Quantized based image watermarking in an independent domain

Abstract : This paper presents a performance study for two well known quantized based watermarking schemes Scalar Costa Scheme (SCS), and Trellis Coded Quantization scheme (TCQ) in an independent domain. For our study, the independent domain is obtained by the combination of the cited schemes with Independent Component Analysis (ICA). The Independent Component Analysis is used while inserting and extracting the message. Thus a performance compromise improvement of the original SCS and TCQ schemes in terms of robustness, capacity, and security is shown. Then the obtained schemes performances are compared to the Spread Transform (ST) based scheme well known for its proven good robustness properties. Our results show that, using watermarking with side information in independent domain permits to improve the global SCS and TCQ schemes performances. For example, in the case of SCS, by studying the Bit Error Rate (BER) in function of the watermark to noise ratio (WNR), the robustness has been improved by 20 dB (1 decade) when WNR is equal to 0 dB. Moreover, the capacity study shows for the same WNR=0 dB, the amount of information to be transmitted without error for a given noise level of the proposed SCS combined with ICA scheme (SCS–ICA) is much higher than that of the existing schemes. Finally, the study of the probability density functions (PDF) of the original and marked signals has shown that the Cachin-security level of the TCQ in an independent domain scheme (TCQ–ICA) is the best one compared to the other known schemes.
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Article dans une revue
Signal Processing: Image Communication, Elsevier, 2011
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https://hal.archives-ouvertes.fr/hal-00575991
Contributeur : Remy Boyer <>
Soumis le : vendredi 11 mars 2011 - 15:59:39
Dernière modification le : mercredi 10 octobre 2018 - 16:10:39

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  • HAL Id : hal-00575991, version 1

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Ilhem Benkara Mostefa, Sofiane Braci, Claude Delpha, Remy Boyer, Mohammed Khamadja. Quantized based image watermarking in an independent domain. Signal Processing: Image Communication, Elsevier, 2011. 〈hal-00575991〉

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