Lossless data hiding based on prediction-error adjustment  

作　　者：WENG ShaoWei ZHAO Yao NI RongRong PAN Jeng-Shyang 

机构地区：[1]Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China [2]Department of Electronic Engineering, Kaohsiung University of Applied Sciences, Kaohsiung 80778

出　　处：《Science in China(Series F)》2009年第2期269-275,共7页中国科学（F辑英文版）

基　　金：Supported by National Natural Science Foundation of China (Grant Nos.60776794,60702013 and 90604032);the Major State Basic Research Development Progrom of China (Grant No.2006CB303104);the National High Technology Research and Development Program (Grant No.2007AA01Z175);PCSIRT (Grant No.IRT0707);Specialized Research Foundation of BJTU

摘　　要：A novel Iossless data hiding scheme based on a combination of prediction and the prediction-error adjustment （PEA） is presented in this paper. For one pixel, its four surrounding neighboring pixels are used to predict it and 1-bit watermark information is embedded into the prediction-error. In traditional approaches, for the purpose of controlling embedding distortion, only pixels with small predictionerrors are used for embedding. However, when the threshold is small, it is difficult to efficiently compress the location map which is used to identify embedding locations. Thus, PEA is introduced to make large prediction-error available for embedding while causing low embedding distortions, and accordingly, the location map can be compressed well. As a result, the hiding capacity is largely increased. A series of experiments are conducted to verify the effectiveness and advantages of the proposed approach.A novel Iossless data hiding scheme based on a combination of prediction and the prediction-error adjustment （PEA） is presented in this paper. For one pixel, its four surrounding neighboring pixels are used to predict it and 1-bit watermark information is embedded into the prediction-error. In traditional approaches, for the purpose of controlling embedding distortion, only pixels with small predictionerrors are used for embedding. However, when the threshold is small, it is difficult to efficiently compress the location map which is used to identify embedding locations. Thus, PEA is introduced to make large prediction-error available for embedding while causing low embedding distortions, and accordingly, the location map can be compressed well. As a result, the hiding capacity is largely increased. A series of experiments are conducted to verify the effectiveness and advantages of the proposed approach.

关 键 词：reversible watermarking PREDICTION PEA 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]