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

Quanitization index modulation (QIM) is a computationally efficient method of watermarking with side information. This paper proposes two improvements to the original algorithm. First, the fixed quantization step size is replaced with an adaptive step size that is determined using Watson's perceptual model. Experimental results on a database of 1000 images illustrate significant improvements in both fidelity and robustness to additive white Gaussian noise. Second, modifying the Watson model such that it scales linearly with valumetric (amplitude) scaling, results in a QIM algorithm that is invariant to valumetric scaling. Experimental results compare this algorithm with both the original QIM and an adaptive QIM and demonstrate superior performance.