  Continuing our previous research on color image compression, we move towards
spectral image compression. This enormous amount of data needs more space to
store and more time to transmit. To manage this sheer amount of data,
researchers have investigated different techniques so that image quality can be
conserved and compressibility can be improved. The principle component analysis
(PCA) can be employed to reduce the dimensions of spectral images to achieve
high compressibility and performance. Due to processing complexity of PCA, a
simple interpolation technique called cubic spline interpolation (CSI) was
considered to reduce the dimensionality of spectral domain of spectral images.
The CSI and PCA were employed one by one in the spectral domain and were
amalgamated with the JPEG, which was employed in spatial domain. Three measures
including compression rate (CR), processing time (Tp) and color difference
CIEDE2000 were used for performance analysis. Test results showed that for a
fixed value of compression rate, CSI based algorithm performed poor in terms of
dE00, in comparison with PCA, but is still reliable because of small color
difference. On the other hand it has lower complexity and is computationally
much better as compared to PCA based algorithm, especially for spectral images
with large size.
