We propose a new reversible watermarking scheme. One first contribution is a DWT [Discrete Wavelet Transform] modulation which adaptively takes care of the local specificities of the image content. In this paper, we propose a new reversible watermarking scheme. One first contribution is a DWT modulation which adaptively takes care of the local specificities of the image content. By applying it to the image prediction-errors and by considering their immediate neighborhood, the scheme we propose inserts data in textured areas where other methods fail to do so. Furthermore, our scheme makes use of a classification process for identifying parts of the image that can be watermarked with the most suited reversible modulation. This classification is based on a reference image derived from the image itself, a prediction of it, which has the property of being invariant to the watermark insertion. Entropy masking is another human visual system’s characteristic, which rarely has been addressed in visual models. In this paper we also propose a universal entropy masking model for watermarking embedding algorithm to keep the balance between watermarks’ imperceptibility and its robustness. Also we conclude from the experiments that a suitable domain of entropy calculation will result in optimal watermarking performance. For the implementation of this proposed work we use Image Processing Toolbox under the Matlab software.