Human vision extracts the visible spectral component C*, called fundamental, from n-dimensional spectrum C. The projection from C to C* is described by the matrix R in FCS (Fundamental Color Space). FCS is spanned by a matrix F with a selected triplet in R. The matrix R is decomposed into “achromatic” R_A and “chromatic” R_C by choosing matrix F.This paper presents a Luma/Chroma opponent-color space that is created from spectral decomposition of fundamental based on matrix R theory. A new color space has orthogonal opponent-color axes with hue linearity because it's created through a linear naive transformation of fundamental in FCS.The key points lie in that the “chromatic” projector R_C is further decomposed into R_R and R_B opponent-color components and an orthogonal Luma/Chroma FCS is newly created by a set of (R_A, R_R, R_B), each composed of n×n matrix. Now image colors are mapped onto Luma/Chroma FCS. First, a tristimulus value XYZ from sRGB camera input is transformed back to the fundamental C* by pseudo-inverse projection. Next, C* is decomposed into the spectral triplet (C_A*, C_R*, C_B*) through the (R_A, R_R, R_B). Finally, the achromatic fundamental C_A*(λ), n-dimensional vector, is converted to the luminance value LA by integral over λ. As well, the chromatic fundamentals, C_R*(λ) and C_B*(λ) are converted to the chrominance values C_R and C_B. The paper shows how the image colors are mapped onto (L_A, C_R, C_B) Luma/Chroma space and introduces its application to the image segmentation in comparison with conventional CIELAB and IPT color spaces.