For the curious, if you want to avoid numpy's memory overhead[1] here's how you'd do it in C with no dependencies: https://github.com/stb-tester/stb-tester/blob/v32/_stbt/sqdi...

This C code isn't doing exactly the same thing; here it's calculating a global similarity measure by calculating sum-of-squared-differences and comparing the end sum against the specified threshold, instead of comparing each pixel-wise diff against the threshold.

It is straightforward C code but it's vectorised by the compiler: https://godbolt.org/z/-3uO1z

Excluding the overhead of reading & decoding the PNGs, it takes 80ms for the same 8400x4725 images as my parent comment.

Benchmark:

  $ git clone git@github.com:stb-tester/stb-tester.git
  $ cd stb-tester
  $ make
  $ ipython
  >>> import _stbt.sqdiff, cv2, timeit
  >>> f1 = cv2.imread("water-4k.png")
  >>> f2 = cv2.imread("water-4k-2.png")
  >>> timeit.timeit(lambda: _stbt.sqdiff._sqdiff_c(f1, f2), number=1)
  0.081

[1] The Python code in my parent comment will allocate memory (the size of the entire image) for each intermediate calculation like the colourspace conversion, the absolute differences, etc.