Image Scrambling Degree Evaluation for Distance and Texture in Wavelet Domain

doi:10.3969/j.issn.1000-3428.2016.05.031

Abstract

Abstract:

Existing image scrambling evaluation methods usually are closely associated with pixel location,and are vulnerable to the influence of intentional and unintentional attacks,such as shear and rotation,and have a high error.To solve this problem,an image scrambling degree evaluation method based on lifting wavelet,Manhattan distance,and texture is proposed.The images before and after scrambling are transformed by lifting wavelet respectively;the statistical distance of corresponding wavelet coefficient is calculated;the Gray Level Co-occurrence Matrix (GLCM) is generated with high-frequency;the texture features are extracted;and the scrambling degree is obtained by distance and texture.Experimental results show that,compared with the existing scrambling methods based on location,this method has a wider application range,is more consistent with the subjective evaluation,less reliant on the original image,and more effective in scrambling degree evaluating.

Key words: lifting wavelet, Manhattan distance, texture, Gray Level Co-occurrence Matrix(GLCM), scrambling degree

摘要：

现有置乱评价方法大多与像素位置有密切关系,容易受到剪切、旋转等有意或无意攻击的影响,导致误差较高。针对该问题,提出一种基于提升小波、曼哈顿距离和纹理进行置乱程度评价的方法。对置乱前后图像分别进行提升小波变换,求对应系数的统计距离,高频生成灰度共生矩阵,提取纹理特征,通过距离和纹理求出置乱度。实验结果表明,与现有位置相关的置乱方法相比,该方法适用范围更广,与主观评价更一致,对原图像的依赖性更小,能更有效地用于置乱程度评价。

关键词: 提升小波, 曼哈顿距离, 纹理, 灰度共生矩阵, 置乱程度

CLC Number:

TP394.41

LU Zengxin,QU Dapeng,FAN Tiesheng. Image Scrambling Degree Evaluation for Distance and Texture in Wavelet Domain[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2016.05.031.

卢曾新,曲大鹏,范铁生. 小波域中距离与纹理的图像置乱程度评价[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2016.05.031.

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References

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