Reversible data hiding based on combined predictor and prediction error expansion

Xiaochao Qu; Suah Kim; Run Cui; Fangjun Huang; Hyoung Joong Kim

doi:10.1007/978-3-319-19321-2_19

Reversible data hiding based on combined predictor and prediction error expansion

Xiaochao Qu, Suah Kim, Run Cui, Fangjun Huang, Hyoung Joong Kim

School of Cybersecurity

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

This paper presents a novel reversible data hiding method which uses a combined predictor. The proposed combined predictor combines five base predictors according to their global and local predicting performance. The weights to combine the base predictors are calculated with a pixel by pixel manner that they adjust to the local image patch characteristics. The proposed predictor is shown to have high prediction precision which is beneficial for the following prediction error expansion (PEE). Observing that our predictor performs well even for images with complex textures, a novel pixel selection criterion that bases on the prediction errors is proposed, which can accurately select the pixels that have small prediction errors to use. Extensive experiments are conducted to verify the superior performance of the proposed method.

Original language	English
Pages (from-to)	254-265
Number of pages	12
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9023
DOIs	https://doi.org/10.1007/978-3-319-19321-2_19
Publication status	Published - 2015
Event	13th International Workshop on Digital-Forensics and Watermarking , IWDW 2014 - Taipei, Taiwan, Province of China Duration: 2014 Oct 1 → 2014 Oct 4

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012015587), and the National Natural Science Foundation of China (no. 61173147).

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

Combined predictor
Pixel selection
Prediction error expansion
Reversible data hiding

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-19321-2_19

Cite this

Reversible data hiding based on combined predictor and prediction error expansion. / Qu, Xiaochao; Kim, Suah; Cui, Run et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9023, 2015, p. 254-265.

Research output: Contribution to journal › Conference article › peer-review

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title = "Reversible data hiding based on combined predictor and prediction error expansion",

abstract = "This paper presents a novel reversible data hiding method which uses a combined predictor. The proposed combined predictor combines five base predictors according to their global and local predicting performance. The weights to combine the base predictors are calculated with a pixel by pixel manner that they adjust to the local image patch characteristics. The proposed predictor is shown to have high prediction precision which is beneficial for the following prediction error expansion (PEE). Observing that our predictor performs well even for images with complex textures, a novel pixel selection criterion that bases on the prediction errors is proposed, which can accurately select the pixels that have small prediction errors to use. Extensive experiments are conducted to verify the superior performance of the proposed method.",

keywords = "Combined predictor, Pixel selection, Prediction error expansion, Reversible data hiding",

author = "Xiaochao Qu and Suah Kim and Run Cui and Fangjun Huang and Kim, {Hyoung Joong}",

note = "Funding Information: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012015587), and the National Natural Science Foundation of China (no. 61173147). Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th International Workshop on Digital-Forensics and Watermarking , IWDW 2014 ; Conference date: 01-10-2014 Through 04-10-2014",

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doi = "10.1007/978-3-319-19321-2_19",

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AU - Kim, Suah

AU - Cui, Run

AU - Huang, Fangjun

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) (No. 2012015587), and the National Natural Science Foundation of China (no. 61173147). Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

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N2 - This paper presents a novel reversible data hiding method which uses a combined predictor. The proposed combined predictor combines five base predictors according to their global and local predicting performance. The weights to combine the base predictors are calculated with a pixel by pixel manner that they adjust to the local image patch characteristics. The proposed predictor is shown to have high prediction precision which is beneficial for the following prediction error expansion (PEE). Observing that our predictor performs well even for images with complex textures, a novel pixel selection criterion that bases on the prediction errors is proposed, which can accurately select the pixels that have small prediction errors to use. Extensive experiments are conducted to verify the superior performance of the proposed method.

AB - This paper presents a novel reversible data hiding method which uses a combined predictor. The proposed combined predictor combines five base predictors according to their global and local predicting performance. The weights to combine the base predictors are calculated with a pixel by pixel manner that they adjust to the local image patch characteristics. The proposed predictor is shown to have high prediction precision which is beneficial for the following prediction error expansion (PEE). Observing that our predictor performs well even for images with complex textures, a novel pixel selection criterion that bases on the prediction errors is proposed, which can accurately select the pixels that have small prediction errors to use. Extensive experiments are conducted to verify the superior performance of the proposed method.

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KW - Pixel selection

KW - Prediction error expansion

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DO - 10.1007/978-3-319-19321-2_19

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 13th International Workshop on Digital-Forensics and Watermarking , IWDW 2014

Y2 - 1 October 2014 through 4 October 2014

ER -

Reversible data hiding based on combined predictor and prediction error expansion

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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