结合纹理复杂度和JND模型的图像水印算法

针对目前基于灰度共生矩阵相关水印算法存在容量较小的问题，提出一种将纹理复杂度和DCT域JND模型相结合的图像水印算法。首先将原图像分成子块，利用各子块灰度共生矩阵的四个纹理特性计算其纹理复杂度，并据此对子块进行排序以确定水印嵌入的子块位置后对原图像素矩阵进行DCT变换，结合新的分区方式计算各块JND值，根据JND值以及新的嵌入规则确定子块内水印嵌入方式。算法有效考虑了图像块的纹理特性和人眼视觉敏感性，加强了嵌入水印后的图像质量，提高了水印嵌入容量。实验结果表明，所提方法在嵌入相同容量的水印时，图像平均峰值信噪比（PSNR）较现有方法高出4.27%左右，在嵌入超过原方法容量上限一倍的水印时，图像平均PSNR仍有53.449 8 dB。

Image watermarking algorithm combining texture complexity and JND model

For a low capacity of the watermarking algorithm based on the gray scale symbiosis matrix, this paper would introduce the image watermark algorithm which combines texture complexity with DCT-domain JND model. Firstly, it divided the original image into several blocks, and the blocks the texture complexity would be calculated by four texture features of GLCM of each block. The blocks would be sorted based on the texture complexity to locate the blocks of watermark embedding. After that, the prime matrix of original image would be transformed into DCT domain and, combined with new partition way, JND value of each block would be calculated. The mode of watermark embedding would be determined on the basis of JND value and new rules of embedding. The experimental results show that In the same capacity of watermark embedding, the average peak sinal-to-noise ratio(PSNR) of the image in this method is 4.27% higher than existing mathods, when embedding a watermark that is twice the upper limit of the capacity of the original method, the average PSNR of the image is still 53.4498 dB.