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Optimizing Non-Local Pixel Predictors for Reversible Data Hiding

Xiaocheng Hu, Weiming Zhang and Nenghai Yu
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Xiaocheng Hu: School of Information Science and Technology, University of Science and Technology of China, Hefei, China
Weiming Zhang: School of Information Science and Technology, University of Science and Technology of China, Hefei, China
Nenghai Yu: School of Information Science and Technology, University of Science and Technology of China, Hefei, China

International Journal of Digital Crime and Forensics (IJDCF), 2014, vol. 6, issue 3, 1-15

Abstract: This paper presents a two-step clustering and optimizing pixel prediction method for reversible data hiding, which exploits self-similarities and group structural information of non-local image patches. Pixel predictors play an important role for current prediction-error expansion (PEE) based reversible data hiding schemes. Instead of using a fixed or a content- adaptive predictor for each pixel independently, the authors first employ pixel clustering according to the structural similarities of image patches, and then for all the pixels assigned to each cluster, an optimized pixel predictor is estimated from the group context. Experimental results demonstrate that the proposed method outperforms state-of-art counterparts such as the simple rhombus neighborhood, the median edge detector, and the gradient-adjusted predictor et al.

Date: 2014
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