Multiple Histograms Modification using Prediction-Error Expansion (PEE) for Efficient Reversible Data Hiding (RDH)

Abstract: The best reversible data hiding (RDH) technique is Prediction-error expansion (PEE), and existing PEE-based RDH techniques are primarily based on the modification of one- or two-dimensional prediction-error histogram (PEH). The one-dimensional PEH-based methods do not perform generally as good as those based on two dimensional PEH; in any case, since the PEH modification manner is fixed and independent of image content, their performance is still unsatisfactory. In this framework, new RDH strategy based on PEE for multiple histograms is proposed .In this framework, Unlike the past methods, consider a sequence of histograms and devise a new embedding mechanism based on multiple histograms modification (MHM). For every pixel, as indicated by its context, a complexity measurement is computed and the pixels with a given complexity are gathered together to generate a PEH. By varying the complexity to cover the whole image, a sequence of histograms can be generated. At that point, two expansion bins are selected in each generated histogram and data embedding is acknowledged based on MHM. The expansion bins are adaptively chosen, considering the image content such that the embedding distortion is minimized. The proposed MHM-based RDH strategy works well, with such chosen expansion bins.

Keywords: Reversible data hiding, multiple histograms modification, prediction-error expansion, prediction-error histogram, adaptive embedding.