Constrained Wavelet Tree Quantization for Image Watermarking

This paper investigates the operations of the wavelet tree based quantization and proposes a constrained wavelet tree quantization for image watermarking. The wavelet coefficients of the cover image are grouped into super trees for watermark embedding where quantization is performed. The watermark bits are extracted based on a modulus approach and the minimum mean comparison of the super tree coefficients efficiently distinguishes which super tree is quantized. Without the needs of the requantization index at the decoder, the constrained quantization of the super trees reduces the uncertainty of the maximum likelihood detection. Therefore, the robustness of the proposed scheme can be effectively improved. This study has performed intensive comparison for the proposed scheme with the non-constrained tree quantization method under various geometric and nongeometric attacks. The experiment results demonstrate that the proposed technique yields better performance with higher degree of robustness.