Spread transform dither modulation (STDM) is a form of quantization index modulation (QIM) that is more robust to re-quantization. However, the robustness of STDM to JPEG compression is still very poor and it remains very sensitive to amplitude scaling. Here, we show how a perceptual model that scales linearly with amplitude scaling can be used to (i) provide robustness to amplitude scaling, (ii) reduce the perceptual distortion at the embedder and (iii) significantly improve the robustness to re-quantization.

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

In previous work, we demonstrated how perceptual modeling can be applied to dither modulated quantization index modulation and rational dither modulation, to improve both robustness and fidelity. These algorithms were shown to be significantly more robust to valumetric scaling. However, they, and their predecessors, remain extremely sensitive to re-quantization which commonly occurs due to JPEG compression, numerical rounding and analog-to-digital conversion. It is well known that spread transform dither modulation (STDM) is more robust to re-quantization. In this paper we describe how to incorporate a perceptual model into this framework and present two algorithms based on Watson's perceptual model. Experimental results of robustness to JPEG compression are reported for 1000 images at embedding rates of 1/32 and 1/320. At the high embedding rate, the robustness of the two algorithms is the same as STDM but the perceptual distortion is reduced 23 to about 4, based on Watson's perceptual distance. At the lower embedding rate, we simultaneously observed superior robustness to STDM as well as improved fidelity. If the perceptual distance rather than the document-to-watermark ratio (DWR) is held fixed, then the two adaptive spread transform methods exhibit significant improvements in robustness to JPEG compression

Quantization 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.

Quantization index modulation (QIM) is a computationally efficient method of informed watermarking. However, the original method is particularly sensitive to variations in the amplitude of the signal. Previously, we proposed using a modification of Watson's perceptual model to adaptively adjust the quantization index step size. This simultaneously improved both the robustness and fidelity of the watermarked image and, most importantly, provided invariance (to a large degree) to valumetric scaling. Contemporaneously, rational dither modulation was proposed as an alternative QIM with valumetric invariance. In this paper, we combine the two methods and compare the performance of the new algorithm with our previous results. Experimental results demonstrate that the new algorithm outperforms the previous algorithms over the entire range of valumetric scale factors, albeit at the expense of a small decrease in fidelity. However all algorithms have a superior performance and improved fidelity compared with QIM