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 predi...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.展开更多
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)+2 种基金the National High Technology Research and Development Program (Grant No.2007AA01Z175)PCSIRT (Grant No.IRT0707)Specialized Research Foundation of BJTU
文摘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.
