Spectral imaging technology have been used mostly in remote sensing, but have recently been extended to new area
requiring high fidelity color reproductions like telemedicine, e-commerce, etc. These spectral imaging systems are
important because they offer improved color reproduction quality not only for a standard observer under a particular
illuminantion, but for any other individual exhibiting normal color vision capability under another illuminantion. A
possibility for browsing of the archives is needed.
In this paper, the authors present a new spectral image browsing architecture. The architecture for browsing is expressed
(1) The spectral domain of the spectral image is reduced with the PCA transform. As a result of the PCA
transform the eigenvectors and the eigenimages are obtained.
(2) We quantize the eigenimages with the original bit depth of spectral image (e.g. if spectral image is
originally 8bit, then quantize eigenimage to 8bit), and use 32bit floating numbers for the eigenvectors.
(3) The first eigenimage is lossless compressed by JPEG-LS, the other eigenimages were lossy compressed by
wavelet based SPIHT algorithm.
For experimental evalution, the following measures were used. We used PSNR as the measurement for spectral
accuracy. And for the evaluation of color reproducibility, ΔE was used.here standard D65 was used as a light source. To
test the proposed method, we used FOREST and CORAL spectral image databases contrain 12 and 10 spectral images,
respectively. The images were acquired in the range of 403-696nm. The size of the images were 128*128, the number of
bands was 40 and the resolution was 8 bits per sample. Our experiments show the proposed compression method is
suitable for browsing, i.e., for visual purpose.