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| author | Jörg Frings-Fürst <debian@jff-webhosting.net> | 2014-09-01 13:56:46 +0200 |
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| committer | Jörg Frings-Fürst <debian@jff-webhosting.net> | 2014-09-01 13:56:46 +0200 |
| commit | 22f703cab05b7cd368f4de9e03991b7664dc5022 (patch) | |
| tree | 6f4d50beaa42328e24b1c6b56b6ec059e4ef21a5 /doc/gamma.html | |
Initial import of argyll version 1.5.1-8debian/1.5.1-8
Diffstat (limited to 'doc/gamma.html')
| -rw-r--r-- | doc/gamma.html | 64 |
1 files changed, 64 insertions, 0 deletions
diff --git a/doc/gamma.html b/doc/gamma.html new file mode 100644 index 0000000..2a9cec3 --- /dev/null +++ b/doc/gamma.html @@ -0,0 +1,64 @@ +<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> +<html> +<head> + <title>About Gamma</title> + <meta http-equiv="content-type" + content="text/html; charset=ISO-8859-1"> +</head> +<body> +<h2 style="text-decoration: underline; font-weight: bold;">About Gamma</h2> +When calibrating display devices, the notion of "gamma value" quickly +becomes a topic for discussion. Various numbers are often bandied about +as if they have a well known and accepted meaning, but it turns out +that gamma values are not a very precise way of specifying real world +device behavior at all.<br> +<br> +A "gamma" curve is typically thought of as an ideal power curve, but no +real world device has the necessary zero output at zero input to be +able to match such a curve, and in general a display may not exactly +reproduce an idealized power curve shape at all. The consequence of +this is that there are countless ways of matching a real world curve +with the ideal gamma power one, and each different method of matching +will result in a different notional gamma value.<br> +<br> +Argyll's approximate specification and reading is simply the gamma of +the ideal curve that matches the real 50% stimulus relative-to-white +output level. I think this is a reasonable (robust and simple) +approximation, +because it matches the overall impression of brightness for an image. A +more sophisticated approximation that could be adopted would be to +locate the idea power curve that minimizes the total delta E of some +collection of test values, but there are still many details that the +final result will depend on, such as what distribution of test values +should be used, what delta E measure should be used, and how can a +delta E be computed if the colorimetric behavior of the device is not +known ? Some approaches do things such as minimize the sum of the +squares of the output value discrepancy for linearly sampled input +values, and while this is mathematically elegant, it is hard to justify +the choice of device space as the metric.<br> +<br> +There are many other ways in which it could be done, and any such +approximation may have a quite different numerical value, even though +the visual result is very similar. This is because the numerical power +value is very sensitive to what's happening near zero, the very point +that is non-ideal. Consider the sRGB curve for instance. It's +technically composed of a power curve segment with a power of 2.4, but +when combined with its linear segment near zero, has an overall curve +best approximated by a power curve of gamma 2.2. Matching the 50% +stimulus would result in yet another slightly different approximation +value of about 2.224. All these different gamma values represent curves +that are very visually similar.<br> +<br> +<img style="width: 400px; height: 400px;" + alt="Plot of sRGB curve vs. power of 2.224" src="srgbplot.gif" + align="left"><br> +<br> +The result of this ambiguity about what gamma values mean when applied +to real world curves, is that it shouldn't be expected that there are +going to be good matches between various gamma numbers, even for curves +that are very visually similar, unless the precise method of matching +the ideal gamma curve to the real world curve is known.<br> +<br> +<br> +</body> +</html> |