Joint watermarking and compression for Gaussian and Laplacian sources using uniform vector quantization

Abstract

Using fixed rate uniform vector quantization, in this paper, we consider how to design a joint watermarking and compression (JWC) system for Gaussian and Laplacian sources to maximize the robustness in the presence of additive Gaussian attacks under constraints on the compression rate and quantization distortion. Firstly, we construct vector quantizers shaped to match the multidimensional distribution of source signals. Then we scale codebooks corresponding to the vector quantizers to maximize the robustness of the watermarks against the additive Gaussian attacks. Simulation results show that the proposed scheme can achieve up to 0.92 dB distortion-to-noise ratio (DNR) gain over JWC schemes using uniform scalar quantization while maintaining the simplicity of implementation with uniform quantization.