Project Inquiry

This project seeks to ask what happens to an image as it falls apart? Starting with a flamingo photograph from the internet, I incrementally degraded its image quality before reducing it to a series of geometric shapes. The image file was then processed through Audacity where I applied various filters that slowly destroyed the data until figure became unfathomable. The image was treated as something unstable, that is prone to corruption in the same way any digital file can be. I compiled the outputs into a gif that plays the degradation in a loop, so the flamingo dissolves and reforms over and over. 

This method sits within the glitch art movement, which treats errors and data corruption not as failures but as a method of exposing the hidden structures of digital media. As an image loses quality it also loses status, becoming a degraded copy of something that once existed in a more complete form. This connects to the nostalgia for early internet and pre-digital visual culture, and the way a low quality image carries the weight of something partially forgotten. I am interested in the threshold where the image becomes unrecognisable, and what that moment reveals about the relationship between visual form, data, and memory. 

Annotated Bibliography

• In Defence of Poor Image (2012) by Hito Steyerl 

Hito Steyerl argues that the poor image, degraded through uploading, downloading, compression, and re-sharing across digital networks, is not a failed copy but a form with its own political and aesthetic value. Its low resolution and visual noise are evidence of circulation, displacement, and survival outside commercial systems rather than signs of failure.

Directly referencing this idea of image pixelation, I circulated an image through a chat channel 100 times to observe the compression artifacts not as damage pixels, but as an accumulative record of the image’s passage through networked infrastructure. 

The subsequent Photoshop-based pixel degradation and Audacity glitching experiments extend this further by subjecting the image to repeated misreading and reinterpretation by methods not intended for continuous application. The series of Audacity processed frames visualise this accumulation over time, each stage of dissolution carrying its own visual weight and aesthetics rather than simply marking a loss of information. This aligns with Steyerl’s argument that pixelation and degradation are not errors to be corrected but meaningful residues of a image’s journey through digital systems. It documents the image’s life as it travels, breaks down, and becomes something new. 

Steyerl, H. (2012) ‘In Defense of the Poor Image’, The Wretched of the Screen. Berlin: Sternberg Press, pp. 31–45.

• Black Gooey Universe (2018) by American Artist

American Artist argues that the graphical user interface (GUI) is not a neutral surface but an ideological one as it shifted from black command-line screen to white GUI encoded whiteness as the default condition of computing. Artist proposes “black gooey” as a counter-interface built on brokenness and refusal that resists the conventional smooth usability of white GUI. 

I adopted this ideology by translating the original photograph into a black and white image, and positioned blackness as the ground with a white geometric flamingo. This aligns with Artist’s argument that blackness is always already foundational to the screen. 

The image degradation process then demonstrates this idea visually as I incrementally applied audio filters in Audacity to dissolve the flamingo into its surrounding blackness, returning the image to what Artist suggests was always its basis. This was reinforced by a material observation during the process: black areas of the image were registered as a flat line in Audacity, data-less and silent, while the white figure carried all the signal. The glitches produced by misread image data also reflects Artist’s exploration of brokenness as a mode of resistance against the seamless legibility of the conventional interface. 

American Artist (2018) Black Gooey Universe, unbag. Available at: https://unbag.net/end/black-gooey-universe(Accessed: 21 April 2026).

• A Vernacular of File Formats (2010) by Rosa Menkman 

A Vernacular of File Formats catalogues the distinct visual noise produced when image files (JPEG, BMP, TIFF, GIF) are data-bent to produce a unique aesthetic signature. Menkman treats these format-specific glitches not as accidents but as a kind of material language, arguing that corruption makes visible the hidden architecture of how digital images store information.

This inspired my exploration of Audacity as a way to create deliberate errors in the encoding of images. Translating image data into an audio processor breaks down the distinction between formats, which highlights Menkman’s idea that all digital files are composed of the same underlying binary data. The degradation effect I applied is therefore not destructing but reinterpreting the image data through a mismatched system. Through this process I came to appreciate the visual aesthetics that emerges from these errors. The glitches are not noise to be corrected but information made legible when the system is failing to conceal its own workings.

Menkman also notes that the number of pixels and compression structure encoded in each format determines how corruption distributes across the image, which shaped my choice of source format (BMP and TIFF files) when preparing my images for processing. 

Menkman, R. (2010) A Vernacular of File Formats [digital prints on dibond]. Available at: https://beyondresolution.info/ABOUT (Accessed: 23 April 2026).

• Glitchometry (2012-present) by Daniel Temkin 

The Glitchometry series works extensively with geometric shapes, grids, gradients, and colour fields rather than figurative imagery, deliberately removing representational content so that the effect of each transformation is more legible. This directs viewer’s attention entirely towards the distortion of the data, how colour values shift, how block structures emerge, and how compression artefacts form patterns from the original composition. 

My project embodied this focus on geometric abstraction. By constructing the flamingo from simple white geometric shapes on a black ground rather than using a photograph, I removed the colour and gradient content of the image to focus on the noises that would emerge from the databending process. 

Temkin’s focus on how geometric forms respond to systematic transformation also inspired me to develop my degradation frames into an animation. I experimented with many different blending modes in After Effects to test how different layer order and transition effects would interact with the processed images to produce unexpected colour. Rather than treating the animation as a neutral sequence of outputs, I used the blending stage as an additional site of formal exploration, finding that certain modes generated chromatic shifts that significantly strengthened the visual intensity of the overall work.

Temkin, D. (2012) Glitchometry Series [digital c-print on Duratrans]. Available at: http://danieltemkin.com/Glitchometry(Accessed: 21 April 2026).

• Recognition by Component Theory (1987) by Irving Biederman

Biederman’s Recognition by Components (RBC) theory describes how the human visual system parses objects into volumetric primitives ‘geons’, and how recognition fails when the contours defining these components are continuously obscured. 

This framework informed the translation of my flamingo image from a photograph to a vectorised form. I reduced the flamingo to its essential geometric basis (circle, rectangle, and triangles) to focus on the threshold of geon-based recognition. The flamingo’s geometric shapes remain identifiable despite noise and distortion, until a certain threshold when the image collapses into unrecognisable fragments where the components can no longer be reconstructed, and the image ceases to be legible as a figure. 

This threshold is further complicated by the Audacity databending process, in which image data is progressively displaced across the waveform. As the white figure migrates and distorts, the spatial relationships between geons break down incrementally, demonstrating the degradation conditions Biederman identifies as critical to recognition failure. It was important to note that even in heavily distorted stages, the flamingo retained a legible wholeness in its outline, which carried sufficient symbolic resonance to sustain recognition. This suggests that cultural familiarity with an image can extend the threshold of recognition beyond what RBC’s purely structural account might predict.

Biederman, I. (1987) ‘Recognition by Components: A Theory of Human Image Understanding’, Psychological Review, 94(2), pp. 115–147. Available at: https://doi.org/10.1037/0033-295X.94.2.115.

• What is New Media? (2002) by Lev Manovich 

Manovich suggests that all digital media objects exist simultaneously on two levels: (1) a cultural layer, where images carry meaning and are interpreted by human viewers, (2) computer layer, where the same object is nothing more than numerical data processed by algorithms. Through the concept of “cultural transcoding,” Manovich proposes that these two layers continually translate into one another, and that this translation is never neutral because the logic of the computer layer continue to shape how the cultural layer is organised and understood.

This challenged me to reconsider the logics behind my degradation process. Importing an image into Audacity and applying audio filters does not damage the file in any meaningful computational sense because the program is re-encoding the numerical data according to a different set of rules. What I interpret culturally as deterioration or loss remains valid output on a computational level. Manovich’s framework makes this tension visible, where my practice imposes an analogue value system, treating legibility as integrity and noise as failure, onto a process that has no such hierarchy built into it. This prompted me to ask whether the narrative of decay I construct across iterations is something the images carry, or something I bring to them.

Manovich, L. (2002) ‘What Is New Media?’, The Language of New Media. Cambridge, MA, USA: MIT Press (Leonardo), pp. 18–55.