NDLI: A Study of Content based Watermarking using an Advanced HVS Model

Content Provider	IEEE Xplore Digital Library
Author	Autrusseau, F. Le Callet, P. Ninassi, A.
Copyright Year	2007
Description	Author affiliation: IRCCyN Lab., Nantes (Autrusseau, F.; Le Callet, P.; Ninassi, A.)
Abstract	A key point of image watermarking schemes is to select the image pixels or transformed coefficients where an embedded watermark would reach the optimal invisibility versus robustness trade-off. Human visual system (HVS) based JND masks are thus useful to optimally adapt the watermark strength right below the visibility threshold. Most of the perceptual masks created so far for watermarking purpose are based on the empirical assumption that edges and/or textures allow the best watermark visual masking and thus ensures good invisibility properties. Nevertheless, although invisibility control in smooth areas is challenging, it could be interesting to extend the watermark in such areas since the robustness performances might increase. The goal of this paper is to use an advanced HVS model to manage invisibility in order to evaluate the robustness of watermarks embedded in smooth areas only, edges/textured areas only or in both areas. This study is further to the one presented in (F. Autrusseau and P. Le Callet, 2007). The robustness was tested against 89 attacked images (using Stirmark), results showed that including smooth areas in the JND mask increases the robustness while preserving good invisibility properties.
Starting Page	485
Ending Page	488
File Size	697717
Page Count	4
File Format	PDF
ISBN	9780769529941
DOI	10.1109/IIH-MSP.2007.63
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-26
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robust control Visual system Humans Transform coding Watermarking Robustness Discrete wavelet transforms Discrete cosine transforms Pixel Testing
Content Type	Text
Resource Type	Article

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