top of page

Image Dithering

Image dithering involves altering the appearance of black and white images by changing the arrangement of pixels to make it seem as though there are more variations in the shades of colors.

In Relativity if you go to Imaging . . . Imaging Profiles . . . in the Native Imaging Engine Options sections, you'll see that you can select between 8 different dithering algorithms. These are employed when rendering documents as the black and white tiff images that the database works with.

The Purdue College of Engineering has a guide on its site which shows the basic differences between these dithering options. A threshold image is the simplest kind, and is one in which a pixel is compared against a fixed threshold. As you can see it's largely a black and white image, and there isn't a lot of detail.

Clustered dithering produces an image by varying the size of dots - light areas are filled by small dots, and in dark areas large dots are close to overlapping with adjacent dots.

Dispersed dithering, of which a Bayer matrix is a particular sort, has a distinctive cross hatched pattern and is based on the precise placement of pixels.

Floyd-Steinberg dithering uses a technique called error diffusion, which has a tendency to produce images with sharper edges. For this reason it may be the best dithering technique for producing readable text.

The above images are used to make very clear the distinctions between the various techniques. kCura shows samples of images of text and a diagram produced with various dithering algorithms on this its site where the differences are much subtler.

bottom of page