# Why we don't normalize the images?

I was watching the video from this stanford course on convolutional neural nets where the professor says (at 28:59) 'we do zero-mean the pixel values in image but we do not normalize the pixel values much because in images, at each location, we already have relatively comparable scale and distribution'. I do not understand what does she mean by 'relatively comparable scale and distribution'?

Say you have an image with pixel values in $[0, 255]$. Besides removing the mean, you also want to divide by either the $(max - min)$ or by the standard deviation. The first step's goal is to reduce the mean of the dataset to zero, while the second's is to scale the pixel values down to a range close to $[-1, 1]$.