Reversible Data Hiding Using a Piecewise Autoregressive Predictor Based on Two-stage Embedding
- Authors
- Lee, Byeong Yong; Hwang, Hee Joon; Kim, Hyoung Joong
- Issue Date
- 7월-2016
- Publisher
- SPRINGER SINGAPORE PTE LTD
- Keywords
- Context prediction; Least-squared-based method; Minimum description length; Piecewise auto-regression; Prediction-error expansion; Reversible data hiding
- Citation
- JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, v.11, no.4, pp.974 - 986
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
- Volume
- 11
- Number
- 4
- Start Page
- 974
- End Page
- 986
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/88165
- DOI
- 10.5370/JEET.2016.11.4.974
- ISSN
- 1975-0102
- 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.
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