Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding

TL;DR

This paper introduces a reversible data hiding method using prediction-error of prediction error (PPE) in gray images, leveraging prediction and sorting techniques to improve payload-distortion performance over existing methods.

Contribution

The novel PPE-based RDH algorithm combines prediction, sorting, and histogram shifting to enhance data embedding efficiency and image quality.

Findings

Outperforms state-of-the-art RDH methods in payload-distortion metrics.

Utilizes PPE and local complexity sorting to optimize data embedding.

Achieves higher embedding capacity with minimal distortion.

Abstract

This paper presents a novel reversible data hiding (RDH) algorithm for gray-scaled images, in which the prediction-error of prediction error (PPE) of a pixel is used to carry the secret data. In the proposed method, the pixels to be embedded are firstly predicted with their neighboring pixels to obtain the corresponding prediction errors (PEs). Then, by exploiting the PEs of the neighboring pixels, the prediction of the PEs of the pixels can be determined. And, a sorting technique based on the local complexity of a pixel is used to collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will be processed first for data embedding. By reversibly shifting the PPE histogram (PPEH) with optimized parameters, the pixels corresponding to the altered PPEH bins can be finally modified to carry the secret data. Experimental results have implied that the proposed method can benefit from the prediction procedure of the PEs, sorting technique as well as parameters selection, and therefore outperform some state-of-the-art works in terms of payload-distortion performance when applied to different images.