The current trend of using noise in digital art is associated with many terms such as glitch, databending, datamoshing, pixel shifting, pixel sorting and others.
We experience digital noise routinely in modern life. The way your Netflix pixelates occasionally when too many in your apartment building is using the internet. The sound of digital noise from the occasional random bit drop from your Spotify stream. This is digital noise, or broadly termed a “glitch”.
Digital glitches occur when the signal is altered in some way that affects how it is rendered for the user. Often a glitch can just make an image unviewable. However, there are several common signal processing and telecommunications ways noise enters the system and some of my favorite glitch art uses the original process to artistically create glitches.
Pixel sorting is a good example for how making glitch art works. Pixels are 3 sets of numbers, from 0 to 255, which represent color values for Red, Green, and Blue LEDs in a digital display.
Organizing those pixels by greatest to largest is one way to “glitch” an image.
Let’s say the first 3 pixels of a .jpg file are such: (34, 125, 179), (212, 55, 0), (138, 20, 36)
A pixel sorting glitch would re-sort them as such (34, 125, 179), (138, 20, 36), (212, 55, 0).
Example below, pixel sorted image on left, original image on right.
This github repository is my go-to for most glitch projets: https://github.com/GlitchTools
It uses the python programming language to mimic the effects of ‘natural’ glitches such as the pixel sort and also some old school methods like a wordpad copy/paste method.
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