This seems more a limitation of monitors. If you had very large bit depth, couldn't you just display images in linear light without gamma correction.
Why exactly? My understanding is that gamma correction is effectively a optimization scheme to allocate bits in a perceptually uniform way across the dynamic range. But if you just have enough bits to work with and are not concerned with file sizes (and assuming all hardware could support these higher bit depths), then this shouldn't matter? IIRC unlike crts, LCDs don't have a power curve response in terms of the hardware anyway, and emulate the overall 2.2 trc via LUT. So you could certainly get monitors to accept linear input (assuming you manage to crank up the bit depth enough to the point where you're not losing perceptual fidelity), and just do everything in linear light.
In fact if you just encoded the linear values as floats that would probably give you best of both worlds, since floating point is basically log-encoding where density of floats is lower at the higher end of the range.
If you kept it linear all the way to the output pixels, it would look fine. You only have to go nonlinear because the screen expects nonlinear data. The screen expects this because it saves a few bits, which is nice but far from necessary.
To put it another way, it appears so dark because it isn't being "displayed directly". It's going directly out to the monitor, and the chip inside the monitor is distorting it.
A better discriminator might be global edits vs local edits, with local edits being things like retouching specific parts of the image to make desired changes, and one could argue that local edits are "more fake" than global edits, but it still depends on a thousand factors, most importantly intent.
"Fake" images are images with intent to deceive. By that definition, even an image that came straight out of the camera can be "fake" if it's showing something other than what it's purported to (e.g. a real photo of police violence but with a label saying it's in a different country is a fake photo).
What most people think when they say "fake", though, is a photo that has had filters applied, which makes zero sense. As the post shows, all photos have filters applied. We should get over that specific editing process, it's no more fake than anything else.
Even that isn't all that clear-cut. Is noise removal a local edit? It only touches some pixels, but obviously, that's a silly take.
Is automated dust removal still global? The same idea, just a bit more selective. If we let it slide, what about automated skin blemish removal? Depth map + relighting, de-hazing, or fake bokeh? I think that modern image processing techniques really blur the distinction here because many edits that would previously need to be done selectively by hand are now a "global" filter that's a single keypress away.
Intent is the defining factor, as you note, but intent is... often hazy. If you dial down the exposure to make the photo more dramatic / more sinister, you're manipulating emotions too. Yet, that kind of editing is perfectly OK in photojournalism. Adding or removing elements for dramatic effect? Not so much.
The only process in the article that involves nearby pixels is to combine R G and B (and other G) into one screen pixel. (In principle these could be mapped to subpixels.) Everything fancier than that can be reasonably called some fake cosmetic bullshit.
Removing dust and blemishes entails looking at more than one pixel at a time.
Nothing in the basic processing described in the article does that.
Raw formats usually carry "Bayer-filtered linear (well, almost linear) light in device-specific color space", not necessarily "raw unprocessed readings from the sensor array", although some vendors move it slightly more towards the latter than others.
The ones that make the annual rounds up here in New England are those foliage photos with saturation jacked. “Look at how amazing it was!” They’re easy to spot since doing that usually wildly blows out the blues in the photo unless you know enough to selectively pull those back.
If you want reality, go there in person and stop looking at photos. Viewing imagery is a fundamentally different type of experience.
We’ve had similar debates about art using miniatures and lens distortions versus photos since photography was invented — and digital editing fell on the lens trick and miniature side of the issue.
Portrait photography -- no, people don't look like that in real life with skin flaws edited out
Landscape photography -- no, the landscapes don't look like that 99% of the time, the photographer picks the 1% of the time when it looks surreal
Staged photography -- no, it didn't really happen
Street photography -- a lot of it is staged spontaneously
Product photography -- no, they don't look like that in normal lighting
Artists, who use these tools with clear vision and intent to achieve specific goals, strangely never have this problem.
So there are levels of image processing, and it would be wrong to dump them all in the same category.
What about this? https://news.ycombinator.com/item?id=35107601
You can look it up because it's published on the web but IIRC it's generally what you'd expect. It's okay to do whole-image processing where all pixels have the same algorithm applied like the basic brightness, contrast, color, tint, gamma, levels, cropping, scaling, etc filters that have been standard for decades. The usual debayering and color space conversions are also fine. Selectively removing, adding or changing only some pixels or objects is generally not okay for journalistic purposes. Obviously, per-object AI enhancement of the type many mobile phones and social media apps apply by default don't meet such standards.
I downsized it to 170x170 pixels
My point is that there IS an experiment which would show that Samsung is doing some nonstandard processing likely involving replacement. The evidence provided is insufficient to show that
Filters themselves don't make it fake, just like words themselves don't make something a lie. How the filters and words are used, whether they bring us closer or further from some truth, is what makes the difference.
Photos implicitly convey, usually, 'this is what you would see if you were there'. Obviously filters can help with that, as in the OP, or hurt.
i.e. Camera+Lens+ISO+SS+FStop+FL+TC (If present)+Filter (If present). Add focus distance if being super duper proper.
And some of that is to help at least provide the right requirements to try to recreate.
A fun tangent on the "green cast" mentioned in the post: the reason the Bayer pattern is RGGB (50% green) isn't just about color balance, but spatial resolution. The human eye is most sensitive to green light, so that channel effectively carries the majority of the luminance (brightness/detail) data. In many advanced demosaicing algorithms, the pipeline actually reconstructs the green channel first to get a high-resolution luminance map, and then interpolates the red/blue signals—which act more like "color difference" layers—on top of it. We can get away with this because the human visual system is much more forgiving of low-resolution color data than it is of low-resolution brightness data. It’s the same psycho-visual principle that justifies 4:2:0 chroma subsampling in video compression.
Also, for anyone interested in how deep the rabbit hole goes, looking at the source code for dcraw (or libraw) is a rite of passage. It’s impressive how many edge cases exist just to interpret the "raw" voltages from different sensor manufacturers.
When I worked at Amazon on the Kindle Special Offers team (ads on your eink Kindle while it was sleeping), the first implementation of auto-generated ads was by someone who didn't know that properly converting RGB to grayscale was a smidge more complicated than just averaging the RGB channels. So for ~6 months in 2015ish, you may have seen a bunch of ads that looked pretty rough. I think I just needed to add a flag to the FFmpeg call to get it to convert RGB to luminance before mapping it to the 4-bit grayscale needed.
I remember trying out some of the home-made methods while I was implementing a creative work section for a school assignment. It’s surprising how "flat" the basic average looks until you actually respect the coefficients (usually some flavor of 0.21R + 0.72G + 0.07B). I bet it's even more apparent in a 4-bit display.
This is the coefficients I use regularly.
The JPEGs cameras produce are heavily processed, and they are emphatically NOT "original". Taking manual control of that process to produce an alternative JPEG with different curves, mappings, calibrations, is not a crime.
I know my Sony gear can't call out to AI because the WIFI sucks like every other Sony product and barely works inside my house, but also I know the first ILC manufacturer that tries to put AI right into RAW files is probably the first to leave part of the photography market.
That said I'm a purist to the point where I always offer RAWs for my work [0] and don't do any photoshop/etc. D/A, horizon, bright adjust/crop to taste.
Where phones can possibly do better is the smaller size and true MP structure of a cell phone camera sensor, makes it easier to handle things like motion blur. and rolling shutter.
But, I have yet to see anything that gets closer to an ILC for true quality than the decade+ old pureview cameras on Nokia cameras, probably partially because they often had sensors large enough.
There's only so much computation can do to simulate true physics.
[0] - I've found people -like- that. TBH, it helps that I tend to work cheap or for barter type jobs in that scene, however it winds up being something where I've gotten repeat work because they found me and a 'photoshop person' was cheaper than getting an AIO pro.
if you take that away, a picture is not very interesting, it's hyperrealistic so not super creative a lot of the time (compared to eg paintings), & it doesn't even require the mastery of other mediums to get hyperrealistism
this is totally out of my own self-interest, no problems with its content
also your question implies a bad assumption even if you disclaim it. if you don't want to imply a bad assumption the way to do that is to not say the words, not disclaim them
“NO EM DASHES” is common system prompt behavior.
I'm imagining a sort of Logan's Run-like scifi setup where only people with a documented em dash before November 30, 2022, i.e. D(ash)-day, are left with permission to write.
I have actually been deliberately modifying my long-time writing style and use of punctuation to look less like an LLM. I'm not sure how I feel about this.
But now, likewise, having to bail on emdashes. My last differentiator is that I always close set the emdash—no spaces on either side, whereas ChatGPT typically opens them (AP Style).
Russians use this for at least 15 years
what's so special about green? oh so just because our eyes are more sensitive to green we should dedicate double the area to green in camera sensors? i mean, probably yes. but still. (⩺_⩹)
There is no such thing as “unprocessed” data, at least that we can perceive.
From the classic file format "ppm" (portable pixel map) the ppm to pgm (portable grayscale map)
https://linux.die.net/man/1/ppmtopgm
The quantization formula ppmtopgm uses is g = .299 r + .587 g + .114 b.
I also love the quote in there...
Quote
Cold-hearted orb that rules the night
Removes the colors from our sight
Red is gray, and yellow white
But we decide which is right
And which is a quantization error.
Is the output produced by the sensor RGB or a single value per pixel?
R G B
B R G
G B R
? R G
G B
Each RGB pixel would be 2x2 grid of
``` G R B G ```
So G appears twice as many as other colors (this is mostly the same for both the screen and sensor technology).
There are different ways to do the color filter layouts for screens and sensors (Fuji X-Trans have different layout, for example).
G G R R
G G R R
B B G G
B B G G
In front of the sensor is a bayer filter which results in each physical pixel seeing illumination filtered R G or B.
From there the software onboard the camera or in your RAW converter does interpolation to create RGB values at each pixel. For example if the local pixel is R filtered, it then interpolates its G & B values from nearby pixels of that filter.
https://en.wikipedia.org/wiki/Bayer_filter
There are alternatives such as what Fuji does with its X-trans sensor filter.
https://en.wikipedia.org/wiki/Fujifilm_X-Trans_sensor
Another alternative is Foveon (owned by Sigma now) which makes full color pixel sensors but they have not kept up with state of the art.
https://en.wikipedia.org/wiki/Foveon_X3_sensor
This is also why Leica B&W sensor cameras have higher apparently sharpness & ISO sensitivity than the related color sensor models because there is no filter in front or software interpolation happening.
Works great. Most astro shots are taken using a monochrome sensor and filter wheel.
> filters are something like quantum dots that can be turned on/off
If anyone has this tech, plz let me know! Maybe an etalon?
https://en.wikipedia.org/wiki/Fabry%E2%80%93P%C3%A9rot_inter...
I have no idea, it was my first thought when I thought of modern color filters.
AKA imagine a camera with R/G/B filters being quickly rotated out for 3 exposures, then imagine it again but the technology is integrated right into the sensor (and, ideally, the sensor and switching mechanism is fast enough to read out with rolling shutter competitive with modern ILCs)
Edit or maybe it does work? I've watched at least one movie on a DLP type video projector with sequential colour and not noticed colour fringing. But still photos have much higher demand here.
https://en.wikipedia.org/wiki/Pixel_shift
EDIT: Sigma also has "Foveon" sensors that do not have the filter and instead stacks multiple sensors (for different wavelengths) at each pixel.
He take a few minutes to get to the punch line. Feel free to skip ahead to around 5:30.
Generally we shoot “flat” (there are so many caveats to this but I don’t feel like getting bogged down in all of it. If you plan on getting down and dirty with colors and really grading, you generally shoot flat). The image that we handover to DIT/editing can be borderline grayscale in its appearance. The colors are so muted, the dynamic range is so wide, that you basically have a highly muted image. The reason for this is you then have the freedom to “push” the color and look and almost any direction, versus if you have a very saturated, high contrast image, you are more “locked” into that look. This matters more and more when you are using a compressed codec and not something with an incredibly high bitrate or raw codecs, which is a whole other world and I am also doing a bit of a disservice to by oversimplifying.
Though this being HN it is incredibly likely I am telling few to no people anything new here lol
It's sort of the opposite of what's going on with photography, where you have a dedicated "raw" format with linear readings from the sensor. Without these formats, someone would probably have invented "log JPEG" or something like that to preserve more data in highlights and in the shadows.
[0] - https://en.wikipedia.org/wiki/Super_CCD#/media/File:Fuji_CCD...
Processing these does seem like more fun though.
It gets even wilder when perceiving space and time as additional signal dimensions.
I imagine a sort of absolute reality that is the universe. And we’re all just sensor systems observing tiny bits of it in different and often overlapping ways.
I’ve been staring at 16-bit HDR greyscale space for so long…
I spent a good part of my career, working in image processing.
That first image is pretty much exactly what a raw Bayer format looks like.
Something that surprised me is that very little of the computation photography magic that has been developed for mobile phones has been applied to larger DSLRs. Perhaps it's because it's not as desperately needed, or because prior to the current AI madness nobody had sufficient GPU power lying around for such a purpose.
For example, it's a relatively straightforward exercise to feed in "dark" and "flat" frames as extra per-pixel embeddings, which lets the model learn about the specifics of each individual sensor and its associated amplifier. In principle, this could allow not only better denoising, but also stretch the dynamic range a tiny bit by leveraging the less sensitive photosites in highlights and the more senstive ones in the dark areas.
Similarly, few if any photo editing products do simultaneous debayering and denoising, most do the latter as a step in normal RGB space.
Not to mention multi-frame stacking that compensates for camera motion, etc...
The whole area is "untapped" for full-frame cameras, someone just needs to throw a few server grade GPUs at the problem for a while!
== Tim's Vermeer ==
Specifically Tim's quote "There's also this modern idea that art and technology must never meet - you know, you go to school for technology or you go to school for art, but never for both... And in the Golden Age, they were one and the same person."
https://en.wikipedia.org/wiki/Tim%27s_Vermeer
https://www.imdb.com/title/tt3089388/quotes/?item=qt2312040
== John Lind's The Science of Photography ==
Best explanation I ever read on the science of photography https://johnlind.tripod.com/science/scienceframe.html
== Bob Atkins ==
Bob used to have some incredible articles on the science of photography that were linked from photo.net back when Philip Greenspun owned and operated it. A detailed explanation of digital sensor fundamentals (e.g. why bigger wells are inherently better) particularly sticks in my mind. They're still online (bookmarked now!)
https://www.bobatkins.com/photography/digital/size_matters.h...
What bothers me as an old-school photographer is this. When you really pushed it with film (e.g. overprocess 400ISO B&W film to 1600 ISO and even then maybe underexpose at the enlargement step) you got nasty grain. But that was uniform "noise" all over the picture. Nowadays, noise reduction is impressive, but at the cost of sometimes changing the picture. For example, the IP cameras I have, sometimes when I come home on the bike, part of the wheel is missing, having been deleted by the algorithm as it struggled with the "grainy" asphalt driveway underneath.
Smartphone and dedicated digital still cameras aren't as drastic, but when zoomed in, or in low light, faces have a "painted" kind of look. I'd prefer honest noise, or better yet an adjustable denoising algorithm from "none" (grainy but honest) to what is now the default.
throw310822•2h ago
delecti•1h ago
throw310822•1h ago
But anyway, I enjoyed the article.