Abstract: High dimension of the spectral space in spectral color management results in long run time and large memory requirement in color processing of multi spectral images. To overcome this shortcoming,a method to construct intermediate space was proposed. 13y analyzing the color management process, an intermediate space was introduced and the flow of spectral color management based on the intermediate space was established firstly. Then for the printing applicalion of multi-spectral images, the principal component analysis method was employed to construct an eigenvector space based on the printer characteristic samples. The new space derived from the method was appointed as the intermediate space. Finally the eigcnvector matrix was utilized to achieve the transformation between the intermediate space and the spectral space. Experiments show that the transformation efficiency is increased when the printer characteristic samples are used during dimension reduction. The colorimetric and spectral precision of the conversion is high and low-dimensional image data in the intermediate space can keep the main information of the source spectral data.

Key words: Spectral color management, Intermediate space, Principal component analysis, Multi spectral image