I found an excellent color reduction algorithm that can be used to produce VB artwork from existing images.
From: http://www.moonflare.com/code/scolorq/
Spatial color quantization is a novel technique for decreasing the color depth of an image, first described in the paper “On Spatial Quantization of Color Images” by Jan Puzicha, Markus Held, Jens Ketterer, Joachim M. Buhmann, and Dieter Fellner, researchers at the University of Bonn in Germany. It combines palette selection and dithering with a simple perceptual model of human vision to produce superior results for many types of images.
[…]
scolorq, standing for “spatial color quantization”, is a faithful implementation of the highest-quality algorithm described in the paper. Its results have richer colors and often better detail than the widely-used median cut and octree algorithms supplied in bitmap editors like Adobe Photoshop, Paint Shop Pro, and the GIMP, particularly when reducing to very low color depths, like 4, 8, or 16 colors, as the sample images below demonstrate.
Attached is a sample image, before and after. The only problem I can see is that it is a bit slow (it took an estimated 45 seconds to 1 minute for a single 384×224 image on my 2.4GHz P4). However, some speed gains might be had by converting the algorithm to work on an 8-bit grey-scale image (and possibly incorporate the grey-scale conversion) rather than a 24-bit, full-color image. Perhaps some algorithm other than the “highest-quality” one implemented could be used, without incurring a noticeable quality loss.
I would also like to see some kind of stereo optimization incorporated, i.e. find the differences (beyond some threshold) between the images of a stereo pair and quantize one image and the differences. This would reduce disparity artifacts caused by dithering.
One thing to notice about the after shot below: the shades chosen are not equally spaced. On the VB, this will have to be compensated for with appropriate BRTx register settings.
Attachments:
That looks nice, but I think there’s too much detail/random noise… I doubt you’d be able to convert that to chars/bgmap (or it’d fill up almost all the Char RAM).
I convert my images in Photoshop, and if I do it with diffusion, it looks nice, but most large images run out of chars… but with pattern, there’s usually quite a few chars that are identical, which of course is good for us. Here’s an example added to the right of yours.
DogP
