Reversible data hiding based on combined predictor and prediction error expansion

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

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)254-265
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9023
DOIs
Publication statusPublished - 2015

Fingerprint

Data Hiding
Prediction Error
Predictors
Pixels
Pixel
Textures
Patch
Texture
Verify
Prediction
Experiments
Experiment

Keywords

  • Combined predictor
  • Pixel selection
  • Prediction error expansion
  • Reversible data hiding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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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.",
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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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