Color plays a fundamental role in how we perceive and analyze images, yet traditional approaches, like the RGB model, often struggle to capture the nuances needed for precise feature recognition. In this study, we explore new ways to interpret color in images through computational experimentation. Two innovative methods, named “Separate” and “Max,” were developed and tested on a small set of synthetic images, created by shifting a base image diagonally. Using Fourier and wavelet-based analysis, we evaluated how these approaches support feature recognition. The results suggest that the Separate method can reveal details more effectively than the conventional Luminosity RGB model, offering clearer insights into image content. This work demonstrates that thoughtfully designed computational experiments can inspire more flexible and accurate strategies for interpreting color, providing a fresh perspective on image analysis and feature extraction.