Reversibility improved data hiding in 3D mesh models using prediction-error expansion and sorting

Abstract Reversible data hiding (RDH) recover the original media without any distortion after extraction of the hidden data. Since massive three-dimensional (3D) mesh models have been constructed from high-dimensional image data nowadays, existing deficiencies of prior studies on RDH of 3D mesh models need to be overcome for performance improvement. In this paper, we propose an improved RDH method for 3D mesh models based on prediction-error expansion and sorting. Instead of considering only several adjacent neighbors, the proposed method predict a vertex position with a ring pattern for better prediction accuracy. To make better capacity-distortion control, data bits are reversibly embedded into 3D mesh models with embedding operations including expansion, shifting and LSB replacement. Moreover, we propose smoothness sorting and twice-layered strategy to enhance the embedding performance further. Thus, the proposed method achieves improved embedding performance in terms of capacity enhancement and distortion reduction. Experimental results demonstrate that the proposed method produces relatively superior results compared with previous related state-of-the-art works.