基于小波压缩域的统计纹理特征提取方法

Statistical Texture Feature Extraction Based on Wavelet Compressed Domain

本文提出了一种基于相邻尺度子带间统计特性的、可直接在小波压缩域操作的纹理特征提取方法 .主要思想是依据小波压缩域各子带相应空间位置的系数间存在着明显的相关性 ,提取反映这种依存关系的纹理特征 .在提取特征时采用与压缩标准兼容的技术 ,可在部分解压的情况下 ,实现压缩域纹理图象的快速分类 .实验结果表明提出的方法优于现有常用方法 ,计算速度明显提高 .尤其是和传统的子带能量特征相结合时 。

Discrete wavelet transform has often been used for multi-scale texture characterization through the analysis of spatial-frequency content.Most previous methods make no account of the correlation of coefficients in adjacent subbands.However,in intuition,the coefficients in adjacent subbands are highly correlated.A novel method for texture feature extraction is now proposed based on the statistics relationships of wavelet coefficients at adjacent scale subbands with the same orientation.In addition,taking into account the fact that most of current images are stored and transmitted in the compressed format,we try to make the proposed method compatible with the new generation image compression standard-JPEG2000.Therefore,texture classification can be performed directly on the compressed DWT domain(just entropy decoding needed).Experimental results show that the proposed scheme has outperformed the previous methods,and the best performance is achieved by combining cross-subband relationship and traditional subband energy.