Spectral Images Browsing using Principal Component Analysis and Set Partitioning in Hierarchical Tree

摘要

Spectral imaging technology have been used mostly in remote sensing, but have recently been extended to new requiring high fidelity color reproductions like telemedicine, e-commerce, etc. These spectral imaging systems 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. 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 as follow: (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 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 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, △E was used.here standard D65 was used as a light source. 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 bands was 40 and the resolution was 8 bits per sample. Our experiments show the proposed compression method suitable for browsing, i.e., for visual purpose.