Similar to indexing for two-dimensional arrays, the coordinate for a digital image pixel is a pair of two integers, representing the x- and y-coordinates of that pixel; the x-coordinate indicates the pixel's location along the horizontal axis starting from the left, and the y-coordinate indicates the pixel's location along the vertical axis starting from the top.

Here, we can see how heavy computational number-crunching processes are typically involved when it comes to image processing, as each image is a matrix of integer tuples. This also suggests that, with the help of the NumPy library and concurrent programming, we can implement significant improvements in execution time for Python image processing applications.

Following the convention of indexing two-dimensional arrays in NumPy, the location of a pixel is still a pair of integers, but the first number indicates the index of the row containing the pixel, which corresponds to the y-coordinate, and similarly, the second number indicates the x-coordinate of the pixel.