基于差分统计特性的图像置乱度盲评价线性模型

当前大部分图像置乱度评价算法均依赖于原始图像，且缺乏科学的数学模型作为基础。在分析置乱图像差分值统计分布特性的基础上，建立了理想置乱图像差分统计分布线性模型，并以此为基础，提出了三种置乱度盲评价算法：斜率绝对差法、差分绝对差法和重叠面积法。实验结果表明：三种算法对于图像差分统计分布有较强的敏感性，且不依赖于原始图像，能客观地评价图像置乱度，与人类视觉系统有着良好的一致性。

Linear model for blind evaluation of image scrambling degree based on difference statistic distribution

Most of the current approaches to evaluate the degree of image scrambling depend on original images. And there are no scientific mathematical models as their theoretic basis. A linear model for difference statistic distribution of ideal scrambled image was put forward in this paper by analyzing the difference statistic distribution of scrambled image. Furthermore，three methods were presented based on this model to evaluate image scrambling degree. The first one was the absolute difference of slope, the second was the absolute difference of difference, and the third was method of overlapping area. The experimental results indicate that these methods are very sensitive to the statistical distribution of image difference, and they are independent of original image with good agreement with human vision system, so they can achieve blind evaluation for image scrambling degree objectively.