if you jack the minHueCols param up to 4096 or 8192, it does much better in many cases of gradients but also much slower. it's certainly not a complete hands-off quantizer. the best results i've found for 0-config quantizarion is Wu's Color Quantizer v2 [1] implementation [2]
However, Wu's algorithm requires preprocessing R * G * B * A * int array, which is 16GB of RAM if done at full RGBA quality, so in practice all implementations have to drop alpha and/or heavily posterize colors.
pngquant the same goal — subdivides RGBA (hyper)cube to minimize variance in each section — but does it with much less memory and can do it at full quality.
Posterization of input is my pet peeve, as it gives images slightly banded and grainy look that we associate with "256-color" images (since VGA only ever supported 6-bit per gun), and presume 256 colors are never enough for photorealistic look — but it often is, and people who use e.g. TinyPNG service think it's magic.
IMO the best quantisation tools are bright183 (proprietary, by Epic for Unreal shared palettes) and neuquant. Wu unfortunately suffers from banding artifacts very badly for a low amount of colours with dithering off, the banding after applying bright looks much more acceptable.
neuquant is quite good for photos but does strange stuff with graphics and low color counts. in fact, the pool table example that's used to show how good it performs, RgbQuant does better on the details and with fewer artifacts at the expense of bsckground smoothness/banding. compare for yourself http://members.ozemail.com.au/~dekker/NEUQUANT.HTML
[1] http://www.ece.mcmaster.ca/~xwu/cq.c
[2] http://nquant.codeplex.com/