Spectral compression using subspace clustering

Abstract

This article describes a subspace clustering strategy for the spectral compression of multispectral images. Unlike standard principal component analysis, this approach finds clusters in several 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. In other words, this strategy allows us to distribute spectra into different subspaces thereby obtaining the best fit for each. As a result, more resources can be allocated to those spectra that need many dimensions for accurate representation and fewer resources to those that can be modeled using fewer dimensions. For a given compression ratio, this trade off reduces the overall reconstruction error. In the case of compressing multispectral images, this initial compression method is followed by JPEG2000 compression in order to remove the spatial redundancy in the data as well. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 7–15, 2016

Charamer mismatch-based spectral gamut mapping

“…The primary goal of the charamer mismatch-based spectral gamut mapping framework is to transform out-of-gamut spectra into a device's spectral gamut based on the spectral characteristics of each point cluster. The subspace clustering method [2,11] is a straightforward and effective technique for doing this by constructing a relation between node addresses and spectral reflectance sets. Instead of mapping a sample's spectra onto the spectral device gamut using a global strategy, the mapping is performed sequentially.…”