Concern for the protocol object has been dominated by issues of relationality, processuality and potentiality. This has drawn a picture that can be seen as reductionist insofar as these accounts position digital objects as built on something more basic, as echoes of a deeper reality – in the case of JPEG, a deeper, more fundamental code. Alternatively the reduction happens upwards rather than downwards. Objects are no more than bundles of qualities, ideas in the mind or relations – in the case of JPEG, objects are reduced to a matter of ‘media’, ‘software’ or ‘imagespace’.

For an account of the digital object that takes objects seriously – even weird objects like JPEG. I turn to Graham Harman’s ‘Object Oriented Philosophy’ as outlined in The Quadruple Object (2011). In this work – a development of earlier work (2002) and traced in  (2010) – Harman lays out what he calls an ‘ontography’, a metaphysics of all objects. The examples that Harman uses are varied – trees, cats, cotton etc. – and he is clear that for his model to be worthwhile it has to be able to account for any and all objects. My aim in this chapter is to take up that challenge and map out a fourfold account of the JPEG object.

It is important to note that the aim here is not to evaluate or critique Harman’s reading of Husserl or Heidegger or even critique his ontological model, merely to use it as a way of mapping JPEG prior to my using it as way into understanding and practically exploring distributed, digital imaging and imagining. In this chapter I outline an object-oriented account of JPEG and in the next I report on an object-oriented account of JPEG photography.

This chapter follows closely Harman’s structure in The Quadruple Object, seeking to carefully construct a coherent picture of the JPEG object in terms of four tensions, three radiations and three junctions. I seek to methodically build a metaphysics of the digital object that can not only enable us to understand the weird character and workings of protocol but also offer a way of developing a new form of object-practice.

In the first section I revisit existing accounts of JPEG in terms of the two forms of reductionism Harman calls ‘overmining’ and ‘undermining’. In the second I follow his account of Husserl and Heidegger in identifying two sorts of object: the sensual object and the real object. In the third I explore how the JPEG sensual object and the JPEG real object can be seen as each having particular qualities (giving the initial fourfold structure). In the fourth section I address how that structure can enable us to trace JPEG’s relation to issues of time, space, eidos and essence. In the fifth section I follow Harman’s account of how objects connect in order to map an object-oriented account of JPEG within the technosocial assemblage.

It’s not difficult to see JPEG in terms of issues of relations. As I will discuss in The Governmental JPEG Object chapter, the protocol is enfolded and connected with other objects with real power implications. That account can be drawn in terms of actant networks where a Latourain litany of Google engineers, Facebook patents, software and camera manufacturing cleanrooms in Taiwan connect and render actants powerful. Similarly JPEG  can be imagined in terms of its potential to structure, empower and enable scopic practices and regimes, positioning imagers rather than photographers and the stream rather than the decisive moment. And JPEG can be particularly understood as a matter of process. JPEG is, after all, a protocol that is about processing data, compressing and rendering a JFIF or EXIF file. The markers, compression algorithms and tables we will address can be drawn as process – JPEG works and withdraws because it is best seen as a process, something that happens or becomes within software.

So what are the advantages of treating JPEG as an object and more specifically an object beyond relations, potential and process? I will come on to argue that treating JPEG as an object not only makes theoretical sense but also practical sense. To follow Harman in refusing to leave the object even when dealing with its connections, to remain rooted in actuality and presence and in particular to open up the fourfold structure of the protocol-object is to take protocol seriously in its own terms, to refuse to over or undermine its specificity and so make it open to struggle as well as creative use.

Before even that however, we need to show how JPEG can be seen as an object.

One thing all of those involved in the object-oriented ontology field, itself a subset of the broader speculative realist ‘movement’, would agree is on the importance of objects. From philosophy, Timothy Morton’s “hyperobjects”, Bryant’s “subjectless object” and Harman’s “quadruple object” all, in their different ways, advocate an attention to objects and emphasise that we can approach social reality through objects. Furthermore they open up a wider conception of what counts as an object. Bryant neatly sums up the stance in his title The Democracy of Objects. This call to take objects seriously and expand our definition of the object has been taken up outside philosophy.^⁠1 As we have seen Bogost’s ‘units’ encompass ”people, network routers, genes, and electrical appliances, but also emotions, cultural symbols, business processes, and subjective experiences. Aggregates of these units, such as works of literature, human conditions, anatomies, and economies can properly be called systems,but such systems are fundamentally different from the kind units have unseated in the many disciplines noted above. Moreover, such systems can be understood in turn as units themselves”(2006, pp. 5-6). Bennett works with “one large men’s black plastic work glove; one dense mat of oak pollen; one unblemished dead rat; one white plastic bottle cap; one smooth stick of wood” (2010, p. 4).^⁠2

These writers would doubtless be happy to say that JPEG can be addressed as an object but in order to begin working (creatively) with the JPEG object, as the project demands, it is important to explore what an understanding of JPEG as object entails. What characterises an object? In what way is JPEG an object? What value does this add to our understanding of JPEG?^⁠3

For Harman, an object is what “is or seems to be one thing” (2010, p. 148). That addition of “seems” is important because it not only allows Harman to deal with imaginary, virtual and, I will argue ‘weird’, objects like unicorns, characters in books and protocol but also points to the fourfold character of objects that allow me to develop a JPEG-based object-oriented photography.

In his letter to a curious five-year-old, Harman gives us a series of ‘brief rules about objects’ (Harman 2010, pp. 147-8). We can use this as a way of mapping the way in which JPEG can be seen as an object in Harman’s sense.^⁠4

1. “Relative size does not matter: an atom is no more an object than a skyscraper”. At one level this appears to have noting to do with software. Where is size in software? The number of bytes in the programme? The number of lines of code? When it comes to JPEG are we looking for the relative size of the code fragment governing ‘export to JPEG’ as against the rest of the code in Photoshop? Such investigation is possible but misses the more important point around scale that Harman is making. All objects are equal, on a flat ontological footing. In terms of JPEG, it has an existence and interest as an object regardless of its scale within software or within photography. Its ‘objectness’ does not depend on it’s scale or it’s relationship to something else – Photoshop, machine code, electrical charges etc.

2. “Simplicity does not matter: an electron is no more an object than a piano”. The JPEG standard is simpler than Photoshop but more complex than the specific Huffman coding algorithm it uses. As we have seen JPEG is a ‘family of compression algorithms’ ([NO STYLE for: Lane 1999]) each of which can be seen as an object, simply nested further ‘down’. This idea of nested objects must not be seen in either value or deterministic terms. Just because an object works at a different scale than another does not make it any less important nor any more powerful in determining that other object’s position or workings. Harman agrees with Bennett who argues that “‘in a world of vibrant matter, it is not enough to say that we are ‘embodied’. We are, rather, an array of bodies, many different kinds of them in a nested set of microbiomes” (2010, pp. 112-113), and Bryant who talks of: “the strange mereology of onticology and object-oriented philosophy where objects can be nested in other objects while nonetheless remaining independent or autonomous of those objects within which they are nested” (**UNRESOLVED**). As Ray Brassier put it: “You have this kind of infinite nesting of objects within objects within objects … Every relation between objects itself unfolds within another object… [w]hat you have [in Harman’s philosophy] then is a kind of egalitarian objective univocity, a kind of ontology of pure objectivity: there are nothing but objects, objects nested within one another, and the really significant metaphysical challenge is explaining their interaction” (Brassier et al 2007, p. 316). A Huffman table is an object, so is the JPEG protocol, so is a JFIF image. The thing to explore is how those objects interact within each other and other governmental objects.  It’s position as object is in its position as having “some sort of unitary reality” (2010, p. 147). JPEG, as an industry standard, as a selling point for cameras and software, as a recognised format (in it’s JFIF or EXIF instantiations) places it within the realm of objects.

3. “Durability does not matter: a soul is no more an object than cotton candy”. OOO’s willingness to extend the concept of object to short-lived, ephemeral, even imaginary or fictional things enables it to address systems and structures as well as cultural practices. Monetarism and Harry Potter may not endure but in their capacity to effect and connect, they must be seen as objects in play. The Huffman algorithm and DCT endure – they are mathematical objects, rules maybe. They also do things within the digital imaging pipeline. JPEG as an object does things but after encoding it ceases to be. For some, as we have seen, that is the JPEG process working, realising its potential and then  changing in the flux or plasma of becoming. For Harman, the JPEG object connects and then reconnects within other objects – the next image taken and written to a memory card, the next upload or data-mining operation. The rule is that its objectness doe not depend on its durability any more than on our ability to hold it in our hand.

4. “Naturaleness does not matter: helium is no more an object than plutonium”. Again the democracy of objects demands that we do not divide natural things atoms, trees, helium from tables, weapons grade plutonium and software. As things in the world doing things, being presences I trip over, use, am data-mined by – all are objects and therefore worthy of study and necessary to account for. This is not the simple point that media and cultural studies made when it said that Homer was as worthy of study as Homer or that we needed an account of tattoos as well as Titian. Nor is it the far earlier establishment of literary or art history studies arguing for their objects being worthy alongside the natural sciences. This is not a flattening of hierarchies and categories for political, professional or academic interest. It is a metaphysical statement that all objects are in play whether we like it or not. Once could debate whether cosine is a natural ‘thing’, a Platonic form. From an OOP perspective such a debate is meaningless. The DCT demonstrably is present within JPEG and does something in the digital imaging pipeline.

5. “Reality does not matter: mountains are no more objects that hallucinated mountains”. Here of course Harman lays himself open to the common criticism that his framework is so loose as to be useless. “When one can talk of unicorns and uranium, Donald Duck and Chairman Mao in the same way, what use is it?” he is often asked. But as he has said on his blog:

“A critique that was apparently stated on Facebook: ‘ooo would have popeye riding a pink unicorn with a lava lamp on his head’. No, OOO wouldn’t…

In my position there’s an absolute difference between real and sensual objects. Popeye riding a pink unicorn with a lava lamp on his head would almost certainly not be a real object. (You never know, of course. We’re not omniscient. But I agree that such an entity almost certainly doesn’t exist.)

However, this same Popeye must be accounted for by any ontology worth its salt. Why? Because imaginary things are not utter non-beings. They don’t have independence from the one who is conceiving them as real objects do, but they’re not just nullities or holes of nothingness. I don’t think Raskolnikov is a real object either, but millions of people have read Crime and Punishment and been influenced by it. Raskolnikov needs to be accounted for by ontology.” ([NO STYLE for: Harman 2011])

What Harman is looking to leave out of analysis is the idea of any kind of “non beings”. If things are at work, then they are objects. JPEG is not imaginary but it is certainly difficult to see or find. It is a standard written or maybe woven into software and hardware assemblages as well as business strategies and grandmother’s doting over a new baby. But even if JPEG was not ‘real’. Even if the idea that a standard compressed data efficiently and effectively was an elaborate Capricorn One-like conspiracy perpetrated by mad scientists, Adobe and Google, it wouldn’t matter. JPEG would still be worthy of study because it was still at play in people’s photography, their photographic consumption and their relation to images and imagespace.

In some ways JPEG is an ideal candidate to explore these rules. It does not really have a size; it is both simple in its role but complex in its form; it is ephemeral in its working but durable in its enfolding within imaging; it is unnatural and it is clearly real within the digital imaging pipeline but simultaneously unreal in its presence within the business plans of photo-network start-ups where it is designed to reassure venture capitalists of interoperability and flexibility.^⁠5

What my practice has shown is that JPEG is an object. It has a unity, a presence and a power within imaging objects and apparatuses. What is more it demonstrates Harman’s three key themes about objects: their existence beyond relations; their presence beyond process and their working beyond potential.

¹ Tim Morton would of course argue that his eco-philosophy marries the concern for philosophical rigour with a political concern around the environment.

² Bennett is clear that thing do not have to be impressive or somehow deserve our attention. Anything is an object and can be lively. I would agrre with Matthew Tiessen (interestingly a practice-research artist-ontologist) who says: “ if nature and things have to be exceedingly impressive to deserve our consideration we’re left repeating the expectations that gave rise to our lack of recognition for thing-power in the first place. In response to Bennett’s concerns about fear and respect my modest proposal is that things be encountered from a position of responsive humility – a position that recognizes that things are all we’ve got, whether they command respect or not (Tiessen, 2010).

³ What follows, as has been noted, is a specifically Harman-based account of objects.

⁴ Harman uses a curious negative way of framing his rules. Once could perhaps reframe these objects as positive statements: “an atom is as much an object as is a skyscraper”; “an electron is as much an object as is a piano”; “a soul is as much an object as is cotton candy”; “helium is as much an object as is plutonium” and “mountains are as much objects as are hallucinated mountains”.

⁵ Instagram’s API documentation says: “You must first save your file in PNG or JPEG (preferred) format“ ([NO STYLE for: instagram 2011]). This is not just instructions to engineers, it is a statement for the whole of the Web 2.0 community – including investors and partners, that instagram works with the standards users (for which read customers) use.

Note: This is a redraft and expansion of previous rags ‘n refuse.

• Bennett, J., 2010, Vibrant Matter: A Political Ecology Of Things, Duke University Press, Durham.
• Brassier, R., Grant, I.H., Harman, G. & Meillassoux, Q., 2007, Speculative Realism, Collapse: Philosophical Research and Development, 3, pp. 307-449.
• Harman, G 2011, not sure why this keeps resurfacing, Object-Oriented Philosophy. Retrieved May 18, 2011,  from http://doctorzamalek2.wordpress.com/2011/01/25/not-sure-why-this-keeps-resurfacing/
• Harman, G., 2002, Tool-being: Heidegger and the metaphyics of objects, Open Court, Chicago.
• Harman, G., 2010, Towards Speculative Realism : Essays And Lectures, Zero Books, Winchester and Washington.
• Harman, G., 2011, The Quadruple Object, Zero Books, Ropley.
• instagram 2011, iPhone Hooks, instagram.com. Retrieved September 15, 2011,  from http://instagram.com/developer/iphone-hooks/
• Lane, T 1999, JPEG image compression FAQ, part 1/2, faqs.org. Retrieved September 14, 2011,  from http://www.faqs.org/faqs/jpeg-faq/part1/

This project seeks to understand and also takes place in the ‘digital imaging pipeline’, that space of objects and object connections within hardware and software scopic apparatuses where light-becomes-date-become-image. Although the term ‘pipeline’ would tend to connote process and movement and linear relations, I will come on to draw it in terms of objects.

To provide a grounding for these discussions it is necessary to lay out the technological form of that pipeline in order to establish the range of objects we are dealing with.

One way to understand the ‘digital imaging pipeline’ is via its chemical equivalent. I use the term ‘chemical’ rather than ‘analogue’ because I want to avoid debates about an analogue-digital divide. The issue here is not whether one deals in discrete one and zero steps and the other a smooth curve, but rather the way in which encoding works. The issue within which JPEG as protocol is important is the difference between action of light on silver halides within a chemical process and light on silicon within a digital process.

In ‘chemical photography’ photographic film carries an emulsion binding silver halide crystals to a gelatine base. Silver halide consists of silver combined with a halogen element, such as chlorine, bromine or iodine. These crystals react to the light that hits them forming a latent image which is amplified to form a visible, black image where light has struck when the film is developed. When the film is ‘fixed’ the remaining unexposed crystals are removed, leaving a negative image on film.  Vastly simplified, the chemical imaging pipeline can be characterised as: light hits silver creating latent image; development amplifies latent image creating final image. Of course photography adds other stages and technologies to the process. Most photographers want to turn the negative into a positive. By shining light through the negative onto a paper coated with a similar silver halide emulsion, the exposed areas (black in the negative) stop light hitting the paper, while unexposed areas (clear in the negative) let light hit the crystals.^⁠1 What was light in the scene and black in the negative become light in the print and vice versus. There are other technologies (or objects as I would refer to them) in play. Lenses (or in my case pinholes); camera apparatuses including the shutter and aperture assembly; enlargers; film and paper etc. These of course are also in play in digital photography. What is different is the encoding – the journey of light through latent image or data to visible image.

Many things are similar, metaphorically and literally. Concentrating just on the encoding, light hits a sensor (silicon rather than silver halide). This generates data (electronic information rather a latent image in silver) that becomes an image (through software and protocol processing rather than through chemical development). But there are important differences that impact on how JPEG, as my main focus, works within imaging and to create images.

To work with objects, the first object in the digital imaging pipeline is the sensor. In digital photography these are one of two types: CCD (charge-coupled device) and CMOS (complimentary metal-oxide semiconductor) sensors.^⁠2

Sensors are effectively a whole array of silicon, solar or photovoltaic cells. When light hits one of these cells, some of its energy is absorbed by the silicon which knocks electrons loose which is forced to flow in a particular direction creating a current: photons become electrons, light become electricity.

Digital camera sensors have either a red, green or blue filter over each pixel/cell, essentially making the cell only sensitive to red, green or blue light.These are arranged in a Bayer mosaic pattern consisting with two green, one red and one blue filter – designed to match the bias of human perception of colours.

The sensor reads the amount of charge from each cells (what comes to be known as pixels). These electrical charges need to be collected and organised before they can be processed by other software objects. A CCD sensor handles this differently than a CMOS sensor. In a CCD sensor, a control circuit circuit causes each capacitor to transfer its contents to its neighbour with the final output read at one corner of the array. In a CMOS sensor, each pixel/cell is accompanied by several transistors that amplify and move the charge using more traditional wires. Thus each pixel can be read individually.^⁠3

At this point the light-as-electricity is still ‘analogue’. In order for the software (including JPEG) to be able to work with it, it needs to become digital. Here we come to our second object^⁠4: the analog-to-digital converter (ADC). An ADC is an integrated circuit that samples the analogue feed from the sensor into a number of discrete levels of brightness. Most cameras use 8bit ADCs which allow 256 distinct values for the brightness of each pixel.^⁠5

This digital information simply records the luminescence at each location on the sensor. This is greyscale data. The ADC adds extra information to its output: information about a pixel’s location (and hence whether it was ‘under’ a red, green or blue filter); metadata about the sensor’s colour space; and the camera’s white balance setting. This digital information becomes the RAW data file that is written to the camera’s storage medium.^⁠6

Because each pixel/cell only sensed one wavelength of light (red, green or blue), the information making up the ‘latent image’ needs to be interpolated so that the image can represent the amount of red across the whole image not just on those bit where the filter measured the red light. To do this a ‘demosaicing algorithm’ averages the values from the closest surrounding pixels to assign a ‘true colour’ to each pixel. This data can be encoded as a visible colour image file. It is here where JPEG comes in.

The demosaicing algorithm outputs three 8 bit colour channels of data as opposed to the one 12 bit RAW channel. Protocols within the in-camera software encode those three channels in particular formats: usually either a 24-bit TIFF or a 24-bit JPEG/JFIF or JPEG/EXIF file, see below.

To concentrate just on the JPEG protocol’s processing of that RAW feed of data, the digital imaging pipeline continues in four steps: Sampling, Discrete Cosine Transform, Quantization and Huffman Coding (Miano 1999, p. 44). At the end, the light-as-data is a JFIF image, commonly know as a ‘jpeg photograph’.

The pixel data is first converted from RGB to YCbCr colorspace. The JPEG protocol is principally about compression. It’s role in the imaging pipeline is to reduce the amount of data in the file – hence its importance in the early days of the Internet when bandwidth was at a premium. Part of the work of compression is the move from RGB to YCbCr. Storing image data in both RGB and YCbCr colorspaces demands three channels of information – in RGB: red, green and blue in YCbCr: Luminance and two chrominance, blue and red (Miano 1999, p. 6). Both allow a full range of colours but in RGB, each channel is sampled at the same frequency while in YCbCr, this can be varied. The Y component contributes most information to the visible image and JPEG therefore assigns more weight to that component and reduces the amount of information in the Cb and Cr channels, thus reducing the amount of information and so the file size. As John Miano explains:

“By adjusting the sampling frequencies you could include each pixel’s Y component value in the compressed data and 1 value for every 4 pixels from the other components. Instead of storing 12 values for every 4 pixels, you would be storing 6 – a 50% reduction” (Miano 1999, p. 41).

The next step in JPEG encoding is ‘Discrete Cosine Transform’ (DCT). First the protocol divides the YCbCr image data into 8×8 blocks called data units.^⁠7 DCT does not actually compress or throw information away, it merely readies the data/information for that to happen in the next step by sorting the information which can safely be discarded. It can assumed that, over an 8 by 8 block, the values of the Y,Cb and Cr components will not be large. Rather than record the individual values of each component, we could average the values for each block and record how each pixel differs from that average value.

DCT takes the set of values in each data unit and transforms it into a set of coefficients to cosine functions with increasing frequencies (Miano 1999, pp. 77-90). DCT arranges the digital information ready for compression by finding the frequency of each value – in lay terms the most frequent tone or colour values.

JPEG compression, depends on the fact that human perception is not perfect. A lot of information can be thrown away and, effectively we fill in the gaps in a similar way to the way the demosaicing algorithm does. The next step takes the sorted data from the DCT and discards those coefficients that contribute less information to the image.^⁠8 This is the Quantization step. Quantization is a “fancy name for division, To quantize the DCT coefficients we simply divide them by another value and round to the nearest integer” (Miano 1999, p. 88). This rounding process effectively discards some of the coefficeints and so information because the value become zero.

The JPEG standard does not specify the value to be used. It leaves that up to the application using the protocol. Rather it provides 8×8 quantization tables’ that map onto the 8×8 data units. We normally come across these table when we choose the ‘quality’ setting for JPEG compression in end-user software such as Photoshop or select Fine, HQ or SHQ quality settings in a camera.

Having discarded data from the RAW data file, JPEG’s final step is to create a visible (JFIF) file. This is achieved through Huffman coding. Like  DCT Huffman coding takes the set of values in each data unit and transforms it into another set of values. Unlike the DCT, Huffman coding is  lossless – no further information is discarded. Rather this process saves further space by assigning shorter codes to the most frequently used values. Like Morse code, Huffman Coding assigns shorter codes to the most frequently occurring values (vowels have shorter Morse code symbols than x or z) according to a Huffman table. As Calvin Haas explains: “Creating these tables generally involves counting how frequently each symbol (DCT code word) appears in an image, and allocating the bit strings accordingly. But, most JPEG encoders simply use the huffman tables presented in the JPEG standard” (2008).

Having mapped the data to new (shorter) values according to a Huffman table, the resultant file must include that table (or reference the standard table) to enable other software to decode the data as a visible image.

Having started as light photons, being turned into electrical charge and from there into data, the resultant information has been sorted and compressed by JPEG into a file ready to be written (alongside a RAW file) to the camera’s memory. The JPEG protocol wraps the compressed data within a format that includes the Huffman and Quantization tables necessary to decode the compressed data, the data itself and a series of markers that break the stream of encoded data into its component structures. These markers are 2 bytes length with the second denoting the type of marker.

One such marker is the APP marker which hold application-specific data. They are used by software or applications to additional information beyond what is demanded by JPEG. An encoder that uses JPEG can specify particular information within an APP marker. This is important when it comes to the two most widely used JPEG-encoded file formats.

JPEG does not define a file format. As John Miano says: “it says nothings about how colors are represented, but deals only with how component values are stored” (1999, p. 40). Other file formats such as TIFF can compress using JPEG. JPEG can therefore write more than one sort of data/image file. The two most common follow the JFIF (JPEG File Interchange Format)) ([NO STYLE for: Hamilton 1992]) and the EXIF (Exchangeable Image File Format) ([NO STYLE for: CIPA 2011]) standards. The two standards are very similar with EXIF allowing the addition of specific metadata tags but does not allow colour profiles. Most cameras encode to an EXIF file while imaging application use JFIF. Technically JFIF and EXIF use different APP markers (APP0 and APP1). In practice most photo applications use JFIF and include the metadata from the APP1 marker.^⁠9

Other markers provide space in the file for comments; details of the width and height and number of components in the image;  the Huffman and Quantization tables.

As I shall discuss in the ‘The JPEG object in theory’ and ‘The JPEG object in practice’ chapters this ‘family of compression algorithms’ (Lane 1999]) can be addressed as an object in Harman’s terms not only in terms of its existence in paper standards documents but also in terms of its ‘weird’ quadruple existence within the digital imaging pipeline. Clearly however, it is possible to address this whole pipeline (or indeed the chemical imaging pipeline) through OOP.

OOP enables, even forces, us to see a panoply of objects in play in any situation or assemblage. Human and unhuman, material and virtual, even real and imaginary actants (in Latour’s terms) connect and reconnect in ways that we experience as processes or pipelines. In terms of chemical photography, the photographer, lens, shutter blades, gelatine, silver and  sodium thiosulfate (fixer) all have their own presence and material actuality. They all do things as individual objects as they connect and reconnect with each other. But they also form components of other objects: the camera, the photographic society, Snappy Snaps, Kodak. It is objects all the way down.

Similarly in the digital imaging pipeline hardware and software objects, mathematical algorithms and tables, silicon and electrical charges, Adobe, the photographer and photons are all in play. They all have their specificity and their connections. Some are material, others virtual. Some we can distinguish, others – like an algorithm have a form (like the law of gravity or Pi) have a weird presence and actuality. Some are often characterised as systems or contexts, but they too are objects just at a different scale. We may experience the pipeline as a process but what we are really faced with is a network of objects connecting and reconnecting within other objects.

Where OOP (at least in the Harman version I explore) differs is firstly in refusing to leave that focus on objects – to refuse to talk of systems, assemblages or contexts as anything other than objects, or as Tim Morton calls them ‘hyperobjects’. Secondly Harman’s OOP refuses to characterise those objects as defined by their relations. Rather they have an existence and, in Jane Bennett’s terms a vitality, that exceeds their relations. Thirdly, those objects are not processes. They are not in flux. Rather change is matter of new objects formed in new object connections. Finally, the objects in play in the digital imaging pipeline do not hold anything back. They do not harbour potential. They are fully present in their connections not harbouring potential, waiting to ‘become’.

This perspective on objects runs counter to much discussion about digital/software objects and protocols.

[NOTE: If you’ve managed to get this far and have any knowledge of maths, electrical engineering or suchlike and can tell me if I’ve made any great glaring errors ever… I’d be very grateful!]

• CIPA 2011, Exchangeable Image File Format For Digital Still Cameras: Exif Version 2.3, Camera & Imaging Products Association. Retrieved September 14, 2011,  from http://www.cipa.jp/english/hyoujunka/kikaku/pdf/DC-008-2010_E.pdf
• Haas, C 2008, JPEG Huffman Coding Tutorial, impulseadventure.com. Retrieved September 13, 2011,  from http://www.impulseadventure.com/photo/jpeg-huffman-coding.html
• Hamilton, E 1992, JPEG File Interchange Format Version 1.02, World Wide Web Consortium. Retrieved September 14, 2011,  from http://www.w3.org/Graphics/JPEG/jfif3.pdf
• Lane, T 1999, JPEG image compression FAQ, part 1/2, faqs.org. Retrieved September 14, 2011,  from http://www.faqs.org/faqs/jpeg-faq/part1/
• Miano, J., 1999, Compressed Image File Formats, Addison Wesley, Reading, Mass..

¹ For the sake of clarity I focus on basic black and white photography rather than colour imaging or reversal (slide) photography.

² The camera I used as the basis for my RAW/JPEG imaging apparatus (the Olympus E-420) uses a Live MOS sensor, The brand name used by Leica, Panasonic and Olympus in their Four Thirds System  cameras  manufactured since 2006. The companies claim the sensor can achieve the same image quality as CCD-based sensors while keeping energy consumption down to CMOS levels.

³ CCD sensors are generally seen as more expensive, more power hungry but also having higher sensitivity and being capable of delivering higher quality. CMOS sensors tend to be found in mobile phone cameras

⁴ Of course OOPs would be clear that we have already been dealing with a whole series of nested objects in terms of the sensor, but for clarity’s sake I outline the key actants.

⁵ A bit in computer terms has a value of on or off, one or zero. A two-bit ADC would divide the information from the sensor into levels 00, 01, 10 and 11. An 8-bit sensor can divide it into 256 from 00000000 to 11111111. My Olympus E-0420 uses a 12-bit ADC dividing the information into 4096 possible levels.

⁶ This RAW data/file is not ‘pure’. Each camera has its own way of writing the RAW data, it’s own format. RAW converters (the part of software that interprets and renders that data as image within other software) have to know the various formats Olympus, nikon, Canon etc use in order to ‘make sense’ of that data.

⁷ JPEG works with 8-bit data.

⁸ This is why JPEG compression is referred to as ‘lossy compression’ because data is lost.

⁹ Strictly speaking this goes against the standards with both JFIF and EXIF demanding that their marker is first in the datastream. As with much software, this demand is fudged.

Three intro sections for three longer sections for one chapter for…

The relational object

For software and critical code studies, locating the digital or code object within a field of relations has been a powerful axiom. As part of a broader hegemonic struggle within media and cultural studies, exploring protocols, interfaces, languages and algorithms as powerful because of the way they relate to other actors in the network^⁠1, has allowed software studies to establish a critical praxis while also arguing for software’s pervasiveness and enfolding within the complex assemblages of contemporary technocapitalism and technoculture.

As an example, the development of ‘platform studies’{Montfort 2009}, with its technologically forensic account of videogame software and hardware objects has opened up new approaches to not only games and gaming but also the political-economic and legal relations within which they work. Addressing sprites, processors and the Atari Television Interface Adapter (TIA) allowed Nick Montfort and Ian Bogost to explore how the Atari VCS system worked but also why. What is more, exploring those actant-objects as enfolded in economic and legal relations, drawing their power from their position with regard to those relations, enabled the authors to explore ‘the platform’ not just the game and the wider capitalist technoculture. Without those relations an account of those objects would have remained abstract and decontextualised. Without an account of the objects, an examination of the context would have remained abstract and general. A study of the specifics and the connections offers more.

As I will come on to argue, OOP is not opposed to an idea of relations. Nor is it against an account of the network. Indeed, as Harman’s feting of Latour in the first part of Prince of Networks{%Harman 2009} makes clear, actants in networks is a powerful model: objects connect. Where OOP differs is in demanding that objects are not defined by their relations. Their character and power exceeds their relations. As my own work will show, exploring JPEG as having an existence, character and power beyond its relations allows us to see how governmental issues of data-mining are best addressed as a matter of the JPEG-object connecting with a search algorithm-object within another, specific object. Objects relate within objects not within contexts or fields. This is the heart of ‘object-orientation’, a refusal to leave an account of specific objects even when building a critique of networks. It is this refusal (or perhaps more positively, focus) that enabled me to engage in my particular imaging and build my particular critique.

For some seminal work in software studies, including the first discussions of porotocl, this is not the case. Objects and best addressed in terms of relationality.

¹ I use the term ‘network’ in a Latourian rather than a technical sense.

The Process Object

If relationality has opened doors for software studies in constructing a technologically informed and yet comprehensive account of technoculture and techno-governmentality, a second theme has helped ensure the technical specifics do not drag the account down to a static or determinist reductionism. The idea that software is dynamic, that it sets new relations in motion as it runs, that its character is change has allowed software studies to understand the relations between what appears to be a static component of software and a dynamic field of culture and power.

As an example, while, like Manovich addressing digital rather than specifically code-objects, Mark B. N. Hansen frames the digital image as processual. His argument that “the digital image demarcates an embodied processing of information”{%Hansen 2004@12} allows him to explore how the image-object is framed by and through the body. It is only by adding a dynamism to the digital object, only by addressing it as process and changing that its power and affectivity can be understood. Assigning movement to our picture of data and code allows us to understand its embodied as well as enfolded workings. A view of static, unchanging data, code or protocols cannot account for our phenomenological or even psychoanalytic relationship to computation.

This is a flux of code-data-subjectivity that we experience as the technosocial assemblage at a bodily and material level. Those digital objects change as they are reinserted and revisited through the body and the body politic. They add and remove new dimensions and relations as they process and are processed.

Again it is important to stress that OOP does not reject change. It is not a philosophy of static objects. When it argues for objects connecting within objects, that movement is as dynamic as any complex adaptive system. Where OOP disagrees with the idea of the processural object in flux is in the idea that an object adds and removes dimensions. Rather, for Harman “Becoming does occur: but in sudden jumps and jolts, not through a meaningless accretion of any-instants-whatever that float away in the canal of fluxion”{Harman 2011@301}.

My work with the JPEG protocol object has shown that the protocol object does change but precisely in those jumps and jolts as sampling takes place, as quantization and huffman coding work and as the markers are set. These are new object moments and it is by addressing them as such rather than as changes in some fixed if dynamic JPEG, that we can account for its flexible and adaptable connection to other governmental objects.

This perspective however runs counter to software studies’ stress on the what Matthew Kirschenbaum{%Kirschenbaum 2008@15} has called the “duality” at the heart of digital mechanisms: product and process.

The potential object

Closely related to this conception of processuality and a flux of becoming is that of potentiality. Again this theme has served software studies well. By positioning the digital object as harbouring a potential, software studies once again enfolds the object into the assemblage, positioning it as empowering subjectivities relations and processes, setting in motion new formations. The flexibility, interoperability and dynamic nature of the digital object makes it the ideal vehicle for critical or disciplinary potential.

It is not just Galloway and Thacker who have used this potentiality as a way of position the object as critical tool. Although perhaps not a ‘software studies’ scholar, Vito Campanelli uses the idea that digital objects harbour a potentiality to explore the DivX and MP3 experience{%Campanelli 2010a}. Here the particular codecs set in motion particular aesthetic (as well as socio-political) experiences as legitimate or ‘pirated’ media is encoded, decoded, streamed or downloaded. His broader target of the web aesthetic experience^⁠1, is an experience of hardware and software objects which harbour a dynamic potential to structure and restructure experience. Directors use that potential as do p2p media sharers. The digital object’s potentiality is actualised in particular ways in particular configurations at particular moments.

Such a perspective clearly adds value in avoiding an over-simplistic essentialism – particularly when it comes to aesthetics. As with processuality, it draws attention to the seemingly paradoxical dynamism at work in what appears to be stable, defined and delimited code. Some within the object-oriented movement would agree. In particular Levi Bryant has argued strongly for the power of seeing objects as harbouring potential. Harman however disagrees. As we shall see, for him objects do not hold anything back. They are always fully present and actual. “Potentiality is merely ‘potential for a future relation’, when we really only ought to be talking about actuality”{Harman 2011@299}.

Again this account of a fully present and actualised protocol object chimes with my practice. JPEG certainly withdraws from access within my imaging but it is fully actualised within the digital imaging pipeline of my apparatuses. It is not holding anything back. The relations and object connections are fully present.

But to refuse to assign a potential power to objects is to run counter to a dominant concern in software studies.

¹ This specifically network aesthetic is different in focus to the digital aesthetic sought and discussed by Sean Cubitt{CubittSean 1998}

• Campanelli, V. 2010, The DivX and MP3 Experience, in Web Aesthetics: How Digital Media Affect Culture And Society, NAi Publishers; Institute of Network Cultures, Rotterdam, pp. 150-66.
• Cubitt, S., 1998, Digital Aesthetics, SAGE Publications, London; Thousand Oaks, Calif..
• Harman, G., 2009, Prince of Networks: Bruno Latour and Metaphysics, Anamnesis, Melbourne.
• Harman, G. 2011, Response to Shaviro, in L Bryant, N Srnicek & G Harman (eds), The Speculative Turn: Continental Materialism And Realism, re.press, Melbourne, pp. 291-303.
• Kirschenbaum, M.G., 2008, Mechanisms: New Media And The Forensic Imagination, MIT Press, Cambridge, Mass..
• Montfort, N. & Bogost, I., 2009, Racing The Beam: The Atari Video Computer System, The MIT Press, Cambridge, MA.

At first sight David M. Berry’s The Philosophy Of Software{Berry 2011} would appear to offer an object-oriented account of software objects. After all,  Berry draws on Bruno Latour’’s ‘philosophy’^⁠1 to argue for an account of power-full code actant-objects. “Code is striking in its ability to act as both an actor performing actions upon data, and as a vessel, holding data within its boundaries”{%Berry 2011@33}. These actants’ power is linked to relations. He says: “no code is ‘bigger’ or ‘more important’ than another, except to the extent that it has a larger number of connections”{Berry 2011@62}. His debt to Latour extends to his method for software studies. “we have to be alert to following the code’s genealogy to see how it is developed as an historical object and its influences on attitudes, movements and ideas”{%Berry 2011@33}.

Furthermore, he explores code in its material specificity, moving beyond a purely linguistic approach to embrace a phenomenological account of the ‘computational image’: “how one know one’s way around with respect to things in a computational image, and conversely, the computational way of making sense of the world and how it gives expression to that sensibility”{%Berry 2011@131-2}. Whether looking at Perl poetry, Obfuscated C Code contests or high frequency trading, the code-object is the focus even if the technosocial assemblage is the target.

But again, Berry’s objects are framed through the themes of relationality, processuality and potentiality that run through software studies. He says: “Code must then be understood in context, as something that is in someway potentially running for it to be code. Code is processual, and keeping in mind its execution and agentic form is crucial to understanding the way in which it is able to both structure the world and continue to act upon it”{%Berry 2011@38} (my emphasis).

When he argues that “the ontology of the computational is increasingly hegemonic in forming the background presupposition for our understanding the world”{%Berry 2011@128}, it is the code-object’s connections, forming a hegemonic bloc within hard/software assemblages that is the context. It is the relations they enact, empower and enable that form the “condition of possibility for a device-dependent, co-constructed subjectivity”{%Berry 2011@160}. The agency is in the running. Google and Facebook’s data mining algorithms are dynamic components in the infinite archive and its governmental praxis. They and their power full actant positions in the assemblage are actualised as they, and the databases they generate, become.

Berry’s philosophy is certainly object-centred but it is not object-oriented in the sense in which I am seeking to use Harman’s quadruple object. For Berry objects do not exceed their relations. They precisely depend on them. There is no reality or power to a code-object outside of its position within a particular relational conjuncture. Objects must be seen as dynamic and holding something back. It is only by framing them in these terms that Berry can explore that field of relationality as well as our  or any other actant’s phenomenological relation to it. Code must be seen as the potential process that fuels the governmental as well as artistic praxis he investigates.

As I will come on to argue, Harman’s object-oriented philosophy, by exploring the multidimensional nature of objects exceeding any relations and as fully realised and present and connecting within other objects to form new objects, allows us to remain focused on the protocol/code-object, addressing its specificity, even weirdness as a governmental actant as well as imaging technology.

¹ The term is in quotes here to highlight Latour’s reluctance to be seen as developing such a framework{Latour 2011a}.

• Berry, D.M., 2011, The Philosophy Of Software: Code And Mediation In The Digital Age, Palgrave Macmillan, Houndmills, Basingstoke, Hampshire ; New York.
• Latour, B., Harman, G. & Erdélyi, P., 2011, The Prince and the Wolf: Latour and Harman at the LSE, Zero Books, Ropley.