Reversible image watermarking based on texture analysis of grey level co-occurrence matrix

Embedding the watermark in the complex area of the image can effectively improve concealment. However, most methods simply use the mean squared error (MSE) and some simple methods to judge the texture complexity. In this paper, we propose a new texture analysis method based on grey level co-occurrence matrix (GLCM) and provide an in-depth discussion on how to accurately choose a complex region. This new method is applied to the reversible image watermarking. Firstly, the original host image is divided into 128 * 128 sub blocks. Then, we use the mean square error to assign the weight of the four texture feature parameters to establish the relationship between the characteristic parameters and the complexity of an image sub block. Applying this formulaic series, we can calculate the complexity of each sub block, along with the selection of the maximum sub blocks of the texture complexity. If the embedding position is insufficient, then we select the second sub block to be embedded in the watermark, until the satisfactory embedding capacity is reached. Pairwise prediction error extend (PPEE) is used to hide the data.