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This book introduces two DWT-based audio watermarking schemes.Unlike the traditional entropy, we use the normalized energy instead of the probability. Thus, we redefined the entropy as an energy-proportion function that maps the low-frequency coefficients of DWT into a positive real value. Based on the invariant features and the properties of the energy-proportion function, the energy-proportion based scheme is then proposed.The second method presents an optimization-based group-amplitude quantization scheme for audio watermarking. Generally, the performance of a watermarking scheme is…mehr

Produktbeschreibung
This book introduces two DWT-based audio watermarking schemes.Unlike the traditional entropy, we use the normalized energy instead of the probability. Thus, we redefined the entropy as an energy-proportion function that maps the low-frequency coefficients of DWT into a positive real value. Based on the invariant features and the properties of the energy-proportion function, the energy-proportion based scheme is then proposed.The second method presents an optimization-based group-amplitude quantization scheme for audio watermarking. Generally, the performance of a watermarking scheme is measured in terms of signal-to-noise ratio (SNR) and bit error rate (BER).The SNR and BER are used to measure the quality and the robustness of a watermarked audio. However, there is a trade-off between them which is still a challenge in the field of the watermarking. To overcome this problem, we propose to select these coefficients based on minimizing a wavelet-based functional. Finally, we obtained an important formula for audio watermarking by using Lagrange Principle.
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Autorenporträt
Shuo-Tsung Chen received the B.S. degree in Mathematics from National Cheng Kung University in 1996 and M.S. degree in Applied Mathematics from Tunghai University in 2003, Taiwan. In 2010, he received the Ph.D. degree in Electrical Engineering from National Chinan University, Taiwan.