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Dynamic energy enabled differentiation (DEED) image watermarking based on human visual system and wavelet tree classification

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Abstract

In this paper, we present a novel dynamic energy enabled differentiation (DEED) watermarking algorithm based on the wavelet tree classification and human visual system (HVS). The wavelet coefficients of the image are divided into disjoint trees and a wavelet tree consists of 21 coefficients which are divided into 6 blocks. One watermark bit is embedded into one wavelet tree using the energy differentiation of positive and negative modulation between coefficients of each block. In addition, the contrast sensitive function (CSF) of human visual system is also considered for better weighting in watermarking since the wavelet coefficients across the subbands perform different characteristics and importance. As DEED still requires extra storage of side information during the extraction and results non-blind watermarking approach, a random direction differentiation approach called DEEDR is then proposed which is a truly blind watermarking technique. This study has performed intensive comparison for the proposed scheme with other tree energy differentiation based techniques like WTQ, ABW-TMD and WTGM under various geometric and nongeometric attacks. From the experimental results, the advantage of DEED based algorithms is not only with low complexity, but also outperforms WTGM and WTQ in terms of robustness and imperceptibility of watermarking.

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Acknowledgments

The author would like to thank the anonymous reviewers with their valuable comments to improve the quality of this manuscript and Bai-Jiun Chen at National Chiao Tung University who helps to write the programs for software experiments. This work is partially supported by the National Science Council in Taiwan, Republic of China, under Grant NSC96-2416-H009-015, NSC97-2410-H009-034 and 99-2918-1-009-008.

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  1. Institute of Information Management, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsin-Chu, 300, Taiwan, Republic of China

    Min-Jen Tsai

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  1. Min-Jen Tsai
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Correspondence to Min-Jen Tsai.

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Tsai, MJ. Dynamic energy enabled differentiation (DEED) image watermarking based on human visual system and wavelet tree classification. Multimed Tools Appl 52, 385–406 (2011). https://doi.org/10.1007/s11042-009-0422-5

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  • DOI: https://doi.org/10.1007/s11042-009-0422-5

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