An Insight into Pixel Value Ordering Prediction Based Prediction-error Expansion

Abstract

As the core of prediction-error expansion technique, prediction method has a fundamental impact on performance of reversible data hiding. Pixel value ordering (PVO) prediction has been extensively investigated for its high accuracy. However, the correlation of pixels within block has not been fully exploited yet. In this paper, a novel prediction method is proposed by developing PVO prediction in the aspects of spatial correlation and correlated pixel pair. The key to PVO embedding is invariant pixel value order such that the predicted pixel can be identified by value. Instead of predicting and enlarging the largest pixel, we propose to predict and reduce the second largest one and even all others. As the largest pixel which serves as predicted value is maintained after embedding, the numerous predicted pixels can be identified and thus reversibility is guaranteed. IPVO prediction which location-dependently determines the predicted pixel is also developed. For further optimization, multi-pass IPVO embedding is extended from single-layered to double-layered such that full-enclosing pixels can be used to estimate pixel distribution and determine the optimal mode of defining spatial location. Finally, an enhanced pairwise PEE is incorporated with multi-pass IPVO for performance enhancement. Experimental results show that the proposed scheme not only outperforms PVO embedding and its miscellaneous extensions, but also achieves significant superiority in fidelity over a series of state-of-the-art schemes.