No - 000000 is not based on a statistical model of what’s most likely to be right if the decimal place. In natural images - the statistical structure allows for this image upscaling but without revealing any previously hidden detail - just using know statistics of the world to show what might be there.

That's not how floating point math works? At least not for standard floats (IEEE 754), and except for very large integers (near 2^m, where m is the mantissa of the FP type). Floats have an exact representation for integers within their mantissa range -- i.e., '18' is exactly the same as '18.0000'.

They're correct when it comes to scientific fields - the number of significant figures is important, so 18.0 and 18.000 really do mean different things.

Sure, of course... I guess they just gave a poor example of what they were trying to express.

I dont think you mean for floating point, but for mechanical tolerances. Many times, you dont want to pay an extra $50,000 for the 5 digits of precision... but sometimes you do. Shitty system if it automatically messed up all your part tolerances.

You probably want go with 0.1+0.2 != 0.3

I would say that it's like pixel's RGB at address 1x1 is 0-0-0 and pixel at address 1x2 is 0-0-2 and squeezing between them a pixel with color 0-0-1 (averaging the two values near it)(assuming doing this on a image that has 1 pixel height and e.g. 2 pixes width; so that the new image would be would be:

1x1 0-0-0 (original)

1x2 0-0-1 (made up)

1x3 0-0-2 (original)

What you're describing is relatively straightforward (bi)linear interpolation. It is worth noting that even at this relatively simple level, going with bicubic interpolation instead will usually give you nicer results, except in cases where the hard edges in the image are only horizontal or vertical.