Using Wavelets and Independent Component Analysis for Quantization Index Modulation Watermarking
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Hyperbolic RDM for nonlinear valumetric distortions
IEEE Transactions on Information Forensics and Security
Watermarking robustness evaluation based on perceptual quality via genetic algorithms
IEEE Transactions on Information Forensics and Security
Comparison of perceptual shaping techniques for digital image watermarking
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Comparison of watermarking algorithms via a GA-based benchmarking tool
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Scalable and credible video watermarking towards scalable video coding
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Perceptually adaptive spread transform image watermarking scheme using Hadamard transform
Information Sciences: an International Journal
A video watermarking technique based on pseudo-3-D DCT and quantization index modulation
IEEE Transactions on Information Forensics and Security
Disparity guided exhibition watermarking for 3D stereo images
Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies
Content-adaptive reliable robust lossless data embedding
Neurocomputing
KL-sense secure image steganography
International Journal of Security and Networks
A highly robust two-stage Contourlet-based digital image watermarking method
Image Communication
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Traditional quantization index modulation (QIM) methods are based on a fixed quantization step size, which may lead to poor fidelity in some areas of the content. A more serious limitation of the original QIM algorithm is its sensitivity to valumetric changes (e.g., changes in amplitude). In this paper, 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, 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 traditional 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