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.
|Number of pages||12|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|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 noteFunding 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).
© Springer International Publishing Switzerland 2015.
- Combined predictor
- Pixel selection
- Prediction error expansion
- Reversible data hiding
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)