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Optimizing Non-Local Pixel Predictors for Reversible Data Hiding

Optimizing Non-Local Pixel Predictors for Reversible Data Hiding

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), and Nenghai Yu (School of Information Science and Technology, University of Science and Technology of China, Hefei, China)
Copyright: © 2014 |Volume: 6 |Issue: 3 |Pages: 15
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781466653498|DOI: 10.4018/ijdcf.2014070101
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MLA

Hu, Xiaocheng, et al. "Optimizing Non-Local Pixel Predictors for Reversible Data Hiding." IJDCF vol.6, no.3 2014: pp.1-15. https://doi.org/10.4018/ijdcf.2014070101

APA

Hu, X., Zhang, W., & Yu, N. (2014). Optimizing Non-Local Pixel Predictors for Reversible Data Hiding. International Journal of Digital Crime and Forensics (IJDCF), 6(3), 1-15. https://doi.org/10.4018/ijdcf.2014070101

Chicago

Hu, Xiaocheng, Weiming Zhang, and Nenghai Yu. "Optimizing Non-Local Pixel Predictors for Reversible Data Hiding," International Journal of Digital Crime and Forensics (IJDCF) 6, no.3: 1-15. https://doi.org/10.4018/ijdcf.2014070101

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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.

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