A novel reversible ternary embedding algorithm based on modified full context prediction errors

Li Li, Chin Chen Chang, K. Bharanitharan, Yanjun Liu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    We propose a high capacity reversible ternary embedding-watermarking algorithm based on a modification of full-context-prediction-errors (MFCPE) wherein the binary bit stream is converted to the ternary stream then error histogram shifting is utilized to embed the ternary stream. Unlike the existing predictor methods, we provide a full context prediction with a modification of each pixel at most by 1, which significantly reduces distortion. Experimental results confirm that the proposed algorithm achieves high PSNR while providing a higher embedding capacity. Also, results indicate that MFCPE outperforms the existing methods in terms of payload and the watermarked image quality.

    Original languageEnglish
    Title of host publication2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages529-533
    Number of pages5
    ISBN (Electronic)9781509023769
    DOIs
    Publication statusPublished - 27 Mar 2017
    Event2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016 - Beijing, China
    Duration: 13 Aug 201615 Aug 2016

    Publication series

    Name2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016

    Conference

    Conference2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
    Country/TerritoryChina
    CityBeijing
    Period13/08/1615/08/16

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