Data hiding for vector quantization images using mixed-base notation and dissimilar patterns without loss of fidelity

Abstract

Data hiding without loss of fidelity (also called reversible data hiding) allows an original image to be restored completely after extraction of hidden data embedded in a cover image. In this paper, we propose a reversible scheme based on mixed base notation and dissimilar pattern strategies for vector quantization (VQ) compressed images. The dissimilar patterns resulting from the declustering process make the proposed hiding method more efficient. Our experimental results show that the time required for the embedding process in the proposed method is much less than that in the side-matched VQ (SMVQ)-based reversible method. In addition, the use of declustering can reduce many more prediction errors than traditional clustering methods. The results also show that the number of declustered groups and the spatial characteristics of the cover image will affect the embedding capacity and the size of the stego VQ index table.