Comment by inopinatus | Hacker News Reader

Comment by inopinatus | Hacker News Reader

Um. Curiously, that first example didn't work for me. Figures 1 & 2, under "Light emission vs perceptual brightness" are compared thus: "On which image does the gradiation appear more even? It’s the second one!"

Except that for me it isn't. The first one, graded by emission rather than perception, appears more evenly graded to me. There is no setting I can find using the Apple calibration tool (even in expert mode) that does anything but strengthen this perception.

This raises only questions. Is this discrepancy caused by my Apple Thunderbolt Display? By my mild myopia? The natural lighting? My high-protein diet? The jazz on the stereo? The NSA? Or do I really have a different perception of light intensity?

And is anyone else getting the same?

Note: I have always had trouble with gamma correction during game setup; there has never been a setting I liked. Typically there'll be a request to adjust gamma until a character disappears, but however I fiddle things it never does.

I highly doubt you perceive light intensity radically differently to your fellow humans... :) Most likely your monitor is miscalibrated.

Try a few different gamma calibration images from other sources (Google Images -> "gamma calibration") and if they consistently indicate that your monitor is miscalibrated, then you have your answer.

Well he never said he was a human. For all we know he could be The Terminator trying to find John Connor in Google Maps.

Instant upvote :)

How do you know GP is male?

I have the same problem as the GP, on four monitors. All four, bought at different times in different countries, are miscalibrated according to these calibration images. Is that plausible? If not, what might be going on?

And if it _is_ plausible that four random monitors are all miscalibrated in the same way, why should we optimize for well-calibrated monitors?

If you read the article carefully, you'll realise that this is not just an optimisation (check for example the sections on colour blending and rendering; those artifacts are gamma calibration independent.)

I'm on a Retina Macbook Pro, and the white/black checkerboard square (A in Figure 12) matches the (128,128,128) gray (B), not the lighter one (C).

Looking closer, it seems like my computer is, like, anti-aliasing the image itself. In Digital Color Meter, the white and black pixels are both grays. See screenshot below, the magnified area is from square A in the browser. When I downloaded the image and opened it in Preview it is black and white like it's supposed to be.

Screenshot: https://cl.ly/2T2U2J0A3v31 (If you're on a Mac maybe try opening the image in an image viewer)

Anyone know what's going on? I also can't distinguish between the first few black bars in Figure 2.

When I disobey the don't-zoom directive ("These examples will only work if your browser doesn’t do any rescaling on the images below") I see what your screenshot has. Try pressing Cmd+0 (command zero) to reset your browser zoom to Actual Size and then the pattern should match C instead of B.

EDIT: Sorry, the above applies to my non-retina external display that I have hooked up to my retina macbook. When I view the test images on my internal retina display, I do see the issue you describe (pattern matches B). If I press cmd+- (command minus) a few times until I'm at a 50% zoom level, the issue is resolved and the pattern matches C! Makes some sense actually, since showing a normal dpi photo at 50% on a retina makes for a 1:1 pixel mapping :) Showing an image at 100% on a retina makes for a 1:2 pixel mapping (each pixel from the image ends up being 2x2=4 physical pixels), which disobeys the don't-rescale directive.

I also had trouble calling either "more even". The first one has greater division at the darker end, and the second at the lighter, but they about balance out.

The extreme for me was figure 12. A and B are so similar I can't see the line between them, but C (the "corrected" square) is a completely different shade.

I'm viewing on a data projector. That's probably the reason. Still, it makes me skeptical that there's anything display-agnostic you can do for gamma.

If the resolution you're sending into the projector matches the native/optimal/recommended resolution of the projector (in at least the long dimension, and it will letterbox the other dimension), and you set the digital keystone to neutral/zero, you will achieve a 1:1 pixel mapping that is required to be in compliance with the author's statement that "These examples will only work if your [browser|projector|etc]* doesn’t do any rescaling on the images below."

* Author only said browser, but actually everything in the chain matters. If you're not at a 1:1 pixel mapping you're resampling, and resampling breaks the checkerboard example. Digital keystone (but not optical keystone with tilting lenses) included.

More even = an even gradation from black to white.

The major point the article makes is not about any particular display. It is about image processing algorithms, such as color blending, anti-aliasing, resizing, etc.

All these algorithms assume they are performing math on linear scale measurements of physical light. However, most image data is not encoded as linear scale samples of light intensity. Instead they are gamma encoded.

What the article gets slightly wrong though is that images are not gamma encoded to deal with the non linear response to intensity of the human eye. Instead, it's to deal with the non linear response of CRT displays to linear amounts of voltage, as produced by camera sensors. The gamma encoding adjusts the image data so that a display will correctly produce linear scales of light intensity to match the physical light measured from a scene.

You are rightly skeptical that Gamma Encoding can't really deal with the broad variety of different displays. However, it is still the case that most images are gamma encoded with roughly gamma 2.2, and that all image processing algorithms on the other hand assume gamma 1.0, and misbehave on data that is gamma 2.2

It is, of course still the case that by chance, human visual response is roughly the inverse of gamma 2.2. But bringing this up while trying to make a point about performing operations on linear gamma data is somewhat distracting.

I thought the same, but then in the rest of the post the gamma adjusted images looked better.

Hey - I'm viewing the page on a macbook air and see what you see.

Reply on news.ycombinator.com