How Is Image Dithering Computed?

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In summary, dithering is a technique used to create the illusion of a larger range of colors from a limited color palette by diffusing the quantization error of a pixel to neighboring pixels. This is done using algorithms such as Floyd-Steinberg, which diffuse the error in a specific pattern to ensure that the average color in the image remains close to the original. This process is necessary because colors have their own "space" and a limited palette may not have an exact representation of a certain color, such as purple, which is a mixture of red and blue. Dithering helps to create a more accurate representation of colors in images.
  • #1
peter.ell
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After reading about how dithering works for created the sense of a larger range of colors from a small color palette, it makes sense how it works, but how in the world do computers figure out how to dither an image so that it looks correct to us?

Is the fact that blue and red combined create the sense of purple just programmed, or how does it come about so that an image capture with no dithering gets dithered to correctly give the impression of certain colors?

Thank you so much!
 
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  • #2
Colors have their own "space", so given a limited palette you can find the closest color just like you'd find the closest point in any other space.

What dithering does is diffuse the quantization error (how far off it is from the original) of a pixel to the neighboring pixels so that the average over an area of the image remains close to the original.

So for example Floyd–Steinberg diffuses the error to neighboring pixels like this:
[0,0,0]
[0,0,a]
[b,c,d]

Where a = 7/16, b = 3/16, c = 5/16, d = 1/16.

You'll also notice that it only diffuses the error to the bottom right, thus leaving already quantized pixels alone when you process the image from left-to-right and top-to-bottom.

Take the example of purple being dithered to a palette containing only Red and Blue:
Purple Value: (255,0,255)

By definition purple is a mixture of red and blue. Our palette doesn't have (255,0,255) so a single pixel can not represent it exactly, so we diffuse Red (255,0,0) and Blue (0,0,255) throughout space so that on average it comes out looking purple.
 
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  • #3
DavidSnider said:
Colors have their own "space", so given a limited palette you can find the closest color just like you'd find the closest point in any other space.

What dithering does is diffuse the quantization error (how far off it is from the original) of a pixel to the neighboring pixels so that the average over an area of the image remains close to the original.

So for example Floyd–Steinberg diffuses the error to neighboring pixels like this:
[0,0,0]
[0,0,a]
[b,c,d]

Where a = 7/16, b = 3/16, c = 5/16, d = 1/16.

You'll also notice that it only diffuses the error to the bottom right, thus leaving already quantized pixels alone when you process the image from left-to-right and top-to-bottom.

Take the example of purple being dithered to a palette containing only Red and Blue:
Purple Value: (255,0,255)

By definition purple is a mixture of red and blue. Our palette doesn't have (255,0,255) so a single pixel can not represent it exactly, so we diffuse Red (255,0,0) and Blue (0,0,255) throughout space so that on average it comes out looking purple.

Thank you. I appreciate your answer, it was very helpful.

All the best!
 

Related to How Is Image Dithering Computed?

1. How does image dithering work?

Image dithering is a technique used to simulate a larger range of colors than what is actually available in an image. It does this by breaking up the pixels of the image into multiple smaller dots of different colors, giving the illusion of a wider range of colors.

2. What is the purpose of image dithering?

The purpose of image dithering is to reduce the visible banding or abrupt transitions between colors in an image. It can also improve the overall quality and perceived sharpness of the image.

3. How is the size of the dithering pattern determined?

The size of the dithering pattern is determined by the resolution of the output device. For example, a printer with a higher resolution will require a smaller dithering pattern compared to a lower resolution printer.

4. What are the different types of dithering algorithms?

There are several types of dithering algorithms, including ordered dithering, error diffusion dithering, and random dithering. Each algorithm uses a different approach to break up the pixels of an image into smaller dots.

5. Can image dithering be used for all types of images?

Yes, image dithering can be used for any type of image, including photographs, digital art, and graphics. However, the effectiveness of dithering may vary depending on the complexity and colors present in the image.

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