Mixing Paint: An analysis of color value transformations in multiple coordinate spaces using multivariate linear regression
This work addresses a domain-specific problem for artists or color scientists, but it is incremental as it applies existing linear regression methods to new data on paint mixing.
The paper tackled the problem of mathematically modeling color transformations when physically mixing paints by testing 120 pairs of 16 paint colors using linear regression across multiple color spaces. The result identified a geometrically symmetrized linear combination in CIEXYZ space with the strongest coefficient of determination, while RGB space mapping yielded a better mean squared error.
I explore the mathematical transformation that occurs in color coordinate space when physically mixing paints of two different colors. I tested 120 pairs of 16 paint colors and used a linear regression to find the most accurate combination of input parameters, both in RGB space and several other color spaces. I found that the fit with the strongest coefficient of determination was a geometrically symmetrized linear combination of the colors in CIEXYZ space, while this same mapping in RGB space returns a better mean squared error.