TY - JOUR
T1 - Reversible data hiding using a piecewise autoregressive predictor based on two-stage embedding
AU - Lee, Byeong Yong
AU - Hwang, Hee Joon
AU - Kim, Hyoung Joong
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2015R1A2A2A01004587).
Publisher Copyright:
© The Korean Institute of Electrical Engineers.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/7
Y1 - 2016/7
N2 - 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.
AB - 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.
KW - Context prediction
KW - Least-squared-based method
KW - Minimum description length
KW - Piecewise auto-regression
KW - Prediction-error expansion
KW - Reversible data hiding
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U2 - 10.5370/JEET.2016.11.4.974
DO - 10.5370/JEET.2016.11.4.974
M3 - Article
AN - SCOPUS:84974801224
VL - 11
SP - 974
EP - 986
JO - Journal of Electrical Engineering and Technology
JF - Journal of Electrical Engineering and Technology
SN - 1975-0102
IS - 4
ER -