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This work presents a new spectral data compression-rebuilding technique to translate the full IR spectral data into compact codes based on the analysis and comprehension encoding approach. This method has been successfully applied to a sample set of 505 IR spectra randomly picked from 100 000 spectra. The results show that the compression ratio reaches 12.7:1 under a very weak curve distortion. The choice of the number and shape of the basis functions is flexible. The IR spectra can be compressed in a fixed data size in fulfilling the distortion criteria. The data after compression have no significance in the sense of 蜶 spectra. To recover the original spectra, a specific algorithm must be applied. So the method can be used as a cryptic tool. Furthermore, the method can be applied to the compression of other complex curve by utilizing some of proper basis functions.
This work presents a new spectral data compression-rebuilding technique to translate the full IR spectral data into compact codes based on the analysis and comprehension encoding approach. This method has been successfully applied to a sample set of 505 IR spectra randomly picked from 100 000 spectra The results show that the compression ratio reaches 12.7: 1 under a very weak curve distortion. The choice of the number and shape of the basis functions is flexible. The IR spectra can be compressed in a fixed data size in fulfilling the distortion criteria. The data after compression have no significance in the sense of 蜶 spectra. To recover the original spectra, a specific algorithm must be applied. complex curve by utilizing some of proper basis functions.