301–310 of 418 posts
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Right. the statement "Here’s a photo of a Christmas tree, as my camera’s sensor sees it" is incoherent.
Don't you think you're being a bit too pedantic? Nothing really "sees". Eyes also do gather light and the brain analyzes the signals. We've invented a word for it but the word is a high level abstraction that could easily be applied to a camera sensor as well.
But does applying the same transfer function to each pixel (of a given colour anyway) count as "processing"? 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…
My theory is that this is trying to do denoising after capturing the image with a high ISO. I personally hate that look.
On my dedicated still camera I almost always set ISO to be very low (ISO 100) and only shoot people when lighting is sufficient. Low light is challenging and I’d prefer not to deal with it when shooting people, unless making everything dark is part of the artistic effect I seek.
On the other hand on my smartphone I just don’t care that much. It’s mostly for capturing memories in situations where bringing a dedicated camera is impossible.
I think everyone agrees that dynamic range compression and de-Bayering (for sensors which are colour-filtered) are necessary for digital photography, but at the other end of the spectrum is "use AI to recognise objects and hallucinate what they 'should' look like" --- and despite how everyone would probably say that isn't a real photo anymore, it seems manufacturers are pushing strongly in that direction, raising iss…
One thing I've learned while dabbling in photography is that there are no "fake" images, because there are no "real" images. Everything is an interpretation of the data that the camera has to do, making a thousand choices along the way, as this post beautifully demonstrates. A better discriminator might be global edits vs local edits, with local edits being things like retouching specific parts of the image to make d…
Ditto for black and white photos. Your visual perception has pretty high dynamic range. Not least because your eyes move and your brain creates a representation that gives you the illusion of higher dynamic range than what your eyes can actually deliver. So when you want to represent it using a technology that can only give you a fraction of the dynamic range you (or your camera) can see, you sometimes make local’ish edits (eg create a mask with brush or gradients to lighten or darken regions)
Ansel Adams did a lot of dodging and burning in his prints. Some of the more famous ones are very obvious in terms of having been “processed” during the exposure of the print.
I see this as overcoming the limitations in conveying what your eyes/brain will see when using the limited capabilities of camera/screen/print. It is local’ish edits, but the intent isn’t so much to deceive as it is to nudge information into a range where it can be seen/understood.
Technology Connections vid: https://youtu.be/va1rzP2xIx4
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The bit about the green over-representation in camera color filters is partially correct. Human color sensitivity varies a lot from individual to individual (and not just amongst individuals with color blindness), but general statistics indicate we are most sensitive to red light. The main reason is that green does indeed overwhelmingly contribute to perceptual luminance (over 70% in sRGB once gamma corrected: https:…
> 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 hallucinat…
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Worse, "NTSC" is not a single standard, Japan deviated it too much that the primaries are defined by their own ARIB (notably ~9000 K white point). ... okay, technically PAL and SECAM too, but only in audio (analogue Zweikanalton versus digital NICAM), bandwidth placement (channel plan and relative placement of audio and video signals, and, uhm, teletext) and, uhm, teletext standard (French Antiope versus Britain's Te…
(this is just a rant) Honestly, the weird 16-239 (on 8-bit) color range and 60000/1001 fps limitations stem from the original NTSC standard, which considering both the Japanese NTSC adaptation and European standards do not have is rather frustating nowadays. Both the HDVS and HD-MAC standards define it in precise ways (exactly 60 fps for HDVS and 0-255 color range for HD-MAC*) but America being America... * I know th…
I pulled it from some old academia papers about why you can’t just max(uv.rgb) to do greyscale nor can you do float val = uv.r
This further gets funky when we have BGR vs RGB and have to swivel the bytes beforehand.
Thanks for adding clarity and history to where those weights came from, why they exist at all, and the decision tree that got us there.
People don’t realize how many man hours went into those early decisions.
I love posts that peel back the abstraction layer of "images." It really highlights that modern photography is just signal processing with better marketing. 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 t…
> The human eye is most sensitive to green light, This argument is very confusing: if is most sensitive, less intensity/area should be necessary, not more.
So to describe/reproduce what our eyes see you need more detection range in the green spectrum
Ok I just never imagined that photons hitting camera lenses would not produce a “raw” image that made sense to my eyes - I am stunned and this is a fantastic addition to the canon of things one should know about the modern world. (I also just realised that the world become more complex than I could understand when some guy mixed two ochres together and finger painted a Woolly Mammoth.)
Our brains are far more impressive than what amounts to fairly trivial signal processing done on digital images.
What I am curious if are the different symmetrical arrangements chosen by such crystals and how they compare with Bayer pattern. The analogy being X becomes a site for green and the other two for red and blue.