 7, No. 11, pp. 178-188, 2013

Rich Franzen, Kodak lossless true color image suite, http://r0k.us/graphics/kodak/

H. R. Sheikh, Z. Wang, L. Cormack, A. C. Bovik, LIVE image quality assessment database release 2, http://live.ece.utexas.edu/research/

quality

S. D. Chen, M. Singh, “Re-evaluation of automatic global histogram equalization-based contrast enhancement methods”, Electronic Journal of Computer Science and Information Technology, Vol. 1, No.1, 2011

VQEG, VQEG final report of FR-TV phase II validation test, 2003

Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, “Image quality assessment:from error visibility to structural similarity”, IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612, 2004

H. R. Sheikh, A. C. Bovik, G. de Veciana, “An information fidelity criterion for image quality assessment using natural scene statistics”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2117-2128, 2005

I. Avcibas, B. Sankur, K. Sayood, “Statistical evaluation of image quality measures”,Journal of Electronic Imaging, Vol. 11, No. 2, pp. 206-223, 2002

H. Arsham, Statistical thinking for managerial decisions, Available at: http://home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm, 2014