There is research about perception of physically printed lines, done by e.g. print photography companies decades ago.
There is plenty of formal signal processing analysis of the aliasing artifacts at different angles created by grids of pixels.
The ImageMagick folks did a bunch of experimentation about resampling filters as used in arbitrary transformations of existing raster images. https://www.imagemagick.org/Usage/filter/nicolas/ – of course what looks best depends significantly on (subjective) preferences and on what the source image is.
So based on this research, what reconstruction filter and gamma curve should I use to render text so it looks good over a range of fonts? :)
Or, perhaps a better posed question. What research-informed model will accurately predict the results of a user study that presents various renderings of antialiased lines on a modern LCD monitor and asks subjects to choose "which line is thicker" types of queries?
Also does the font have variable weight? Are you adjusting it in any other way before rendering? What context were the fonts designed for – did the font designers evaluate their appearance using any specific displays / rendering engines?
> asks subjects to choose "which line is thicker" types of queries
I think you could develop a model for “which line is thicker” given a specific target display without inordinately much trouble; you might have to tweak some parameters for matching particular displays. The harder question is “which line is the right thickness”, especially if you don’t have any correct answers to reference.
We also don’t just care about apparent line thickness but also spatial resolution, aliasing artifacts, ...
There is plenty of formal signal processing analysis of the aliasing artifacts at different angles created by grids of pixels.
The ImageMagick folks did a bunch of experimentation about resampling filters as used in arbitrary transformations of existing raster images. https://www.imagemagick.org/Usage/filter/nicolas/ – of course what looks best depends significantly on (subjective) preferences and on what the source image is.