On the Quality Evaluation of Spectral Image Processing Algorithms

Abstract

The main contribution of this work lies in the context of the quality assessment of spectral image processing operators or algorithms. Several means to define quality measurements for low-level spectral image processing tools are explored, i.e. Spectral distance and spectral image filtering. The quality assessment starts from simple theoretical validation on simulated spectra to reduced and full reference quality assessment on real spectral data. Spectral image of a pigment patch with different shades and a simple model of spectral noise are proposed to be used in reduced and full reference quality assessment. Finally, given an application purpose, this quality assessment protocol will aid reader in selecting a correct spectral distance which will be the core of other distance-based spectral image processing tools.