Let's see some differences between linear gamma space (gamma = 1) and "perceptually uniform" spaces, usually having gamma = 1.8..2.4
CCD/CMOS sensors have linear response to light. That is, they act not in the same way human senses do. For human eye, 100W bulb is not twice as bright as 50W bulb. But for sensor, it is the case - sensor output will be times two for 100W compared to the output sensor produces being litvwith 50W bulb.
Major reason to use linear gamma for editing is that we work directly with camera output, accessing real data coming from the sensor.
Linear response of sensor means that if we are taking photos of evenly lit surface varying exposure one step, resulting readings from the sensor will vary exactly two times for each step. So, in linear space it is very easy to correlate linear histogram to exposure compensation. In non-linear cases, when the signal is subjected to unknown or undisclosed transform, one can't do so.
Please review http://pochtar.com/gamut_view/gamma.htm
Effectively, linear response means that shadows are compressed compared to perceptually uniform response curve, while highlights are decompressed. That is why with linear data small manipulations in the shadow part of the image have such a strong effect, while results of manipulations in highlight portion are much less noticeable.
If one uses integer arithmetic, it is impossible to effectively manipulate those highly compressed shadows in gamma=1 space, where difference in 1eV is just 3 to 6 levels (R=G=B=3 is -4eV, while R=G=B=6 is -3eV) - because of rounding errors. Even in gamma=2.2 decompression full stop is only 13 levels in shadow zone. But with floating point this is not an issue.
For extracting details from shadows in perceptually uniform spaces with integer arithmetic our manipulations are limited, because once we go past "safe" slope of the curve, we encounter banding and posterization due to same problems of rounding in integer. But we need really extreme moves here quite often, as shadows are decompressed.
Highlights are also difficult in non-linear spaces because noise starts to show up.
Control over linear data is actually control of digital development. The goal of curve manipulations here is to extract maximum details and establish maximum dynamic range with regards to noise. That is, the goal is the same as with any development - extract maximum information by varying development process (we use dilution, temperature, time, agitation, chemical composition etc in endless combinations; obtaining results through trial and error - only to control that characteristic curve in wet lab; while in digital we have direct access to it!). (As an additional benefit, here it is possible to bring the image closer to visually pleasing look.)
After this is done, and our digital film is processed, we go to postprocessing.
CCD/CMOS sensors have linear response to light. That is, they act not in the same way human senses do. For human eye, 100W bulb is not twice as bright as 50W bulb. But for sensor, it is the case - sensor output will be times two for 100W compared to the output sensor produces being litvwith 50W bulb.
Major reason to use linear gamma for editing is that we work directly with camera output, accessing real data coming from the sensor.
Linear response of sensor means that if we are taking photos of evenly lit surface varying exposure one step, resulting readings from the sensor will vary exactly two times for each step. So, in linear space it is very easy to correlate linear histogram to exposure compensation. In non-linear cases, when the signal is subjected to unknown or undisclosed transform, one can't do so.
Please review http://pochtar.com/gamut_view/gamma.htm
Effectively, linear response means that shadows are compressed compared to perceptually uniform response curve, while highlights are decompressed. That is why with linear data small manipulations in the shadow part of the image have such a strong effect, while results of manipulations in highlight portion are much less noticeable.
If one uses integer arithmetic, it is impossible to effectively manipulate those highly compressed shadows in gamma=1 space, where difference in 1eV is just 3 to 6 levels (R=G=B=3 is -4eV, while R=G=B=6 is -3eV) - because of rounding errors. Even in gamma=2.2 decompression full stop is only 13 levels in shadow zone. But with floating point this is not an issue.
For extracting details from shadows in perceptually uniform spaces with integer arithmetic our manipulations are limited, because once we go past "safe" slope of the curve, we encounter banding and posterization due to same problems of rounding in integer. But we need really extreme moves here quite often, as shadows are decompressed.
Highlights are also difficult in non-linear spaces because noise starts to show up.
Control over linear data is actually control of digital development. The goal of curve manipulations here is to extract maximum details and establish maximum dynamic range with regards to noise. That is, the goal is the same as with any development - extract maximum information by varying development process (we use dilution, temperature, time, agitation, chemical composition etc in endless combinations; obtaining results through trial and error - only to control that characteristic curve in wet lab; while in digital we have direct access to it!). (As an additional benefit, here it is possible to bring the image closer to visually pleasing look.)
After this is done, and our digital film is processed, we go to postprocessing.
) to improve shadow recovery.
