Convolutional Neural Network Prediction Error Algorithm Based on Block Classification Enhanced

Reversible data hiding techniques can effectively solve the information security problem, and One crucial approach to enhance the level of reversible data hiding is to predict images with higher accuracy, thereby effectively increasing the embedding capacity. However, conventional prediction methods encounter limitations in fully exploiting global pixel correlation. In this study, we propose a novel framework that combines image splitting and convolutional neural network (CNN) techniques. Specifically, grayscale images are divided into non-overlapping blocks and categorized into texture and smoothing groups based on mean square error calculations for each block. This strategy not only improves operational efficiency but also enhances prediction accuracy. Additionally, we leverage the multi-sensory field and global optimization capability of CNNs for image prediction. By assigning image blocks to specific predictors targeting either texture or smoothing groups according to their respective categories, more precise predicted images can be obtained. The image predictor is trained using 3000 randomly selected images from ImageNet. The experimental results show that the proposed method can predict images accurately and improve the prediction performance more effectively. Compared with other methods, the overall performance of the method is higher.