Visualizing RGB, take two - Update

When I posted my 'Visualizing RGB, take two' article last month, an anonymous commenter going by the name Full-size had a good suggestion - I should use a form of error diffusion to better hide the pixel errors when selecting the colours to use. Within a couple days I had this implemented, and wow does it make an improvement! Unfortunately, between school and needing to reinstall Windows I never ended up posting the results. So, three weeks late, here they are.

Results for Barcelona image

As you may recall, the goal of the algorithm was to transform a source image into a 4096x4096 version that uses every RGB colour exactly once. As a reminder, here is the original image, and what my algorithm previously did with it:

Original

Result from previous version

Now, take a look at how it looks using a form of error diffusion (with error terms capped at 60):

(Full 50 meg png available here). Much nicer to look at! Take a look at the sky in particular - where it used to go psychedelic trying to deal with all that near white, large portions now end up simply turning into a uniform gray. The new version is worse in a couple spots (e.g. the wheel wells of the car), but overall I think it is hugely improved. Now, I wonder how the new version would do on a harder image?

Results for the Mona Lisa

As promised, I am also posting the results of running this algorithm on an image of the Mona Lisa. This is an especially difficult image, because the colour palette is very limited - notice the lack of blue in particular. First, let's take a look at the original, and the result from the previous version of the code:

Original

Result from previous version

Ouch. Poor Lisa. Still, let's forge on and see how the new version does, shall we?

(Full 50 meg png available here). Considerably better overall, although the colour burning on the forehead and neck is pretty ugly to look at. Still, considering we are trying to use every RGB colour once, I think the results are quite decent.

Postscript

The code to achieve this is in the same place as before, updated on GitHub. Right now you have to change the source code to edit the error diffusion cap, sorry - but feel free to fix that!