Robust Digital Image Watermarking using Gradient Vector Quantization and Denoising using Contourlet

we propose a robust quantization-based image watermarking scheme, called the gradient direction watermarking (GDWM), and based on the uniform quantization of the direction of gradient vectors. In GDWM, the watermark bits are embedded by quantizing the angles of significant gradient vectors at multiple wavelet scales. The proposed scheme has the following advantages: 1) Increased invisibility of the embedded watermark because the watermark is embedded in significant gradient vectors, 2) robustness to amplitude scaling attacks because the watermark is embedded in the angles of the gradient vectors, and 3) increased watermarking capacity as the scheme uses multiple-scale embedding. To quantize the gradient direction, the DWT coefficients are modified based on the derived relationship between the changes in the coefficients and the change in the gradient direction. This watermarking technique is more Robust to various sizes of watermark Images.In this paper, we pursue a “true” two dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develops a transform in the continuous domain and then discretizes for sampled data, our approach starts with a discrete-domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, we construct a discrete-domain mutiresolution and multidirectional expansion using non-separable filter banks, in much the same way that wavelets were derived from filter banks. This construction results in a flexible mutiresolution, local, and directional image expansion using contour segments, and thus it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires order N operations for N-pixel images. Finally, we show simulation results using contourlet denoising method over wavelets.