Prediction-Error Value Ordering for High-Fidelity Reversible Data Hiding

Abstract

Prediction-error expansion (PEE) is the most widely utilized reversible data hiding (RDH) technique. However, the performance of PEE is far from optimal since the correlations among prediction-errors are not fully exploited. Then, to enhance the embedding performance of PEE, a new RDH method named prediction-error value ordering (PEVO) is proposed in this paper. The main idea of PEVO is to exploit the inter-correlations of prediction-errors by combining PEE with the recent RDH technique of pixel value ordering. Specifically, the prediction-errors within an image block are first sorted, and then the maximum and minimum prediction-errors of the block are predicted and modified for data embedding. By the proposed approach, the image redundancy is better exploited and promising embedding performance is achieved. Experimental results demonstrate that the proposed PEVO embedding method is better than the PEE-based ones and some other state-of-the-art works.