# Revisiting Dithering

It’s been a while since my last post and an even longer while since the last one about image processing. However I’ll just catch up, where I left off. The last post in this category was about image dithering. A common usage of dithering is the conversion of a grey scale picture to a picture consisting just out of black and white pixels, saving space and also useful for some print media. On a further note dithered images have a nice visual style, I would call something like dollar bill style.

Conventional Dithering methods however also introduce artifacts to pictures and of course they also lose sharpness. So there’s the question, if one could approve on those conventional dithering methods in order to reduce artifacts and increase sharpness.
If you’re mathematically minded, you might also be worried, that conventional dithering is not rotationally invariant, which I assume is due to the fact that the dithering kernel is not symmetric and the way one iterates over the picture. Rotationally invariant basically means, that the output of a method doesn’t change, when you rotate your input image, which is of course a desirable trait.

So what about taking some inspiration from physics? After all physical laws of motion are rotationally invariant. Lattice Boltzmann methods are a way of computing dynamics in fluids. And they have been successfully employed for dithering by Hagenburg et al . The paper regarding this research also served as my main source for the method and its implementation, even though my specific implementation might differ in some details.
I’ll call the method Lattice Boltzmann Dithering during the rest of the article, as I didn’t find any other handy name for it.

## The Idea Behind Lattice Boltzmann Dithering

The idea behind the method is to model the picture like a fluid with particles in it. In this case the grey values are the particles. A pixel that is black (with a grey value of 0) has so to say no particles in it. Those particles dissipate in the fluid over time. The time is modelled as discrete steps. They dissipate according to the following laws during each time step:

• If a pixel has a value v greater than 1, where 1 is the maximal grey value, (v – 1) values are distributed amongst the neighbors of the pixel and the value of original pixel becomes 1.
• If a pixel’s value v is lower than a minimal treshold, the user can set, v is distributed over its neighbors and the pixel itself becomes 0.
• Otherwise a fraction of the pixels value is distributed to each neighbor, that has a larger value, while being smaller than 1. This fraction is subtracted from the pixel itself.

I hope you see, how with this laws pixels with a high value (many particles) attract more values. After some time steps you should more or less only be left with black and white pixels this way.

And the whole process is stopped, when the difference from one time step to the next one becomes sufficiently small. And that’s about it… Not really that complicated, if you ask me! 🙂
Of course the paper goes more into detail about the theoretical background and proves some things about the method. If you’re interested, you should absolutely read it.

## Implementation of Lattice Boltzmann Dithering

And now let’s come to the part you all waited for… The implementation. First I’ll implement a method for the dissipation of the values from each pixel, which will e executed in each time step to get the next distribution of values.

One thing to keep in mind, when working with images is, that they have borders. And as you don’t want to dissipate the values across borders or worse access a part of the image, that isn’t there. So you have to treat the pixels at the borders differently. In the case of this method, I always check first, if a neighboring pixel is there or not.

dissipatePixel <- function(img, minimalTreshold = 0.01){

imgAtNewStep <- matrix(c(0), ncol = ncol(img), nrow = nrow(img))

for(i in seq(nrow(img))){
for(j in seq(ncol(img))){
if(img[i,j] > 1 || img[i,j] < minimalTreshold){

toDissipate <- img[i,j]

if(img[i,j] > 1){
toDissipate <- toDissipate - 1
}

dissipated <- 0

if( i > 1){
imgAtNewStep[i - 1,j] <- imgAtNewStep[i - 1,j] + 1.0 / 9.0 * toDissipate
dissipated <- dissipated + 1.0 / 9.0 * toDissipate
}

if( j > 1){
imgAtNewStep[i,j - 1] <- imgAtNewStep[i,j - 1] + 1.0 / 9.0 * toDissipate
dissipated <- dissipated + 1.0 / 9.0 * toDissipate
}

if(i < nrow(img)){
imgAtNewStep[i + 1,j] <- imgAtNewStep[i + 1,j] + 1.0 / 9.0 * toDissipate
dissipated <- dissipated + 1.0 / 9.0 * toDissipate
}

if(j < ncol(img)){
imgAtNewStep[i,j + 1] <- imgAtNewStep[i,j + 1] + 1.0 / 9.0 * toDissipate
dissipated <- dissipated + 1.0 / 9.0 * toDissipate
}

if( i > 1 && j > 1){
imgAtNewStep[i - 1,j - 1] <- imgAtNewStep[i - 1,j - 1] + 1.0 / 36.0 * toDissipate
dissipated <- dissipated + 1.0 / 36.0 * toDissipate
}

if( i > 1 && j < ncol(img)){
imgAtNewStep[i - 1,j + 1] <- imgAtNewStep[i - 1,j + 1] + 1.0 / 36.0 * toDissipate
dissipated <- dissipated + 1.0 / 36.0 * toDissipate
}

if( i < nrow(img) && j > 1){
imgAtNewStep[i + 1,j - 1] <- imgAtNewStep[i + 1,j - 1] + 1.0 / 36.0 * toDissipate
dissipated <- dissipated + 1.0 / 36.0 * toDissipate
}

if( i < nrow(img) && j > ncol(img)){
imgAtNewStep[i + 1,j + 1] <- imgAtNewStep[i + 1,j + 1] + 1.0 / 36.0 * toDissipate
dissipated <- dissipated + 1.0 / 36.0 * toDissipate
}

## add the non dissipated amount to the same pixel in next time-step
imgAtNewStep[i,j] <- imgAtNewStep[i,j] + (img[i,j] - dissipated)
}else{

dissipated <- 0
currentPixel <- img[i,j]

if( i > 1 && img[i - 1,j] > img[i,j] && img[i - 1,j] < 1){
imgAtNewStep[i - 1,j] <- imgAtNewStep[i - 1,j] + 1.0 / 9.0 * currentPixel
dissipated <- dissipated + 1.0 / 9.0 * currentPixel
}

if( j > 1 && img[i,j - 1] > img[i,j] && img[i,j - 1] < 1){
imgAtNewStep[i,j - 1] <- imgAtNewStep[i,j - 1] + 1.0 / 9.0 * currentPixel
dissipated <- dissipated + 1.0 / 9.0 * currentPixel
}

if(i < nrow(img) && img[i + 1,j] > img[i,j] && img[i + 1,j] < 1){
imgAtNewStep[i + 1,j] <- imgAtNewStep[i + 1,j] + 1.0 / 9.0 * currentPixel
dissipated <- dissipated + 1.0 / 9.0 * currentPixel
}

if(j < ncol(img) && img[i,j + 1] > img[i,j] && img[i,j + 1] < 1){
imgAtNewStep[i,j + 1] <- imgAtNewStep[i,j + 1] + 1.0 / 9.0 * currentPixel
dissipated <- dissipated + 1.0 / 9.0 * currentPixel
}

if( i > 1 && j > 1 && img[i - 1,j - 1] > img[i,j] && img[i - 1,j - 1] < 1){
imgAtNewStep[i - 1,j - 1] <- imgAtNewStep[i - 1,j - 1] + 1.0 / 36.0 * currentPixel
dissipated <- dissipated + 1.0 / 36.0 * currentPixel
}

if( i > 1 && j < ncol(img) && img[i - 1,j + 1] > img[i,j] && img[i - 1,j + 1] < 1){
imgAtNewStep[i - 1,j + 1] <- imgAtNewStep[i - 1,j + 1] + 1.0 / 36.0 * currentPixel
dissipated <- dissipated + 1.0 / 36.0 * currentPixel
}

if( i < nrow(img) && j > 1 && img[i + 1,j - 1] > img[i,j] && img[i + 1,j - 1] < 1){
imgAtNewStep[i + 1,j - 1] <- imgAtNewStep[i + 1,j - 1] + 1.0 / 36.0 * currentPixel
dissipated <- dissipated + 1.0 / 36.0 * currentPixel
}

if( i < nrow(img) && j > ncol(img) && img[i + 1,j + 1] > img[i,j] && img[i + 1,j + 1] < 1){
imgAtNewStep[i + 1,j + 1] <- imgAtNewStep[i + 1,j + 1] + 1.0 / 36.0 * currentPixel
dissipated <- dissipated + 1.0 / 36.0 * currentPixel
}

## add the non dissipated amount to the same pixel in next time-step
imgAtNewStep[i,j] <- imgAtNewStep[i,j] + (img[i,j] - dissipated)
}
}
}

return(imgAtNewStep)

}


Done that! Now the easy part…
But as the implementation in R with loops is incredibly inefficient I’ll just run 50 time steps this time. I will however implement this method at some later point in C++, where loops aren’t inefficient. This will also serve as a good example on how to integrate C++ code in R.

lbDithering <- function(img, epsilon, minimalTreshold){

i <- 0
while(TRUE){

imgAtNewStep <- dissipatePixel(img = img, minimalTreshold = minimalTreshold)

#if(norm(imgAtNewStep - img, type = "2") < epsilon){
if(i >= 50){
return(imgAtNewStep)
}else{
img <- imgAtNewStep
}
i <- i +1
}
}


## Usage

Now let’s reap the fruits of our coding and test the method on a picture I’ve selected. 🙂

birb <- imageBWFromJpeg("birb.jpg")
birbDithered <- lbDithering(birb@current, epsilon = 20, minimalTreshold = 0.05)
writeJPEG(round(birbDithered), "birbDithered.jpg")


Let me show you the resulting Jpeg:

Isn’t that beautiful? As comparison the original:

And a conventionally dithered version:

birbDithered2 <- errorDiffusiondDithering(birb, method = "mea")
writeJPEG(birbDithered2@current, "birbDithered2.jpg")


You can see more structure in the one created with Lattice Boltzmann Dithering, don’t you? And also you can better understand how light and shadows are distributed.
So overall a pretty nice algorithm I would say! Although I like the dollar bill look of the conventional one as well.

So that’s it for now! Until soon…

Yours, David

## Availability Of The Code

You can access a maintained version of the code for the color spaces in my GitHub repository Raspository under R/imageConversion.R.

And well… You might’ve noticed, that I used some of my methods/classes a bit different than the last few times. Having some distance from coding this stuff I noticed I have to change some things about it. Although I don’t know yet, if I’ll make a blog post about it or not. Do you wanna read one?