(This article was first published on Statistic on aiR, and kindly contributed to R-bloggers)
Here I implemented in R some dithering algorithms:
- Floyd-Steinberg dithering
- Bill Atkinson dithering
- Jarvis-Judice-Ninke dithering
- Sierra 2-4a dithering
- Stucki dithering
- Burkes dithering
- Sierra2 dithering
- Sierra3 dithering
For each algorithm, I wrote a 2-dimensional convolution function (a matrix passing over a matrix); it is slow because I didn't implemented any fasting tricks. It can be easily implemented in C, then used in R for a faster solution.
Then, a function to transform a grey image in a grey-dithered image is provided, with an example. The library rimage was used for loading and displaying images (see the other post R is a cool image editor).
These function can be easily re-coded for a RGB image.
Only the first code is commented, 'cause they're all very similar.
library(rimage)
y <- read.jpeg("valve.jpg")
plot(y)
plot(normalize(grey2FSdith(rgb2grey(y))))
plot(normalize(grey2ATKdith(rgb2grey(y))))
plot(normalize(grey2JJNdith(rgb2grey(y))))
plot(normalize(grey2S24adith(rgb2grey(y))))
plot(normalize(grey2Stucki(rgb2grey(y))))
plot(normalize(grey2Burkes(rgb2grey(y))))
plot(normalize(grey2Sierra2(rgb2grey(y))))
plot(normalize(grey2Sierra3(rgb2grey(y))))
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