Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.724606
Title: Using Watson perceptual model to improve quantization index modulation based watermarking schemes
Author: Li, Qiao
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2007
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Abstract:
Quantization index modulation (QIM) is a popular watermarking scheme that has received considerable attention. Nevertheless, there are practical limitations of QIM. For example, traditional QIM uses a fixed quantization step size, which may lead to poor fidelity in some areas of the content. More serious problems of the original QIM algorithm include its extremely sensitivity to valumetric scaling (e.g., changes in amplitude) and re-quantization (e.g., JPEG compression). In this thesis, we first propose using Watson's perceptual model to adaptively select the quantization step size based on the calculated perceptual "slack". Experimental results on 1000 images indicate improvements in fidelity as well as improved robustness in high-noise regimes. Watson's perceptual model is then modified such that the slacks scale linearly with valumetric scaling, thereby providing a QIM algorithm that is theoretically invariant to valumetric scaling. In practice, the robustness against valumetric scaling is significantly improved, but scaling can still result in errors due to cropping and roundoff that are an indirect effect of scaling. Two new algorithms are proposed the first based on regular QIM and the second based on rational dither modulation. A comparison with other methods demonstrates improved performance over other recently proposed valumetric-invariant QIM algorithms, with only small degradations in fidelity. Spread transform dither modulation (STDM) is a form of QIM that is more robust to re-quantization. However, the robustness of STDM to JPEG compression is still poor and it remains very sensitive to valumetric scaling. We describe how a perceptual model can be incorporated into the STDM framework to (i) provide robustness to valumetric scaling, (ii) reduce the embedding-induced perceptual distortion and (iii) significantly improve the robustness to re-quantization.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.724606  DOI: Not available
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