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Reversible Data Hiding Using a Piecewise Autoregressive Predictor Based on Two-stage Embedding
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 Title & Authors
Reversible Data Hiding Using a Piecewise Autoregressive Predictor Based on Two-stage Embedding
Lee, Byeong Yong; Hwang, Hee Joon; Kim, Hyoung Joong;
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 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;
 Language
English
 Cited by
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