基于Zernike矩和熵的图像感知哈希算法

提出了基于Zernike矩和熵特征的数字图像感知哈希算法。算法利用Zernike矩计算参考方向,以计算等面积环块和等角度扇形块内的熵作为感知特征,并通过量化处理构造哈希序列。算法利用哈希码之间的欧氏距离作为图像内容相似性的判定依据。实验结果表明,该算法对加性噪声、JEPG压缩、几何变换等操作具有较好的鲁棒性,且对于内容不同的图像有较好的区分度。

Perceptual Image Hashing Based on Zernike Moment and Entropy

A novel perceptual image hashing based on entropies features and Zernike moments has been proposed in this paper.This approach calculates the reference orientation by Zernike moments and the entropies of equal area ring and angle as perceptual features.Then the hash sequence is generated after quantization.The Euclidean distance is used to measure content similarity in the scheme.The experimental results show that the proposed algorithm has good robustness on additive noise,JPEG compression,geometric operations and good discrimination for different images.