基于小波包变换的三维荧光光谱数据压缩方法

基于db小波包变换，采用频率分级阈值方法对三维荧光光谱数据进行了压缩。建立了数据的小波包分解树，根据对数能量熵最小原则确定最优树，通过频率分级阈值方法对最优树中的小波包系数进行压缩，并且用实验获取的数据加以验证。实验结果表明，和小波变换相比小波包变换能够更有效地保留数据的细节信息。通过和其他阈值法比较可知，频率分级阈值法具有更好的压缩率和数据恢复能力，其压缩分数达到90%，恢复分数大于98%，谱线相对误差小于1%。平行因子分析重构光谱与原始光谱的结果表明，小波包压缩能有效保留有用信息。

Study of excitation-emission-matrix (EEMs) compression based on wavelet packet transform

Excitation-emission-matrix (EEMs) data have been compressed with a frequency grade threshold based on a Daubechies (db) wavelet packet transform. First, the tree of the wavelet packet was formulated; secondly, the best-tree was determined by the principle of minima; finally, the frequency grade threshold was used to compress the wavelet packet coefficient. Experimental data were used to test the compression ability of this method. The compression results indicated that wavelet packet compression could retain more information than wavelet compression. The frequency grade threshold method based on wavelet packets was adopted to compress EEMs. The frequency grade threshold method afforded higher reconstruction percentages and compression percentages than other threshold compression methods: the maximum reconstruction percentage reached 90%, and the compression percentage was larger than 98%, with errors between spectra of lower than 1%. With the use of parallel factor analysis (PARAFAC) of the original EEMs data and the compressed data, it was possible to obtain the characteristics of the excitation and emission spectra of each component. The results of PARAFAC show that wavelet packet transform can compress and recover the information in EEMs data effectively.