When designing a watermarking algorithm, there are trade-offs between three parameters: payload, fidelity, and robustness. Data payload is the number of bits that can be embedded in digital data; fidelity is the degradation introduced into the signal; and robustness is the ability of the watermark to remain readable after harmless or malicious signal processing operations on the watermarked image. These parameters conflict with each other and should be set to meet the requirements of the application (Levicky & Foris,2004). Watermarking techniques are generally classified into the spatial domain or the transformation domain. Previous watermarking techniques were almost in the spatial domain. Spatial domain techniques are not sufficiently robust to image compression and other image processing (Potdar & et al, 2005). Although some methods, for example in (Depovere & et al, 1998), have adopted pre-filtering capabilities to increase the identification rate, experimental results have shown the fundamental disadvantages of spatial domain watermarking. Transformed domain watermarking schemes such as those based on the discrete cosine transform (DCT) ((Chu, 2003), (Lin & Chin2000), (Deng & Wang, 2003)) and the discrete wavelet transform (DWT) ((Hsieh &t al, 2001), (Reddy & Chatterji, 2005), (Tay & Havlicek, 2002)) typically provide greater image imperceptibility and are much more resistant to image manipulation. It is generally believed that the performance of most existing watermarking systems is not close enough to the fundamental limit on robust watermark embedding rates at which high perceptual image quality is maintained. However, incorporating the watermark into the perceptually significant coefficients could alter the perceived visual quality...... middle of paper ......f Third International Conference on Image and Graphics, IEEE, pp. 349 - 352.26) A Jain, (1989). “Fundamentals of Digital Image Processing,” Prentice-Hall, chap. 7.27) R. Schalkoff, (1989). “Digital Image Processing and Computer Vision,” John Wiley & Sons, chap. 4.28) R. Haralick and L. Shapiro, (2006). “Computer Vision and Robotics,” vol. 2, Addison-Wesley Publishing Company.29) Tinku Acharya, Ajoy K. Ray, (2005). “Principles and Applications of Image Processing” A Wiley Interscience, pp. 253 – 285.30) B. Horn, (1986). “Robot Vision”, MIT Press, Chap.6, 8.31) D. Vernon, (1991). “Machine Vision”, Prentice-Hall, pp. 98 - 99, 214.32) D. Marr, (1982). “Vision,” Freeman, chap. 2, pp. 54 - 78.33) G. Langelaar, I. Setyawan, RL Lagendijk, (2000). “Watermarking Digital Image and Video Data,” in IEEE Signal Processing Magazine, September, volume 17, pp. 20-43.