High-Fidelity Reversible Data Hiding Using Directionally Enclosed Prediction

Recently, a number of high-fidelity reversible data hiding algorithms have been developed based on prediction-error expansion (PEE) and pixel sorting. In PEE, prediction is made using either a full-enclosed or a half-enclosed predictor. While in PEE with pixel sorting, the local complexity (LC), which is usually assumed to be proportional to the magnitude of prediction-error (PE), is exploited to reduce the embedding distortion. However, this assumption may not always hold in all conditions. In this letter, a directional enclosed predictor is proposed to detect the locations where LC is not proportional to PE. And, a directionally enclosed prediction and expansion (DEPE) scheme is then developed for efficient reversible data hiding. With DEPE, data embedding is restricted to pixels where LC correlates to PE with a proportional relationship. Experimental results show that, compared to the full-enclosed or half-enclosed prediction schemes, DEPE significantly improves the image fidelity while providing a considerable payload.