Gaussian Mixture Model Based Contrast Enhancement with the Reversible Data Hiding (RDH) Algorithm

In this paper, a reversible data hiding (RDH) algorithm with contrast enhancement is proposed for images. Here, to improve the image quality we need a contrast enhancement; the proposed algorithm for contrast enhancement is called Gaussian Mixture Model based Contrast Enhancement (GMMCE), which enhances the contrast of a image to improve its visual quality. It brings into play the Gaussian mixture modeling of histograms to model the content of the images. From the histogram, the highest two bins are selected for data embedding hence histogram equalization can be performed by repeating the process. The side data is inserted alongside the message bits into the host image with the goal that the original image is totally recoverable. Based on this, that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. Contrasted with the current histogram-based strategies, the experimental results demonstrates that the nature of GMMCE improved pictures are for the most part steady and beat other benchmark techniques.