Reversible Data Hiding Scheme Based on VQ Prediction and Adaptive Parametric Binary Tree Labeling for Encrypted Images

In this paper, we propose a reversible data hiding scheme for the encrypted images (RDHEI) based on vector quantization (VQ) prediction and parametric binary tree labeling (PBTL). VQ compression is a lossy image compression method, the difference between the original image and the decompressed image is small when the length of codebook is sufficient. Thus, VQ can be applied as a tool for pixel value prediction. Based on VQ prediction, PBTL method is applied to label the embeddable and non-embeddable pixels. Through adaptive setting of parameters, the modified PBTL can provide optimal pixel labeling strategies and thus maximize the overall embedding capacity. Furthermore, the VQ index and the secret data are stream ciphered to avoid leakage of the image content and secret information. Different metrics are used to show that the marked encrypted images are highly secure. In comparison with several state-of-the-art schemes, our scheme outperforms the related works in embedding rate for two commonly applied image databases. In addition, extraction of the secret data and recovery of the original image can be operated separately according to authorization.