A Novel High-Fidelity Reversible Data Hiding Method Based on Adaptive Multi-pass Embedding

In reversible data hiding, prediction error generation plays a crucial role, with pixel value ordering (PVO) standing out as a prediction method that achieves high fidelity. However, conventional PVO approaches select predicted pixels and their predictions independently, failing to fully exploit the inherent redundancy in ordered pixel sequences. This paper proposes a novel PVO-based prediction method that leverages the continuity and spatial correlation of ordering pixels. We first introduce a new prediction technique that exploits the redundancy of consecutive pixels. Our approach selects the most appropriate prediction method from preset prediction errors, considering both pixel position and value characteristics. Furthermore, we implement an adaptive strategy that dynamically selects multiple iteration parameters based on pixel content to obtain more expandable prediction errors and adjusts the modification of prediction errors accordingly. Unlike traditional fixed-parameter methods, our approach better utilizes the inherent structure and redundancy of image pixels, thereby improving data embedding efficiency while minimizing image distortion. We enhance performance by combining pairwise prediction-error expansion with content-based prediction error analysis. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art solutions in terms of image fidelity while maintaining competitive embedding capacity, confirming the effectiveness of our method for efficient data embedding and image recovery.