Just Noticeable Distortion Model and Its Application in Color Image Watermarking

In this paper, a perceptually adaptive watermarking scheme for color images is proposed in order to achieve robustness and transparency. A new just no-ticeable distortion (JND) estimator for color images is first designed in the wavelet domain. The key issue of the JND model is to effectively integrate visual mask-ing effects. The estimator is an extension to the per-ceptual model that is used in image coding for gray-scale images. Except for the visual masking effects given coefficient by coefficient by taking into account the luminance content and the texture of grayscale images, the crossed masking effect given by the inter-action between luminance and chrominance compo-nents and the effect given by the variance within the local region of the target coefficient are investigated such that the visibility threshold for the human visual system (HVS) can be evaluated. In a locally adaptive fashion based on the wavelet decomposition, the esti-mator applies to all subbands of luminance and chrominance components of color images and is used to measure the visibility of wavelet quantization errors. The subband JND profiles are incorporated into a color image watermarking scheme. Performance in terms of robustness and transparency of the water-marking scheme is obtained by means of the proposed approach to embed the maximum strength watermark while maintaining the perceptually lossless quality of the watermarked color image. Simulation results show that the proposed scheme with inserting watermarks into luminance and chrominance components is more robust than the existing scheme while retaining the watermark transparency.