Q:

What does "Mean of each pixel over all images" mean?

I was reading a paper related to Auto encoders for my project work. It is required to input images as vectors to the neural network. I couldn't understand a certain sentence due to lack of knowledge of statistics (I guess). I Googled, but the problem is I don't know what it is exactly and searching the same phrase returns the same kind of documents but not their explanation.
Source: http://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf

We train on 1.6 million 32*32 color images that have been preprocessed
  by subtracting from each pixel its mean value over all images and then
  dividing by the standard deviation of all pixels over all images.

What does it mean by "subtracting from each pixel its mean value over all images and then
dividing by the standard deviation of all pixels over all images".
My interpretation is: "Subtracting from each pixel its mean value over all images"
   It means, for a pixel position in an image, subtract the average of values of that pixel position over all images and subtract from the current pixel value.
Am I correct? 
It is somewhat ambiguous to me.
Please explain in some math terms.

A:

Each image is composed of 32 $\times$ 32 pixels, so for a given pixel (say row 13, column 31) something measured is averaged over all the images, and the standard deviation (SD for short) for the same something is also calculated. 
(value − mean) / SD is often called a z-score and is a way of standardizing values to take account of mean and SD. Presumably that's done for every pixel, meaning every pixel position. 
It is spelled out that they are "dividing by the standard deviation of all pixels over all images" [my emphasis] and that SD would usually be calculated with reference to the corresponding overall mean. However, division by that SD would be dividing by a constant, so it won't have any effect on the images beyond a question of units.

