Hierarchical watermarking framework based on analysis of local complexity variations

Increasing production and exchange of multimedia content have increased the need for better protection of copyright using watermarking. Different methods have been proposed to satisfy the tradeoff between imperceptibility and robustness as two important characteristics in watermarking while maintaining proper data-embedding capacity. Many watermarking methods use independent image set of parameters. Different images possess different potentials for the robust and transparent hosting of watermark data. To overcome this deficiency, in this paper we have proposed a new hierarchical adaptive watermarking framework. At the higher level of the hierarchy, the complexity of an image is ranked in comparison with complexities of images of a dataset. For a typical dataset of images, the statistical distribution of block complexities is found. At the lower level of the hierarchy, for a single cover image that is to be watermarked, complexities of blocks can be found. Local complexity variation among a block and its neighbors is used to change the watermark strength factor of each block adaptively. Such local complexity analysis creates an adaptive embedding scheme, which results in higher transparency by reducing blockiness effects. This two-level hierarchy has enabled our method to take advantage of all image blocks to elevate the embedding capacity while preserving imperceptibility. For testing the effectiveness of the proposed framework, contourlet transform in conjunction with discrete cosine transform is used to embed pseudorandom binary sequences as a watermark. Experimental results show that the proposed framework elevates the performance the watermarking routine regarding both robustness and transparency.