Algorithms to “anti-alias” (or somehow improve) binary 1-bit drawings and fonts

Question

These days anti-aliasing uses gray scale pixel values on displays with high pixel counts.

I'd like to take a step back in time and learn what is available for anti-aliasting or at least improving the quality of curved lines and fonts drawn on bindary (on/off) 1-bit displays, such as these low cost OLED displays shown below.

I don't know if the procedure would be called anti-aliasing, or something else. Ideally an explanation or a link/reference to a mathematical procedure or algorithm would be most helpful, or if something exists in Python, that would be great as well.

PIL, the Python Image Library is wonderful but I have a hunch it is mostly useful for gray scale or RGB continuous tone images rather than binary 1-bit images.

See for example the question Drawing multilingual text using PIL and saving as 1-bit and 8-bit bitmaps, where PIL turned this gray scale image:

into this 1-bit binary image:

and similar things would happen if I drew circles in PIL and then converted to 1-bit. The antialiasing is designed for continuous tone display, and so the subsequent thresholding just makes a mess.

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Example of a 1-bit binary OLED display (128x64 pixels) cropped from this image from the AdaFruit Page Monochrome 0.96" 128x64 OLED graphic display, Product ID: 326.

Answer 1

PIL's Image.convert function performs dithering by default when you convert the image to 1-bit—not simply thresholding. That's what creates the noise along the edges of the shapes; the antialiasing isn't the problem. Unfortunately, at such a low resolution, the dithering does more harm than good.

I grabbed your original image from your other question and did a straight threshold at 128, and it comes out looking a lot better:

That's probably about as good as you'll be able to do on a low-resolution 1-bit display, absent a bitmap font that's specifically designed for those circumstances.

In PIL this operation can be implemented using Image.point, as follows:

image1Bit = image8Bit.point(lambda x: 0 if x < 128 else 1, mode='1')

Answer 2

Have a look at Improved Alpha-Tested Magnification for Vector Textures and Special Effects [Gre07] (pdf). The gist of their method is to store distance field information in the font texture instead of bitmap glyphs. This information is then used to build much higher quality up-scaled versions of the glyphs. They even describe a rendering path without programmable shading that gives ok results.

Answer 3

This is just a supplementary answer based on the accepted answer, using the original 64x128 pixel images instead of the zoomed-in interpolated screen shot.

imtype = "RGB": (true RGB)

imtype = "L": (grayscale 8-bit)

imtype = "1": (binary 1-bit, threshold = 128)

imtype = "1": (binary 1-bit, threshold = 80)

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Here are screenshots zoomed-in for those without their glasses:

(screenshot of 1-bit, threshold = 128)

(screenshot of 1-bit, threshold = 80)

from PIL import Image, ImageDraw, ImageFont
w_disp   = 128
h_disp   =  64
fontsize =  32
text     =  u"你好!"

imageRGB  = Image.new("RGB", (w_disp, h_disp))
draw      = ImageDraw.Draw(imageRGB)
font      = ImageFont.truetype("/Library/Fonts/Arial Unicode.ttf", fontsize)
w, h      = draw.textsize(text, font=font)

draw.text(((w_disp - w)/2, (h_disp - h)/2), text, font=font)

imageRGB.save("NiHao! " + "RGB" + ".bmp")

image8bit = imageRGB.convert("L")

image8bit.save("NiHao! " + "L" + ".bmp")

threshold = 80
image1bit = image8bit.point(lambda x: 0 if x < threshold else 1, mode='1')
image1bit.save("NiHao! " + "1" + " threshold = " + str(threshold) + ".bmp")

threshold = 128
image1bit = image8bit.point(lambda x: 0 if x < threshold else 1, mode='1')
image1bit.save("NiHao! " + "1" + " threshold = " + str(threshold) + ".bmp")

Answer 4

In the olden days, for things like fonts, they didn't do antialiasing. They generally hand made bitmap fonts because it generated the clearest, easiest-to-read results.

That said, you could look into error diffusion, halftoning or other dithering techniques for changing continuous-tone images into 1-bit images. These are techniques for reducing the bit depth of images.