Human Visual System Models in Digital Image Watermarking

In this paper some Human Visual System (HVS) models used in digital image watermarking are presented. Four different HVS models, which exploit various properties of human eye, are described. Two of them operate in transform domain of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). HVS model in DCT domain consists of Just Noticeable Difference thresholds for corresponding DCT basis functions corrected by luminance sensitivity and self- or neighborhood contrast masking. HVS model in DWT domain is based on different HVS sensitivity in various DWT subbands. The third presented HVS model is composed of contrast thresholds as a function of spatial frequency and eye's eccentricity. We present also a way of combining these three basic models to get better tradeoff between conflicting requirements of digital watermarks. The fourth HVS model is based on noise visibility in an image and is described by so called Noise Visibility Function (NVF). The possible ways of exploiting of the described HVS models in digital image watermarking are also briefly discussed

Implementations of HVS Models in Digital Image Watermarking

In the paper two possible implementations of Human Visual System (HVS) models in digital watermarking of still images are presented. The first method performs watermark embedding in transform domain of Discrete Cosine Transform (DCT) and the second method is based on Discrete Wavelet Transform (DWT). Both methods use HVS models to select perceptually significant transform coefficients and at the same time to determine the bounds of modification of selected coefficients in watermark embedding process. The HVS models in DCT and DWT domains consist of three parts which exploit various properties of human eye. The first part is the HVS model in DCT (DWT) domain based on three basic properties of human vision: frequency sensitivity, luminance sensitivity and masking effects. The second part is the HVS model based on Region of Interest (ROI). It is composed of contrast thresholds as a function of spatial frequency and eye\'s eccentricity. The third part is the HVS model based on noise visibility in an image and is described by so called Noise Visibility Function (NVF). Watermark detection is performed without use of original image and watermarks have a form of real number sequences with normal distribution zero mean and unit variance. The robustness of presented perceptual watermarking methods against various types of attacks is also briefly discussed

Adaptive Digital Image Watermarking Based on Combination of HVS Models

In this paper two new blind adaptive digital watermarking methods of color images are presented. The adaptability is based on perceptual watermarking which exploits Human Visual System (HVS) models. The first method performs watermark embedding in transform domain of DCT and the second method is based on DWT. Watermark is embedded into transform domain of a chosen color image component in a selected color space. Both methods use a combination of HVS models to select perceptually significant transform coefficients and at the same time to determine the bounds of modification of selected coefficients. The final HVS model consists of three parts. The first part is the HVS model in DCT (DWT) domain. The second part is the HVS model based on Region of Interest and finally the third part is the HVS model based on Noise Visibility Function. Watermark has a form of a real number sequence with normal distribution