Pixualization: Glitch art and Data visualization

Ernesto Peña,Omar Juárez,Teresa M Dobson

doi:10.51358/id.v14i1.502

Ernesto Peña, Omar Juárez + Show 1 more

Open Access

https://doi.org/10.51358/id.v14i1.502

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Abstract

Due to their trans-modal affordances, techniques mainly used for the production of glitch art such as pixel sorting and data-bending are potential data visualization tools that can be effectively adapted to the representation of abstract relations. To demonstrate this assertion, we present a method intended for the visualization of data through these techniques. The method presented here consists in sorting the composite pixels of greyscale images to make evident relationships between pixels that otherwise would be impossible to perceive. Here, we use this method to arrange an array of images in a very specific order, but this is only one of the potential applications that this method could have.

Highlights

Due to their trans-modal affordances, techniques mainly used for the production of glitch art such as pixel sorting and data-bending are potential data visualization tools that can be effectively adapted to the representation of abstract relations
The approach to pixel sorting that glitch artists have usually engaged in entails the following: 1) the identification of each individual pixel by the retrieval of its color information and 2) the rearrangement of the pixels
The process of pixel sorting is done through specialized software such as Processing 2 (Fry & Reas, 2004), but due to the acceptance that this practice has had and because many glitch artists – often programmers – (e.g., Asendorf, n.d.) have made their own protocols available, there is an increasing number of communities of informed practitioners (e.g., “/r/ pixelsorting,” n.d.) and pixel redistribution behaviours

Summary

Glitch art techniques

Pixel sorting is only one of the many practices that glitch artists engage in for the intervention of digital images Colors in hexadecimal format are expressed in 3 bytes of 2 bits that account for the red, green and blue color layers, known as channels. Each one of these channels contains not colour, but luminance information scaling from white to black. Modifying the raw data in ASCII format by adding lexical items to the code results in apparently random colour information: for instance, the addition of the lexical item “Dog” in a particular position would generate a single RGB color pixel with a hexadecimal colour code of 446f67 (see Table 1). Transcodification between ASCII and hexadecimal notation can be accomplished through many open access standalone and webbased applications

Data visualization

How black or white is “Black and White”

The method

Discussion and future endeavours