整数提升小波多相矩阵分解系数的快速提取算法  

Fast extraction algorithm of the polyphase matrix decomposition coefficient based integer lifting wavelet

作　　者：王锋[1] 赵志文[1] 牟盛[1] 

机构地区：[1]北京师范大学信息科学与技术学院,北京100875

出　　处：《中国图象图形学报》2012年第3期329-336,共8页Journal of Image and Graphics

摘　　要：整数提升小波多相矩阵分解系数不唯一,选取方法多样,计算量大。首先采用滤波器迭代次数选取算法,按照输入的信噪比(SNR)比例求出优化迭代次数;然后以非线性迭代比较算法为判定准则,结合求出的优化迭代次数,得到满足参数要求的优化分解系数。迭代次数是依据待测数据求得的,因此优化分解系数对该数据取得较好的处理效果,满足多相矩阵分解系数选取的要求。迭代比较算法满足收敛特性,通过比较滤波器的冲击和阶跃响应是否满足设定的误差限,可减少迭代运算次数,快速准确地选取优化小波系数。通过实验分析可知,该快速提取算法能有效满足数据处理的要求,减少待测数据处理的计算量,提高数据处理的效率。The polyphase matrix decomposition coefficient of Integer Lifting Wavelet is is not unique. It has diverse selection methods and large computation quantity. First, the selection algorithm of filter iteration times is adopted to obtain the optimal iteration times, according to the input signal-to-noise ratio. Then, the comparison algorithm of the nonlinear iterated function, which combines the optimal iteration time, is treated as criteria to obtain the optimal decomposition coefficient. The iteration time is based on the input data, so the decomposition coefficient will have the optimal processing effect for the data, meeting the selection requirements of the polyphase matrix decomposition coefficients. The comparison algorithm meets the convergence characteristics, which can reduce the number of iterations by comparing whether the impulse response and step response of filters meet the error limit, in order to obtain the optimal decomposition coefficient quickly. Stemming from experiment results, this fast extraction algorithm can effectively meet the requirements of data processing, reduce the data computation quantity and elevate the data processing efficiency.

关 键 词：整数提升小波 迭代次数 多相矩阵 快速提取算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]