Optimizing Non-Local Pixel Predictors for Reversible Data Hiding
Weiming Zhang and
Additional contact information
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
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.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijdcf.2014070101 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:jdcf00:v:6:y:2014:i:3:p:1-15
Access Statistics for this article
More articles in International Journal of Digital Crime and Forensics (IJDCF) from IGI Global
Bibliographic data for series maintained by Journal Editor ().