In this chapter, the color reduction problem in the RGB color space is addressed using multilevel histogram-based thresholding techniques. Optimal threshold values for each color channel are determined by employing Otsu’s method, Kapur’s entropy-based method, and the histogram weighted mean approach. The RGB space is then partitioned into sub-classes according to these thresholds, and the pixels within each sub-class are represented by the corresponding class mean intensity value. This strategy enables significant data reduction while preserving the fundamental structural characteristics of the image. The effects of different thresholding techniques on color distribution, intra-class homogeneity, and visual quality are comparatively analyzed. The experimental findings demonstrate that multilevel thresholding-based color quantization can be effectively used as a preprocessing step for various image processing applications such as compression, segmentation, and pattern recognition.