> The quantization formula ppmtopgm uses is g = .299 r + .587 g + .114 b.

Seriously. We can trust linux man pages to use the same 1-letter variable name for 2 different things in a tiny formula, can't we?

Funnily enough that's not the only mistake he made in that article. His final image is noticeably different from the camera's output image because he rescaled the values in the first step. That's why the dark areas look so crushed, eg around the firewood carrier on the lower left or around the cat, and similarly with highlights, e.g. the specular highlights on the ornaments.

After that, the next most important problem is the fact he operates in the wrong color space, where he's boosting raw RGB channels rather than luminance. That means that some objects appear much too saturated.

So his photo isn't "unprocessed", it's just incorrectly processed.

If someone's curious about those particular constants, they're the PAL Y' matrix coefficients: https://en.wikipedia.org/wiki/Y%E2%80%B2UV#SDTV_with_BT.470

I wouldn't worry about it too much, looking at ads is always a shitty experience. Correctly grayscaled or not.

I don't think Kindle ads were available in my region in 2015 because I don't remember seeing these back then, but you're a lucky one to fix this classic mistake :-)

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.

> Small neural networks I believe are the current state of the art (e.g. train to reverse a 16x16 color filter pattern for the given camera). What is currently in use by modern digital cameras is all trade secret stuff.

Considering you usually shoot RAW, and debayer and process in post, the camera hasn't done any of that.

It's only smartphones that might be doing internal AI Debayering, but they're already hallucinating most of the image anyway.

> we are most sensitive to red light

> green does indeed overwhelmingly contribute to perceptual luminance

so... if luminance contribution is different from "sensitivity" to you - what do you imply by sensitivity?

Is it related to the fact that monkeys/humans evolved around dense green forests ?

Exactly - film photographers heavily process(ed) their images from the film processing through to the print. Ansel Adams wrote a few books on the topic and they’re great reads.

And different films and photo papers can have totally different looks, defined by the chemistry of the manufacturer and however _they_ want things to look.

True, but there may be different intentions behind the processing.

Sometimes the processing has only the goal to compensate the defects of the image sensor and of the optical elements, in order to obtain the most accurate information about the light originally coming from the scene.

Other times the goal of the processing is just to obtain an image that appears best to the photographer, for some reason.

For casual photographers, the latter goal is typical, but in scientific or technical applications the former goal is frequently encountered.

Ideally, a "raw" image format is one where the differences between it and the original image are well characterized and there are no additional unknown image changes done for an "artistic" effect, in order to allow further processing when having either one of the previously enumerated goals.

Thanks! First hit online: https://www.lab404.com/3741/readings/sontag.pdf

Out of curiosity: what led you to write "Susan Sunday" instead of "Susan Sontag"? (for other readers: "Sonntag" is German for "Sunday")

Great series of articles!

Protanopia and protanomaly shift luminance perception away from the longest wavelengths of visible light, which causes highly-saturated red colours to appear dark or black. Deuteranopia and deuteranomaly don't have this effect. [1]

Blue cones make little or no contribution to luminance. Red cones are sensitive across the full spectrum of visual light, but green cones have no sensitivity to the longest wavelengths [2]. Since protans don't have the "hardware" to sense long wavelengths, it's inevitable that they'd have unusual luminance perception.

I'm not sure why deutans have such a normal luminous efficiency curve (and I can't find anything in a quick literature search), but it must involve the blue cones, because there's no way to produce that curve from the red-cone response alone.

[1]: https://en.wikipedia.org/wiki/Luminous_efficiency_function#C...

[2]: https://commons.wikimedia.org/wiki/File:Cone-fundamentals-wi...

The cones are the colour sensitive portion of the retina, but only make up a small percent of all the light detecting cells. The rods (more or less the brightness detecting cells) would still function in a deuteranopic person, so their luminance perception would basically be unaffected.

Also there’s something to be said about the fact that the eye is a squishy analog device, and so even if the medium wavelengths cones are deficient, long wavelength cones (red-ish) have overlap in their light sensitivities along with medium cones so…

It’s not that their M-cones (middle, i.e. green) don’t work at all, their M-cones responsivity curve is just shifted to be less distinguishable from their L-cones curve, so they effectively have double (or more) the “red sensors”.

Since the human eye is most sensitive to green, it will find errors in the green channel much easier than the others. This is why you need _more_ green data.

Because that reasoning applies to binary signals, where the sensibility is about detection, in the case of our eyes sensibility means that we can detect many more distinct values let's say we can see N distinct luminosity levels of monochrome green light but only N*k or N^k distinct levels of blue light.

So to describe/reproduce what our eyes see you need more detection range in the green spectrum

Note that there are two measurement systems involved: first the camera, and then the human eyes. Your reasoning could be correct if there were only one: "the sensor is most sensitive to green light, so less sensor area is needed".

But it is not the case, we are first measuring with cameras, and then presenting the image to human eyes. Being more sensitive to a colour means that the same measurement error will lead to more observable artifacts. So to maximize visual authenticity, the best we can do is to make our cameras as sensitive to green light (relatively) as human eyes.

Yeah, was thinking the same. If we're more sensitive, why do we need double sensors? Just have 1:1:1, and we would anyways see more of the green? Won't it be too much if we do 1:2:1, when we're already more perceptible to green?

As a mostly amateur photographer, it doesn't bother me if people ask that question. While I understand the point that the camera itself may be making some 'editing' type decision on the data first, a) in theory each camera maker has attempted to calibrate the output to some standard, b) public would expect two photos taken at same time with same model camera should look identical. That differs greatly from what often can happen in "post production" editing - you'll never find two that are identical.

I noticed this a lot when taking pictures in the mountains.

I used to have a high resolution phone camera from a cheaper phone and then later switched to an iPhone. The latter produced much nicer pictures, my old phone just produces very flat-looking pictures.

People say that the iPhone camera automatically edits the images to look better. And in a way I notice that too. But that’s the wrong way of looking at it; the more-edited picture from the iPhone actually corrresponds more to my perception when I’m actually looking at the scene. The white of the snow and glaciers and the deep blue sky really does look amazing in real life, and when my old phone captured it into a flat and disappointing looking photo with less postprocessing than an iPhone, it genuinely failed to capture what I can see with my eyes. And the more vibrant post processed colours of an iPhone really do look more like what I think I’m looking at.

I don't think it's the same, for me personally I don't like heavily processed images. But not in the sense that they need processing to look decent or to convey the perception of what it was like in real life, more in the sense that the edits change the reality in a significant way so it affects the mood and the experience. For example, you take a photo on a drab cloudy day, but then edit the white balance to make it seem like golden hour, or brighten a part to make it seems like a ray of light was hitting that spot. Adjusting the exposure, touching up slightly, that's all fine, depending on what you are trying to achieve of course. But what I see on instagram or shorts these days is people comparing their raws and edited photos, and without the edits the composition and subject would be just mediocre and uninteresting.

JPEG with OOC processing is different from JPEG OOPC (out-of-phone-camera) processing. Thank Samsung for forcing the need to differentiate.

There's a difference between an unbiased (roughly speaking) pipeline and what (for example) JBIG2 did. The latter counts as "editing" and "fake" as far as I'm concerned. It may not be a crime but at least personally I think it's inherently dishonest to attempt to play such things off as "original".

And then there's all the nonsense BigTech enables out of the box today with automated AI touch ups. That definitely qualifies as fakery although the end result may be visually pleasing and some people might find it desirable.

it's not a crime but applying post processing in an overly generous way that goes a lot further than replicating what a human sees does take away from what makes pictures interesting imho vs other mediums, that it's a genuine representation of something that actually happened.

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 ratio allows for a relatively simple 2x2 repeating pattern. That makes interpolating the values immensely simpler.

Also you don't want the red and blue to be too far apart, reconstructing the colour signal is difficult enough as it is. Moire effects are only going to get worse if you use an even sparser resolution.

Green is in the center of the visible spectrum of light (notice the G in the middle of ROYGBIV), so evolution should theoretically optimize for green light absorption. An interesting article on why plants typically reflect that wavelength and absorb the others: https://en.wikipedia.org/wiki/Purple_Earth_hypothesis

Several reasons, -Silicon efficiency (QE) peaks in the green -Green spectral response curve is close to the luminance curve humans see, like you said. -Twice the pixels to increase the effective resolution in the green/luminance channel, color channels in YUV contribute almost no details.

Why is YUV or other luminance-chrominance color spaces important for a RGB input? Because many processing steps and encoders, work in YUV colorspaces. This wasn't really covered in the article.

You think that's bad? Imagine finding out that all video still encodes colour at half resolution simply because that is how analog tv worked.

Not sure why it would invoke such strong sentiments but if you don’t like the bayer filter, know that some true monochrome cameras don’t use it and make every sensor pixel available to the final image.

For instance, the Leica M series have specific monochrome versions with huge resolutions and better monochrome rendering.

You can also modify some cameras and remove the filter, but the results usually need processing. A side effect is that the now exposed sensor is more sensitive to both ends of the spectrum.

If the Bayer pattern makes you angry, I imagine it would really piss you off to realize that the whole concept encoding an experienced color by a finite number of component colors is fundamentally species-specific and tied to the details of our specific color sensors.

To truly record an appearance without reference to the sensory system of our species, you would need to encode the full electromagnetic spectrum from each point. Even then, you would still need to decide on a cutoff for the spectrum.

...and hope that nobody ever told you about coherence phenomena.

Upon inspection, the author's personal website used em dashes in 2023. I hope this helped with your witch hunt.

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 been overusing em dashes and bulleted lists since the actual 80s, I'm sad to say. I spent much of the 90s manually typing "smart" quotes.

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.

found the guy who didn't know about em dashes before this year

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