Subspace-Clustering-Based Multispectral Image Compression

Resource type
Date created
2014-11
Authors/Contributors
Author: Funt, Brian
Abstract
This paper describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard PCA, this approach finds clusters in different subspaces of different dimension. Consequently, instead of representing all spectra in a single low-dimensional subspace of a fixed dimension, spectral data are assigned to multiple subspaces having a range of dimensions from one to eight. For a given compression ratio, this tradeoff reduces the maximum reconstruction error dramatically. In the case of compressing multispectral images, this initial compression step is followed by lossless JPEG2000 compression in order to remove the spatial redundancy in the data as well.
Document
Description
Presented at the CIC'22 Color Imaging Conference, November, 2014.
Published as
Agahian, F., and Funt, B. "Subspace-Clustering-Based Multispectral Image Compression." Proceedings of CIC'22 Color Imaging Conference, Society for Imaging Science and Technology, Nov. 2014
Publication title
Proceedings of CIC'22 Color Imaging Conference, Society for Imaging Science and Technology
Document title
Subspace-Clustering-Based Multispectral Image Compression
Date
2014
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
Member of collection