Turbo coding for sample-level watermarking in the DCT domain

Abstract

Coding at the sample level in still image watermarking takes advantage of avoiding a non-optimum initial diversity stage, used in many watermarking systems for tailoring Gaussian channels with desired properties. Nevertheless, the low signal-to-noise ratio (SNR) encountered, due to the perceptual constraints imposed to the watermark, makes necessary the use of very powerful coding techniques as it happens in deep-space communications.We study the application of turbo coding to sample-level discrete cosine transform (DCT) domain watermarking. This near-optimum codification strategy is possible thanks to the existence of statistical models in the DCT domain, that also permit the computation of theoretical bounds for performance.