Keywords: wavelet-tree, structure-based quantization, super-trees, statistical characteristic, PSNR
Received May 27, 2008; revised October 24, 2008; accepted February 27, 2009.
Communicated by H. Y. Mark Liao.
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GIN-DER WU AND PANG-HSUAN HUANG
Department of Electrical Engineering
National Chi Nan University
Puli, 545 Taiwan
This work presents a novel robust wavelet-tree-based watermarking method based on
structure-based quantization. Wavelet-trees are arranged into super-trees. The watermark
bits are then embedded into the super-trees by using the proposed structure-based quantization
method. Next, the super-trees are quantized into a significant structure according to
these bits. The quantized super-tree has a stronger statistical characteristic than the unquantized
super-tree. Based on this characteristic, the watermark bits could be extracted
robustly after an image distortion attack. Finally, an adaptive method is developed to raise
the PSNR value. Compared with Wang et al. [17] method, the proposed adaptive method
increases PSNR about 5.83dB. The proposed method also has a higher maximum number
of watermark bits than other methods, thus increasing the capacity for embedding. Besides,
its computation load is low. Experimental results demonstrate that the proposed watermarking
method using adaptive structure-based wavelet-tree quantization performs well in
JPEG compression, filtering (Gaussian filter, median filter and sharpen) and geometric attacks
(pixel shifting and rotation). In addition, it is very robust against multiple watermark
attacks.
Received May 27, 2008; revised October 24, 2008; accepted February 27, 2009.
Communicated by H. Y. Mark Liao.