摘要
针对Contourlet变换在方向滤波分解过程中没有结合图像自身特性且各高频子带方向分解数目固定的问题,提出将熵作为描述图像高频子带纹理/边缘特性的指标,对Contourlet方向滤波过程进行优化处理.图像经小波变换后,首先对其各高频子带进行完全方向分解,然后分析各子带不同方向分解数时熵的变化情况,并按照最小熵原则给出各子带最优方向分解数目.在此基础上,将其与SPECK(set partitioning embedded block coder)图像编码算法结合,并根据视觉敏感度带通特性模型对不同子带分配不同的权值,以确保视觉上重要信息的优先传输.实验结果表明,算法的峰值信噪比得到明显提高,而且对局部纹理失真较小.
Directional decompositions of the Contourlet transform do not take into account the characteristics of the image itself and the number of directional decompositions in each high-frequency sub-band is fixed. To address these problems, we take entropy as the criterion for describing image texture/edge characteristics of high-frequency subbands in order to achieve optimal directional decomposition with Contourlet. After the wavelet transform, complete directional decomposition is carried out for each of the image＇s high-frequency sub-bands, and the sub-bands are then analyzed with respect to their changing entropy for different directional decompositions. The optimal number of directional decompositions can be determined using the minimum entropy principle. Next, we combined this optimal Contourlet directional decomposition method with the SPECK imago-ceding algorithm, wherein each sub-band is assigned a different weight according to its visual sensitivity band-pass characteristics, such that visually important information can be transmitted in advance. The experimental results show that the peak signal-to-noise ratio of the reconstructed image is significantly improved, and the distortion of local texture is effectively reduced.
出处
《信息与控制》
CSCD
北大核心
2015年第6期641-647,共7页
Information and Control
基金
国家自然科学基金资助项目(60736043)
中国石油科技创新基金研究项目(2012D-5006-0609)
黑龙江省教育厅科学技术研究项目(12521050)
