Content Adaptive Watermark Embedding in the Multiwavelet Transform Using a Stochastic Image Model

Abstract

Content adaptive watermark embedding algorithm using a stochastic image model in the multiwavelet transform is proposed in this paper. Usually, watermark is embedded with the same embedding strength regardless of local properties of the cover image, so the visible artifacts are taken placed at flat regions. A watermark is embedded into the perceptually significant coefficients (PSCs) of each subband using multiwavelet transform. The PSCs in high frequency subband are selected by SSQ, that is, by setting the thresholds as the one half of the largest coefficient in each subband. The perceptual model is applied with a stochastic approach based on noise visibility function (NVF) that has local image properties for watermark embedding. This model uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The watermark estimation use shape parameter and variance of subband region, it is derive content adaptive criteria according to edge and texture, and flat region.