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

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.