K means clustering of image with k=1 vs mean of all pixels

I have relatively uniformly colored images and I extracted colors using k-means. k means 1 showed the best results for my modeling purposes, k means 2 not so much, and with k-means 3 there ceased to be differences between some channels of samples.

Is this a reasonable approach and is it technically different from calculating the mean of all pixels in the image.

Can it be said that I took the mean of all pixels if the result is the …

clustering colors datascience differences image images k-means mean modeling pixels samples