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The Design and Application of DWT-Domain Optimum Decoders

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Digital Watermarking (IWDW 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2613))

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Abstract

Based on Bayes theory of hypothesis testing, a new DWT-domain decoder structure for image watermarking has been proposed in this work. The statistical distribution of wavelet coefficients is deliberately described with the Laplacian model so that the decoding algorithm could couple effectiveness and simplicity. Under the Neyman-Pearson criterion, the decision rule is optimized by minimizing the probability of missing the watermark for a given false detection rate. Compared with other domain decoders, the proposed DWT-domain decoder has more flexibility in constructing new watermarking algorithms by using visual models that have varying spatial support.

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References

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, PRC

    Yongjian Hu, Sam Kwong & Y. K. Chan

  2. Dept. of Automatic Control Engg., South China University of Tech., 510641, Guangzhou, PRC

    Yongjian Hu

  3. School of Info. Sci. and Tech., Sun Yat-Sen University, 510275, Guangzhou, PRC

    Yongjian Hu

Authors
  1. Yongjian Hu
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  2. Sam Kwong
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  3. Y. K. Chan
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Editor information

Editors and Affiliations

  1. Microsoft Research, 7 J. J. Thomson Avenue, CB3 0FB, Cambridge, UK

    Hyoung Joong Kim

  2. Department of Control and Instrumentation Engineering, Kangwon National University, 200-701, Chunchon, Korea

    Hyoung Joong Kim

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© 2003 Springer-Verlag Berlin Heidelberg

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Hu, Y., Kwong, S., Chan, Y.K. (2003). The Design and Application of DWT-Domain Optimum Decoders. In: Kim, H.J. (eds) Digital Watermarking. IWDW 2002. Lecture Notes in Computer Science, vol 2613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36617-2_3

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  • DOI: https://doi.org/10.1007/3-540-36617-2_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01217-7

  • Online ISBN: 978-3-540-36617-1

  • eBook Packages: Springer Book Archive

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