Speech Watermarking Using Adaptive Vector Quantization Index Modulation
Conventional Quantization Index Modulation (QIM) methods employ a fixed quantization step-size that results in poor robustness of watermarking schemes. In this paper, the quantization step-size in the QIM method is adaptively selected using a power-law function. In this paper, the magnitude of DFT coefficients of the host signal are used for data hiding. The analytical error probability and embedding distortion are derived and assessed by simulations on artificial signals. The optimum parameter in the power-law function is obtained based on minimizing the error probability.