Its dimension is not always equal to three, as for classical color spaces.
Through linear or nonlinear transforms, the RGB color space can be transformed to other classical color spaces.
On contrary to classical color spaces, we treat the R, G, and B components as a sampled spectral signal.
However, these classical color spaces mostly treat the R, G, and B components as three independent elements.
We define a new kind of color space by selecting a set of color components which can belong to any of the different classical color spaces.
This demonstrates that the discriminating power of the IF1F2 color space is higher than that of the other two classical color spaces.
In this paper, on contrary to classical color spaces, we treat the R, G, and B components as a sequence of a spectral signal, called a RGB signal.
Because of this, it has been subject of interest to obtain the Hajós-like theorems for several variations of the classical coloring problem.
Unlike the classical color spaces that treat the R, G, and B (RGB) components of a pixel's color as independent elements, the proposed color space treats RGB as a sampled spectral signal, called a RGB signal.
In microscopic image processing, such as the cervical cell image segmentation, most algorithms are still based on classical color spaces, which cannot best describe the color features of microscopic images [8],[10]-[13] ,[10]-[13]
Because the high frequency component of the RGB signal's Fourier spectrum is, to some extent, the reflection of the difference between the R, G, and B, it may be more suitable for describing colors of the microscopic images than classical color spaces.
