Highly Reliable Stochastic Perceptual Watermarking Model Based on Multiwavelet Transform

This paper presents highly reliable adaptive image watermark embedding using a stochastic perceptual model based on multiwavelet transform. To embedding watermark, the original image is decomposed into 4 levels using a discrete multiwavelet transform, then a watermark is embedded into the only JND (just noticeable differences) of the image each subband. The perceptual model is applied with a stochastic multiresolution model for watermark embedding. This is based on the computation of a NVF (noise visibility function) that have local image properties. The perceptual model that has adaptive image watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the JND. This method uses not only stationary GG (Generalized Gaussian) model characteristic but also nonstationary JND model because watermark has noise properties. The experiment results of simulation of the proposed watermark embedding method using stochastic perceptual model based on multiwavelet transform techniques was found to be excellent invisibility and robustness more than Podilchuk’s algorithm.