You are claiming it’s the best possible. Box filter is simply not the best possible, and this fact is well understood and documented.
You can relax your claim to say it’s good enough for what you need, and I won’t disagree with you anymore. Personally, I’m sensitive to visible pixelation, and the Box Filter will always result in some visible pixelation with all 2D vector graphics, so if you really care about high quality rendering, I’m very skeptical that you really want Box filtering as the ideal target. Box filter is a compromise, it’s easier & faster to compute. But it’s not the highest quality. It would be good to understand why that’s the case.
* Edit to further clarify and respond to this:
> With the analytical coverage calculation you can have perfect font/text rendering, perfect thin lines/shapes and, by solving all the areas at once, no conflating artifacts.
You cannot get perfect font or text rendering with a Box filter, and you will get some conflating artifacts. They might be very slight, and not bothersome to most people, but they do exist with a Box filter, always. This is a mathematical property of Box filtering, not a subjective claim.
If you have two non-overlapping shapes of the same color covering the plane and use a box filter on the first shape to sample a pixel on the boundary, and then use the same box filter on the second shape, and then composit them with alpha blending, you get a conflation artifact along the boundary where the background bleeds through.
But if you use the fact that the shapes are non-overlapping and sum their contributions instead, the artifact disappears, while still using the same box filter.
You can see this with a single sharp edge, it doesn’t need to involve multiple polygons, nor even vector rendering, it happens when downsampling images too.
Can we make a magical adaptive filter which resembles box filter for half-planes, a tent filter for thin lines, Mitchell-Netravali or oblique projection for natural images, and Gaussian when filtering images for which high frequency detail is not important? Perhaps, but that feels like advanced research, and also computationally expensive. I don't think you can claim "perfect" without backing it up with human factors data really demonstrating that the filtered images are optimum with respect to perceived quality.
All of this "filtering" is variations on adding blur. In fact the article extends the technique to deliberately blur images on a larger scale. When we integrate a function (which could be a color gradient over a fully filled polygon) and then paint the little square with a solid "average" color that's also a form of blurring (more like distorting in this case) the detail.
It is notable that the examples given are moving, which means moire patterns and other artifacts will have frame-to-frame effects that may be annoying visually. Simply blurring the image takes care of that at the expense of eliminating what looks like detail but may not actually be meaningful. Some of the less blurry images seem to have radial lines that bend and go back out in another location for example, so I'd call that false detail. It may actually be better to blur such detail instead of leaving it look sharper with false contours.
Conceptually, what we’re doing is low-pass filtering, rather than blurring, so I wouldn’t necessarily call filtering just “adding blur”, but in some sense those two ideas are very close to each other, so I wouldn’t call it wrong either. :P The render filtering is a convolution integral, and is slightly different than adding blur to an image without taking the pixel shape into account. Here the filter’s quality depends on taking the pixel shape into account.
You’re right about making note of the animated examples - this is because it’s easier to demonstrate aliasing when animated. The ‘false detail’ is also aliasing, and does arise because the filtering didn’t adequately filter out high frequencies, so they’ve been sampled incorrectly and lead to incorrect image reconstruction. I totally agree that if you get such aliasing false detail, it’s preferable to err (slightly) on the side of blurry, rather than sharp and wrong.
All this comes down to the projected pixels not being nearly as square as one might think (maybe that’s on purpose), though do note that squares are not the ideal shape of a pixel in the first place, for the same reason box filtering isn’t the best filter. If your pixel has sharp edges, that causes artifacts.
Take a look at the pixel-close-up comparisons in the projection shoot-out: https://www.projectorcentral.com/Projector-Resolution-Shooto...
Notice how all of them are visibly blurrier than the source image, and even that all of them have visible aliasing.
Also just for fun, check out this interesting video showing what DMD mirrors look like under a microscope: https://youtu.be/KpatWNi0__o