Adaptive dynamic prediction mechanism and heuristic algorithm based fast threshold selection for reversible data hiding

Abstract

The Prediction Error Expansion (PEE) framework has been extensively studied in the field of Reversible Data Hiding (RDH). Prediction-Error Value Ordering (PEVO), based on the PEE framework significantly optimizes the embedding performance by exploiting the correlation between prediction errors. Nevertheless, this scheme merely exploits the correlation between multiple maximum/minimum prediction errors within a block, and fails to adequately consider the case of large fluctuations in prediction errors. Therefore, to further exploit the redundancy in texture images, a novel median prediction scheme is proposed in this paper. It utilizes the median value to calculate multiple values on both sides, and this scheme is more accurate for correlation analysis of fluctuating prediction errors. To adapt to various texture images, an adaptive prediction mechanism is proposed, which combines the median prediction and PEVO approaches to generate more embeddable prediction error pairs. Unlike PEVO that employs fixed prediction values for all blocks, the proposed scheme dynamically selects the appropriate prediction scheme for each block based on inter-correlation between target value and its neighboring pixels. Moreover, instead of traversing all possible candidates for block noise level thresholds to determine optimal number of error pairs to be embedded, a heuristic-based algorithm is proposed that quickly determines noise level thresholds using an objective function based on the embeddable prediction error proportion. Finally, experimental results show that the proposed scheme outperforms state-of-the-art works. For example, when the capacity is 20,000 bits, the Boat image achieves a PSNR of 55.52 dB, showing a gain of 0.24 dB compared to the best results in the literature.