篇名 | Guest Editorial Special Issue on International Conference on Information Science and Technology |
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卷期 | 14:2 |
作者 | Hong-Hai Liu 、 Zhi-Gang Zeng 、 Jun Wang |
頁次 | 244-256 |
關鍵字 | Reversible data hiding 、 watermarking 、 autoregression 、 histogram 、 prediction 、 EI 、 SCI 、 SCIE 、 Scopus |
出刊日期 | 201206 |
Reversible data hiding embeds information in a host media in a visually plausible way such that both the embedded message and the original host media can be exactly recovered. In this paper we present a new reversible data hiding framework based on prediction error histogram modification. This framework is general and flexible that includes some of the state-of-the-art methods as special cases. In addition, we propose a new adaptive prediction method using the autoregression model. In this method, a threshold is adjusted for each image to divide all pixels into two regions: the smooth region and the texture region. Then the proposed method optimally estimates the coefficients of the autoregression model for pixel value prediction through least-squares minimization. Experimental results show that the proposed reversible data hiding framework and the adaptive prediction algorithm offer valuable advantages over state-of-the-art methods in general.