Optimized principal component analysis for camera spectral sensitivity estimation.

Abstract

This paper describes the use of a weighted principal component analysis (PCA) method for camera spectral sensitivity estimation. A comprehensive set of spectral sensitivities of 111 cameras was collected from four publicly available databases. It was proposed to weight the spectral sensitivities in the database according to the similarities with those of the test camera. The similarity was evaluated by the reciprocal predicted errors of camera responses. Thus, a set of dynamic principal components was generated from the weighted spectral sensitivity database and served as the basis functions to estimate spectral sensitivities. The test stimuli included self-luminous colors from a multi-channel LED system and reflective colors from a color chart. The proposed method was tested in both the simulated and practical experiments, and the results were compared with the classical PCA method, three commonly used basis function methods (Fourier, polynomial, and radial bases), and a regularization method. It was demonstrated that the proposed method significantly improved the accuracy of spectral sensitivity estimation.