Improved Spread Transform Dither Modulation Using Luminance-Based JND Model

Abstract

In the quantization-based watermarking framework, perceptual just noticeable distortion (JND) model has been widely used to determine the quantization step size to provide a better tradeoff between fidelity and robustness. However, the perceptual parameters computed in the embedding procedure and the detecting procedure are different, as the image has been altered by watermark embedding. In this paper, we incorporate a new DCT-based perceptual JND model, which not only shows better consistency with the HVS characteristics compared to the conventional models, but also can be invariant to the changes in the watermark framework. Furthermore, an improved spread transform dither modulation (STDM) watermarking scheme based on the new JND model is proposed. Experimental results show that the proposed scheme provides powerful resistance against common attacks, especially in robustness against Gauss noise, amplitude scaling and JPEG compression.