Gradient based prediction for reversible watermarking by difference expansion

Ioan-Catalin Dragoi, D. Coltuc, I. Caciula
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引用次数: 21

Abstract

This paper proposes a novel predictor, EGBSW (Extended Gradient Based Selective Weighting), and investigates its usefulness in difference expansion reversible watermarking. EGBSW is inspired by GBSW, a causal predictor previously used in lossless image compression and known to outperform well-known predictors as the median edge detector (MED) or the gradient-adjusted predictor (GAP). The proposed predictor operates on a larger prediction context than the one of GBSW, namely a rectangular window of 16 pixels located around the pixel to be predicted. Similar to GBSW, the extended predictor computes the gradients on horizontal, vertical and diagonal directions and selects the smallest two gradients. Opposite to the classical predictor, EGSBW uses a set of four simple linear predictors associated with the four principal directions and computes the output value as a weighted sum between the predicted values corresponding to the selected gradients. The reversible watermarking scheme based on EGBSW appears to outperform not only the ones based on GBSW, MED or GAP, but also some recently proposed schemes based on the average on the rhombus context. Experimental results are provided.
基于梯度的差分展开可逆水印预测
提出了一种新的预测器——基于扩展梯度的选择性加权(EGBSW),并对其在差分展开可逆水印中的应用进行了研究。EGBSW受到GBSW的启发,GBSW是一种之前用于无损图像压缩的因果预测器,并且已知优于众所周知的预测器,如中值边缘检测器(MED)或梯度调整预测器(GAP)。所提出的预测器操作在比GBSW更大的预测上下文上,即位于待预测像素周围的16个像素的矩形窗口。与GBSW类似,扩展预测器计算水平、垂直和对角线方向上的梯度,并选择最小的两个梯度。与经典预测器相反,EGSBW使用一组与四个主方向相关的四个简单线性预测器,并将输出值计算为与所选梯度对应的预测值之间的加权和。基于EGBSW的可逆水印方案不仅优于基于GBSW、MED和GAP的可逆水印方案,而且优于近年来提出的基于菱形背景下均值的可逆水印方案。给出了实验结果。
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