High-Capacity Reversible Data Hiding in Encrypted Images With Edge-Directed Prediction and Adaptive Entropy Coding

As cloud services rapidly evolve and the demand for privacy protection grows, reversible data hiding in encrypted images (RDHEI) has gained significant attention. To enhance the data embedding capacity of RDHEI, this paper proposes an adaptive prediction-error entropy encoding framework that dynamically allocates edge-directed prediction (EDP) errors to either separate or grouped entropy coding modes, thereby optimizing net payload capacity. The image owner first predicts the pixel values of the cover image using EDP algorithm and calculates the prediction errors. After computing the prediction errors, the optimal thresholds are adaptively determined by minimizing the expected codeword length through entropy coding theory. Using these optimized thresholds, the prediction errors are classified into separate or grouped encoding categories, and losslessly compressed via arithmetic encoding. Through the processes of image encryption and self-embedding, an encrypted image with embedding room is generated and subsequently uploaded to the cloud server. The data hider can easily locate the data embedding room in the encrypted domain of the image and embed encrypted additional data to obtain the marked encrypted image. The authorized recipient can correctly extract the embedded data, restore the original plaintext image without any loss, or do both. The experimental results demonstrate the effectiveness of the proposed approach, surpassing many state-of-the-art RDHEI techniques.