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Continuing our previous research on color image compression, we move towardsspectral image compression. This enormous amount of data needs more space tostore and more time to transmit. To manage this sheer amount of data,researchers have investigated different techniques so that image quality can beconserved and compressibility can be improved. The principle component analysis(PCA) can be employed to reduce the dimensions of spectral images to achievehigh compressibility and performance. Due to processing complexity of PCA, asimple interpolation technique called cubic spline interpolation (CSI) wasconsidered to reduce the dimensionality of spectral domain of spectral images.The CSI and PCA were employed one by one in the spectral domain and wereamalgamated with the JPEG, which was employed in spatial domain. Three measuresincluding compression rate (CR), processing time (Tp) and color differenceCIEDE2000 were used for performance analysis. Test results showed that for afixed value of compression rate, CSI based algorithm performed poor in terms ofdE00, in comparison with PCA, but is still reliable because of small colordifference. On the other hand it has lower complexity and is computationallymuch better as compared to PCA based algorithm, especially for spectral imageswith large size.

Comparing CSI and PCA in Amalgamation with JPEG for Spectral Image Compression