Using perceptual models to improve fidelity and provide resistance to valumetric scaling for quantization index modulation watermarking

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.