Improved STDM Watermarking Using Semantic Information-Based JND Model

Abstract

The perceptual just noticeable distortion (JND) model has attracted increasing attention in the field of the quantization-based watermarking framework. The JND model can provide a superior tradeoff between robustness and fidelity. However, the conventional JND models are not fit for the quantization-based watermarking, as the image has been altered by watermarking embedding. In this paper, we present an improved spread transform dither modulation (STDM) watermarking scheme, which is based on the image primitive features produced according to JND mechanism. The procedures include the contrast masking effect by utilizing a new measurement of edge strength which represent semantic information. What’s more, the proposed semantic information-based JND model can be theoretically invariant to the changes in the watermark-embedding processing. The newly proposed JND model is very simple but more effective in the STDM watermarking. Experiments results demonstrate that the proposed watermarking scheme can bring about better performance compared with previously proposed perceptual STDM schemes.