Is it worth setting your smartphone camera to use RAW format, instead (or: alongside) of the standard JPG format?
I must say I am not sure. Above you should be able to see three versions of the same photo. The first one is one produced with the automatic settings of my Huawei Mate 20 Pro. It is a f/1.8 photo coming from the main camera module, processed with various algorithms to create a “nice”, tonally rather balanced JPG with 2736 x 3648 pixels.
The second one is direct/non-edited conversion of the original RAW (imported into desktop Lightroom, then directly turned into JPG), with 5456 x 7280 pixels and plenty of information that is potentially valuable for editing, yet it is also bit too dark and the lens quality is frankly probably not quite worth all those pixels, to start with (the depth of field is narrow here, and most of the photo is soft, when you look it 1:1 in a large screen).
The third version is the RAW-based and Lightroom-edited photo, where I have just accepted some “auto” corrections that the software has available for beginners. This time, we can see many of the details again better, since Lightroom has tweaked the exposure and contrast settings and tonal curves. Yet, the change of white balance setting into the automatic “daylight” version has made the cold, Autumn morning photo to appear a bit too warm in colours to my mind.
This could be of course fixed in further, more nuanced and sensible Lightroom editing, but the point perhaps is that the out-of-camera JPG that Huawei is capable of producing is a rather nice compromise in itself. It is optimised for what the small, fixed lenses are capable of achieving, and the file size is good for sharing in social media – which is what most smartphone photos are used for, in any case. Artificial intelligence does it best to produce what a typical “Autumn Leaves” shot should look like. That might then again be something that you like – or not.
It is surely possible to achieve more striking and artistically ambitious (“non-typical”) outcomes when the original photo is taken in RAW, even when it is coming from a smartphone camera. But I would say that the RAW based workflow probably really makes sense when you are using a SLR style camera with a lens that is sharp enough for you to really go deep into the details, do some more ambitious cropping or tonal adjustments, for example.
Or, what do you think?
There are various articles online that you can also have a look on this, e.g.
Currently in the Canon camp, my only item from their “Lexus” line – of the more high-quality professional L lenses – is the old Canon EF 70-200mm f/4 L USM (pictured). The second picture, the nice crocus close-up, is however not coming from that long tube, but is shot using a smartphone (Huawei Mate 20 Pro). There are professional quality macro lenses that would definitely produce better results on a DSLR camera, but for a hobbyist photographer it is also a question of “good enough”. This is good enough for me.
The current generation of smartphone cameras and optics are definitely strong in the macro, wide angle to normal lens ranges (meaning in traditional terms the 10-70 mm lenses on full frame cameras). Going to telephoto territory (over 70 mm in full frame terms), a good DSLR lens is still the best option – though, the “periscope” lens systems that are currently developed for smartphone cameras suggest that the situation might change in that front also, for hobbyist and everyday photo needs. (See the Chinese Huawei P30 Pro and OPPO’s coming phones’ periscope cameras leading the way here.) The powerful processors and learning, AI algorithms are used in the future camera systems to combine data coming from multiple lenses and sensors for image-stabilized, long-range and macro photography needs – with very handy, seamless zoom experiences.
My old L telephoto lens is non-stabilized f/4 version, so while it is “fast” in terms of focus and zoom, it is not particularly “fast” in terms of aperture (i.e. not being able to shoot in short exposure times with very wide apertures, in low-light conditions). But in daytime, well-lighted conditions, it is a nice companion to the Huawei smartphone camera, even while the aging technology of Canon APS-C system camera is truly from completely different era, as compared to the fine-tuning, editing and wireless capabilities in the smartphone. I will probably next try to set up a wireless SD card & app system for streaming the telephoto images from the old Canon into the Huawei (or e.g. iPad Pro), so that both the wide-angle, macro, normal range and telephoto images could all, in more-or-less handy manner, meet in the same mobile-access photo roll or editing software. Let’s see how this goes!
(Below, also a Great Tit/talitiainen, shot using the Canon 70-200, as a reference. In an APS-C crop body, it gives same field of view as a 112-320 mm in a full frame, if I calculate this correctly.)
I have studied immersive phenomena over the years, and still am fascinated by what Finnish language so aptly catches with the idiom “Muissa maailmoissa” (literally: “in other worlds” – my dictionary suggests as an English translation “away with the fairies”, but I am not sure about that).
There is a growing concern with the effects of digital technologies, social media, and with games and smartphones in particular, as they appear to be capable of transporting increasing numbers of people into other worlds. It is unnerving to be living surrounded by zombies, we are told: people who stare into other realities, and do not respond to our words, need for eye contact or physical touch. Zombies are everywhere: sitting in cafeterias and shopping centres, sometimes slowly walking, with their eyes focused in gleaming screens, or listening some invisible sounds. Zombies have left their bodies here, in our material world, but their minds and mental focus has left this world, and is instead transported somewhere else.
The problem with the capacity to construct mental models and living the life as semiotic life-forms has always included somewhat troublesome existential polyphony – or, as Bakhtin wrote, it is impossible for the self to completely coincide with itself. We are inaccessible to ourselves, as much as we are to others. Our technologies have not historically remedied this condition. The storytelling technologies made our universes polyphonic with myths and mythical beings; our electronic communication technologies made our mental ecosystems polyphonic with channels, windows, and (non-material) rooms; and our computing technologies made our distributed cognition polyphonic with polyphonic memory and intelligence that does not coincide with our person, even when designed to be personalized.
Of course, we need science fiction for our redemption, like it has always been. There are multiple storyworlds with predictive power that forecast the coming of shared sensorium: seeing what you see, with your eyes, hearing your hearings. We’ll inevitably also ask: how about memory, cognition, emotion – cannot we also remember your remembering, and feel your thinking? Perhaps. Yet, the effect will no doubt fail to remedy our condition, once more. There can be interesting variations of mise-en-abyme: shared embeddedness into each other’s feeds, layers, windows and whispers. Yet, all that sharing can still contain only moments of clear togetherness, or desolate loneliness. But the polyphony of it all will be again an order of magnitude more complex than the previous polyphonies we have inhabited.
The main media attention in applications of AI, artificial intelligence and machine learning, has been on such application areas as smart traffic, autonomous cars, recommendation algorithms, and expert systems in all kinds of professional work. There are, however, also very interesting developments taking place around photography currently.
There are multiple areas where AI is augmenting or transforming photography. One is in how the software tools that professional and amateur photographers are using are advancing. It is getting all the time easier to select complex areas in photos, for example, and apply all kinds of useful, interesting or creative effects and functions in them (see e.g. what Adobe is writing about this in: https://blogs.adobe.com/conversations/2017/10/primer-on-artificial-intelligence.html). The technical quality of photos is improving, as AI and advanced algorithmic techniques are applied in e.g. enhancing the level of detail in digital photos. Even a blurry, low-pixel file can be augmented with AI to look like a very realistic, high resolution photo of the subject (on this, see: https://petapixel.com/2017/11/01/photo-enhancement-starting-get-crazy/.
But the applications of AI do not stop there. Google and other developers are experimenting with “AI-augmented cameras” that can recognize persons and events taking place, and take action, making photos and videos at moments and topics that the AI, rather than the human photographer deemed as worthy (see, e.g. Google Clips: https://www.theverge.com/2017/10/4/16405200/google-clips-camera-ai-photos-video-hands-on-wi-fi-direct). This development can go into multiple directions. There are already smart surveillance cameras, for example, that learn to recognize the family members, and differentiate them from unknown persons entering the house, for example. Such a camera, combined with a conversant backend service, can also serve the human users in their various information needs: telling whether kids have come home in time, or in keeping track of any out-of-ordinary events that the camera and algorithms might have noticed. In the below video is featured Lighthouse AI, that combines a smart security camera with such an “interactive assistant”:
In the domain of amateur (and also professional) photographer practices, AI also means many fundamental changes. There are already add-on tools like Arsenal, the “smart camera assistant”, which is based on the idea that manually tweaking all the complex settings of modern DSLR cameras is not that inspiring, or even necessary, for many users, and that a cloud-based intelligence could handle many challenging photography situations with better success than a fumbling regular user (see their Kickstarter video at: https://www.youtube.com/watch?v=mmfGeaBX-0Q). Such algorithms are already also being built into the cameras of flagship smartphones (see, e.g. AI-enhanced camera functionalities in Huawei Mate 10, and in Google’s Pixel 2, which use AI to produce sharper photos with better image stabilization and better optimized dynamic range). Such smartphones, like Apple’s iPhone X, typically come with a dedicated chip for AI/machine learning operations, like the “Neural Engine” of Apple. (See e.g. https://www.wired.com/story/apples-neural-engine-infuses-the-iphone-with-ai-smarts/).
Many of these developments point the way towards a future age of “computational photography”, where algorithms play as crucial role in the creation of visual representations as optics do today (see: https://en.wikipedia.org/wiki/Computational_photography). It is interesting, for example, to think about situations where photographic presentations are constructed from data derived from myriad of different kinds of optical sensors, scattered in wearable technologies and into the environment, and who will try their best to match the mood, tone or message, set by the human “creative director”, who is no longer employed as the actual camera-man/woman. It is also becoming increasingly complex to define authorship and ownership of photos, and most importantly, the privacy and related processing issues related to the visual and photographic data. – We are living interesting times…
In the 1970s and 1980s the concept ‘cognitive engineering’ was used in the industry labs to describe an approach trying to apply cognitive science lessons to the design and engineering fields. There were people like Donald A. Norman, who wanted to devise systems that are not only easy, or powerful, but most importantly pleasant and even fun to use.
One of the classical challenges of making technology suit humans, is that humans change and evolve, and differ greatly in motivations and abilities, while technological systems tend to stay put. Machines are created in a certain manner, and are mostly locked within the strict walls of material and functional specifications they are based on, and (if correctly manufactured) operate reliably within those parameters. Humans, however, are fallible and changeable, but also capable of learning.
In his 1986 article, Norman uses the example of a novice and experienced sailor, who greatly differ in their abilities to take the information from compass, and translate that into a desirable boat movement (through the use of tiller, and rudder). There have been significant advances in multiple industries in making increasingly clear and simple systems, that are easy to use by almost anyone, and this in turn has translated into increasingly ubiquitous or pervasive application of information and communication technologies in all areas of life. The televisions in our living rooms are computing systems (often equipped with apps of various kinds), our cars are filled with online-connected computers and assistive technologies, and in our pockets we carry powerful terminals into information, entertainment, and into the ebb and flows of social networks.
There is, however, also an alternative interpretation of what ‘cognitive engineering’ could be, in this dawning era of pervasive computing and mixed reality. Rather than only limited to engineering products that attempt to adapt to the innate operations, tendencies and limitations of human cognition and psychology, engineering systems that are actively used by large numbers of people also means designing and affecting the spaces, within which our cognitive and learning processes will then evolve, fit in, and adapt into. Cognitive engineering does not only mean designing and manufacturing certain kinds of machines, but it also translates into an impact that is made into the human element of this dialogical relationship.
Graeme Kirkpatrick (2013) has written about the ‘streamlined self’ of the gamer. There are social theorists who argue that living in a society based on computers and information networks produces new difficulties for people. Social, cultural, technological and economic transitions linked with the life in late modern, capitalist societies involve movements from projects to new projects, and associated necessity for constant re-training. There is necessarily no “connecting theme” in life, or even sense of personal progression. Following Boltanski and Chiapello (2005), Kirkpatrick analyses the subjective condition where life in contradiction – between exigency of adaptation and demand for authenticity – means that the rational course in this kind of systemic reality is to “focus on playing the game well today”. As Kirkpatrick writes, “Playing well means maintaining popularity levels on Facebook, or establishing new connections on LinkedIn, while being no less intensely focused on the details of the project I am currently engaged in. It is permissible to enjoy the work but necessary to appear to be enjoying it and to share this feeling with other involved parties. That is the key to success in the game.” (Kirkpatrick 2013, 25.)
One of the key theoretical trajectories of cognitive science has been focused on what has been called “distributed cognition”: our thinking is not only situated within our individual brains, but it is in complex and important ways also embodied and situated within our environments, and our artefacts, in social, cultural and technological means. Gaming is one example of an activity where people can be witnessed to construct a sense of self and its functional parameters out of resources that they are familiar with, and which they can freely exploit and explore in their everyday lives. Such technologically framed play is also increasingly common in working life, and our schools can similarly be approached as complex, designed and evolving systems that are constituted by institutions, (implicit, as well as explicit) social rules and several layers of historically sedimented technologies.
Beyond all hype of new commercial technologies related to virtual reality, augmented reality and mixed reality technologies of various kinds, lies the fact that we have always already lived in complex substrate of mixed realities: a mixture of ideas, values, myths and concepts of various kinds, that are intermixed and communicated within different physical and immaterial expressive forms and media. Cognitive engineering of mixed reality in this, more comprehensive sense, involves involvement in dialogical cycles of design, analysis and interpretation, where practices of adaptation and adoption of technology are also forming the shapes these technologies are realized within. Within the context of game studies, Kirkpatrick (2013, 27) formulates this as follows: “What we see here, then, is an interplay between the social imaginary of the networked society, with its distinctive limitations, and the development of gaming as a practice partly in response to those limitations. […] Ironically, gaming practices are a key driver for the development of the very situation that produces the need for recuperation.” There are multiple other areas of technology-intertwined lives where similar double bind relationships are currently surfacing: in social use of mobile media, in organisational ICT, in so-called smart homes, and smart traffic design and user culture processes. – A summary? We live in interesting times.
– Boltanski, Luc, ja Eve Chiapello (2005) The New Spirit of Capitalism. London & New York: Verso.
– Kirkpatrick, Graeme (2013) Computer Games and the Social Imaginary. Cambridge: Polity.
– Norman, Donald A. (1986) Cognitive engineering. User Centered System Design, 31(61).
Portable Document Format (PDF) files are a pretty standard element in academic and business life these days. It is sort of a compromise, a tool for living life that is partly based on traditional paper documents and their conventions, and part on new, digital functionalities. A PDF file should maintain the appearance of the document same, as moved from device to device and user to user, and it can facilitate various more advanced functionalities.
One such key function is ability to sign a document (an agreement, a certificate, or such) with digital signatures. This can greatly speed up many critical processes in contemporary, global, mobile and distributed lives of individuals and organisations. Rather than waiting for a key person to arrive back from trip to their office, to physically use pen and paper to sign a document, a PDF document version of the document (for example) can be just mailed to the person, who then adds their digital signature to the file, saves, and sends the signed version back.
In legal and technical terms, there is nothing stopping from moving completely to using digital signatures. There are explanations of the legal situation e.g. here:
And Adobe, the leading company in electronic documents business, provides step-by-step instructions on how to add or generate the cryptographic mechanisms to ensure the authenticity of digital signatures in PDFs with their Acrobat toolset:
According to my experience, most contracts and certificates still are required to be signed with a physical pen, ink, and paper, even while the digital tools exist. The reasons are not legal or technical, but rather rooted in organisation routines and processes. Many traditional organisations are still not “digital” or “paperless”, but rather build upon decades (or: centuries!) of paper-trail. If the entire workflow is built upon the authority of authentic, physically signed contracts and other legal (paper) documents, it is hard to transform the system. At the same time, the current situation is far from optimal: in many cases there is double work, as everything needs to exist both as the physical papers (for signing, and for paper-based archiving), and then scanned into PDFs (for distribution, in intranets, in email, in other electronic archives that people use in practice).
There are useful new tools like Kami (https://www.kamihq.com/) that facilitate move to “paperless classroom”, with their easy to use functions for drawing, editing, and commenting on PDFs (Adobe’s business oriented solutions are not the best answer to all users and situations)