Pixel-based pixel value ordering predictor for high-fidelity reversible data hiding

Xiaochao Qu, Hyoung Joong Kim

Research output: Contribution to journalArticlepeer-review

183 Citations (Scopus)


A pixel-value-ordering (PVO) predictor has been used in reversible data hiding methods to produce high-fidelity marked images when the payload size is moderate. The original PVO generates predictions in a block-by-block manner where only two pixels can be predicted in each block. In this paper, a novel pixel-based PVO (PPVO) is proposed with which each pixel is predicted using its sorted context pixels. The benefit of the PPVO is that the pixels in smooth image regions can be better used to embed data, while most of those pixels are neglected in PVO due to the block constraint. Experimental results demonstrate that the embedding capacity of PPVO is larger than that of PVO, especially for relatively smooth images. The obtained marked image quality of PPVO is also better than that of PVO and other state-of-the-art reversible data hiding methods.

Original languageEnglish
Pages (from-to)249-260
Number of pages12
JournalSignal Processing
Publication statusPublished - 2015 Jun

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MEST) (No. 2012015587 ). This work was supported by the ICT R&D program of MSIP/IITP . [2014 ( I5501-14-1007 ), 3D Smart Media/Augmented Reality Technology, Korea-China-Japan-Russia Cooperation International Standardization]. This research was supported by the MKE (The Ministry of Knowledge Economy), Korea , under the ITRC support program supervised by the NIPA (National IT Industry Promotion Agency) ( NIPA-2010-C1090-1001-0004 ). This research was supported by Korea University .

Publisher Copyright:
© 2015 Elsevier B.V.


  • Pixel selection
  • Pixel value ordering
  • Pixel-based pixel value ordering
  • Reversible data hiding

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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