Compared to traditional images and 2D videos, 3D videos are more likely to cause distortion drift problems in data hiding. In this paper, an improved scheme for high capacity and efficient data hiding in 3D videos based on Multi-view coding (MVC) standard is proposed, which avoids the problems of distortion drift. To improve the visual quality of data hiding, two selection modes are provided to limit the distortion drift. By modifying the selected coefficients of 4×4 quantized discrete cosine transforms (QDCT) in macroblocks and hiding data into b4-frames, the proposed scheme will effectively prevent distortion drift which caused by intra prediction and inter prediction. Moreover, the proposed algorithm maintains great randomness by using two random sequences. Several benchmark 3D video sequences of different resolutions and diverse contents are used for experimental evaluation. It is experimentally proved that the algorithm has greater embedding capacity and higher efficient than the previous methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.