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http://dx.doi.org/10.5370/JEET.2016.11.4.974

Reversible Data Hiding Using a Piecewise Autoregressive Predictor Based on Two-stage Embedding  

Lee, Byeong Yong (Graduate School of Information Security, Korea University)
Hwang, Hee Joon (Graduate School of Information Security, Korea University)
Kim, Hyoung Joong (Graduate School of Information Security, Korea University)
Publication Information
Journal of Electrical Engineering and Technology / v.11, no.4, 2016 , pp. 974-986 More about this Journal
Abstract
Reversible image watermarking, a type of digital data hiding, is capable of recovering the original image and extracting the hidden message with precision. A number of reversible algorithms have been proposed to achieve a high embedding capacity and a low distortion. While numerous algorithms for the achievement of a favorable performance regarding a small embedding capacity exist, the main goal of this paper is the achievement of a more favorable performance regarding a larger embedding capacity and a lower distortion. This paper therefore proposes a reversible data hiding algorithm for which a novel piecewise 2D auto-regression (P2AR) predictor that is based on a rhombus-embedding scheme is used. In addition, a minimum description length (MDL) approach is applied to remove the outlier pixels from a training set so that the effect of a multiple linear regression can be maximized. The experiment results demonstrate that the performance of the proposed method is superior to those of previous methods.
Keywords
Context prediction; Least-squared-based method; Minimum description length; Piecewise auto-regression; Prediction-error expansion; Reversible data hiding;
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Times Cited By KSCI : 3  (Citation Analysis)
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