基于多尺度分解与预测误差扩展的可逆图像水印算法

针对现有可逆图像水印算法中嵌入量与图像视觉质量之间的矛盾,提出了一种基于多尺度分解与预测误差扩展的可逆图像水印算法。首先,对原始图像进行多尺度分解,将其分解为同质块和非同质块；其次,通过对同质块进行预测误差扩展来嵌入水印信息；最后,通过计算非同质块信息熵,根据嵌入水印信息量选择合适的非同质块,通过对选取的非同质块进行整数小波变换,在中高频中嵌入剩余水印信息。实验结果表明,该算法易于实现且完全可逆,能有效提高水印嵌入量,具有较高的视觉质量。

Reversible Image Watermarking Algorithm Based on Multi-scale Decomposition and Prediction Error Expansion

In view of the contradiction between the embedding capacity and the visual quality of the existing reversible image watermarking algorithm,a reversible image watermarking algorithm based on multi-scale decomposition and prediction error expansion was proposed.Firstly,the original image is decomposed into homogeneous and non-homogenous blocks by multi-scale decomposition.Then,the watermark information is embedded by the prediction error expansion of homogeneous blocks.Finally,based on the calculation of non-homogenous block information entropy,the non-homogenous blocks are selected according to the amount of embedded watermark information,and the remaining watermark information is embedded in the middle and high frequency by integer wavelet transform.Experimental results show that the proposed algorithm is easy to implement and is completely reversible,and it can effectively improve the watermark embedding capacity and has higher visual quality.