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

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
Volume: 7 | Issue: 6 | Pages: 2277-2281 | December 2017 | https://doi.org/10.48084/etasr.1625
Corresponding author: H. T. R. Kurmasha

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%).

Keywords:

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

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[1]
Kurmasha, H.T.R., Alharan, A.F.H., Der, C.S. and Azami, N.H. 2017. Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques. Engineering, Technology & Applied Science Research. 7, 6 (Dec. 2017), 2277–2281. DOI:https://doi.org/10.48084/etasr.1625.

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