For any image there is a unique optimal combination of channel mixing weights that produces the greatest amount of contrast. The line that corresponds to this axis (called the principal components axis) in color space is the one that best fits the cloud of data points representing the color coordinates of all of the image pixels. This can be determined by regression. This interactive tutorial illustrates the monochrome image contrast produced by principal components regression for various color images.

The tutorial initializes with a randomly selected specimen appearing in the Specimen Image window. The Choose A Specimen pull-down menu provides a broad selection of colorful specimen images, in addition to the initial randomly chosen one. Adjacent to the Specimen Image window is the PCA Grayscale Image window showing the result of PCA-based grayscale conversion of the specimen.

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