PCA component mixing for watermark embedding in spectral images

This study considers watermark embedding in spectral images. The embedding takes place in a transform space which is obtained through the Principal Component Analysis (PCA). The watermark is embedded in one eigenimage by mixing one eigenimage and the watermark. The watermark is a visual watermark which spreads to all bands of the image after the inverse PCA-transform. This new method is a generalization of an existing method. Our experiments indicate that a suitable set of parameter values allows better embedding than the methods compared.