title: Adaptive multiwavelet-based watermarking through JPW masking.
abstract: In this paper, a multibit, multiplicative, spread spectrum watermarking using the discrete multiwavelet (including unbalanced and balanced multiwavelet) transform is presented. Performance improvement with respect to existing algorithm is obtained by means of a new just perceptual weighting (JPW) model. The new model incorporates various masking effects of human visual perception by taking into account the eye's sensitivity to noise changes depending on spatial frequency, luminance and texture of all the image subbands. In contrast to conventional JND threshold model, JPW describing minimum perceptual sensitivity weighting to noise changes, is fitter for nonadditive watermarking. Specifically, watermarking strength is adaptively adjusted to obtain minimum perceptual distortion by employing the JPW model. Correspondingly, an adaptive optimum decoding is derived using a statistic model based on generalized-Gaussian distribution (GGD) for multiwavelet coefficients of the cover-image. Furthermore, the impact of multiwavelet characteristics on proposed watermarking scheme is also analyzed. Finally, the experimental results show that proposed JPW model can improve the quality of the watermarked image and give more robustness of the watermark as compared with a variety of state-of-the-art algorithms.
authors.affiliation: Department of Mathematics and Computer Science, Beijing University of Chemical Technology, Beijing 100080, China. mathcui@163.com
authors.forename: Lihong
authors.initials: L
authors.lastname: Cui
authors.forename: Wenguo
authors.initials: W
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D044962:Data Compression / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011356:Product Labeling / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis