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A Bi-Histogram Shifting Contrast Enhancement for Color Images

Lord Amoah1,2,*, Ampofo Twumasi Kwabena3

1 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing, 210044, China
2 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China
3 Department of Computer Science, University of Ghana-Legon, Accra, 00233, Ghana

* Corresponding Author: Lord Amoah. Email:

Journal of Quantum Computing 2021, 3(2), 65-77.


Recent contrast enhancement (CE) methods, with a few exceptions, predominantly focus on enhancing gray-scale images. This paper proposes a bihistogram shifting contrast enhancement for color images based on the RGB (red, green, and blue) color model. The proposed method selects the two highest bins and two lowest bins from the image histogram, performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images. The proposed method simultaneously performs both right histogram shifting (RHS) and left histogram shifting (LHS) in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization (HE). The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images. Compared to an existing contrast enhancement method for color images and evaluated with PSNR, SSIM, RCE, and RMBE quality assessment metrics, the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram. The proposed method achieves higher embedding capacities and embedding rates in all images, with an average increase in embedding capacity of 52.1%.


Cite This Article

L. Amoah and A. Twumasi Kwabena, "A bi-histogram shifting contrast enhancement for color images," Journal of Quantum Computing, vol. 3, no.2, pp. 65–77, 2021.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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