Reversible Data Hiding based on optimized CNN predictor and Prediction Error Expansion with Lower Surround Background Complexity

Convolutional Neural Network-based Predictors (CNNP) have emerged as a viable solution for enhancing global optimization and prediction capabilities in the field of Reversible Data Hiding (RDH). However, they often encounter limitations in data embedding space due to scarcity of zero-valued points in predicted images. To address these challenges, in this paper, we propose an Optimized CNNP (OCNNP) technique that increases the zero-valued points in predicted images, significantly enhancing the embedding capacity. Additionally, we introduce a novel Lower Surround Background Complexity (LSBC)-based Prediction Error Expansion (PEE) method, which refines the sorting of prediction errors for data embedding, thereby reducing image distortion and improving overall embedding performance. Experimental results demonstrate that our approach markedly improves the Peak Signal-to-Noise Ratio (PSNR) across various embedding capacities compared to existing methods and shows high robustness against various noise types, establishing its superiority in enhancing both the capacity and quality of RDH in complex image distributions.