Reversible Data Hiding With Pixel Prediction and Pixel Value Ordering in Industrial Images

Reversible data hiding is essential for data security and integrity in industrial environments. However, existing methods suffer from unstable prediction errors and limited embedding capacity. This article proposes an adaptive reversible data hiding framework for industrial images using enhanced content pixel prediction error. Initially, a refined content pixel selection method is proposed to accurately identify embeddable pixels in industrial images. Then, a distance-based pixel prediction scheme is developed that utilizes high-relevance content pixels for improved prediction accuracy. In addition, a content-adaptive embedding strategy is introduced that prioritizes smooth blocks while employing prediction error pair mapping to simultaneously maximize embedding capacity and minimize distortion. Experimental results show that the proposed method outperforms state-of-the-art approaches in embedding capacity, achieving an average of 45 625 bits per image with the capability to embed in complex textured regions. Extensive evaluation on standard benchmarks and two industrial image databases validates its effectiveness for secure industrial data transmission.