To anti-alias, or not to anti-alias? Hit the button or leave it alone?

[Tglad]

Here is Syntopia's Julia set example using normal supersampling at 2x2 on the left and 16x16 on the right

lots of detail but fractal fades away as you get more accurate, as n increases in nxn supersampling.

Since my method calculates the shade by just scaling the number of supersample points by n^-dimension rather than n^-2, and since a Julia set has constant dimension, my method does nothing more than make the image darker on this fractal.
In this case that is a problem because you lose detail if you make it darker and clamp to values beyond black (left image below, 16x16), but you can always scale the darkness down as in the right image:


I find the fractal dimension per pixel in the image using simple box counting, here is an image of the Mandelbrot coloured by the dimension at each pixel, for 20 iterations and 2000 iterations. The paler colour is lower dimension:

[lycium]

You should bear in mind that displays don't have linear brightness response, i.e. you have to apply gamma correction after you've done your AA.

[Syntopia]

This box-counting thing could be interesting to for making a automated journey through the Mandelbrot set. You probably want to zoom in on regions with a dimension somewhere between void and massive.

Btw, on problem with method is that it is not obvious how you handle supersamples of different color. (The same of course applies to my DE-based renderings).

You should bear in mind that displays don't have linear brightness response, i.e. you have to apply gamma correction after you've done your AA.

The Fragmentarium image was Gamma corrected, which actually helps a lot in bringing out detail (and will reduce anti-aliasing artifacts, such as roping on straight lines).

Btw, I used 16 samples per pixel in my image, not 16x16 (not that there is much gained by going much higher in this case).

[chaos_crystal]

Hum...I'm pretty much lost, not being very knowledgeable about much of the terminology and techniques discussed here. I doubt that my computer could even manage the processes you speak of. It's slow, and old, only 1 processor and 2 GB RAM. Anyway, here (see link) is a fractal image I've had for a while, not knowing for certain just what to do with it. I did try rendering it at 4 x original size and then antialiasing 3:1 (the tentative approach). That didn't help much. I suspect this is one of those that would have to be supersampled at 10 to 20 times actual size and then downsampled. My computer would probably freeze and roll over and die,    LOL.

This is the original file complete with the "ugly" bits I don't intend to use:

http://img69.imageshack.us/img69/629/alittlenightmusicdemo.png

Here is a zoomed in portion:

http://img571.imageshack.us/img571/5930/alittlenightmusic1.png

and here is a portion showing my pathetic efforts at antialiasing  which show the exact problem I mention in my original post of this thread...

http://img191.imageshack.us/img191/1931/alittlenightmusic1aa.png

There are many, many small delicate lines running everywhere, I've never seen a fractal like this one before (no idea how I conjured it up either!) and never since... Tglad has already said, "if you are rendering details which have no area by definition, e.g. lines, points, fractals like Koch curve, menger carpet, Sierpinski triangle etc then the problem is that more supersampling just dilutes the pixels until you can't see the shape at all!" That could be what's happening here. But I am unsure just how to fix such problems. Freeware such as Fractal Explorer and Tierazon, etc. don't have the DE feature or other options that might help.