Hierarchical watermarking framework based on analysis of local complexity variations
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abstract
Increasing production and exchange of multimedia content has increased the
need for better protection of copyright by means of 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 image independent
set of parameters. Different images possess different potentials for 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 hierarchy, 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 (LCV) among a block and its
neighbors is used to adaptively control the watermark strength factor of each
block. 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 (CT) in
conjunction with discrete cosine transform (DCT) is used to embed pseudo-random
binary sequences as watermark. Experimental results show that the proposed
framework elevates the performance the watermarking routine in terms of both
robustness and transparency.
