The DSLR, even in sRGB JPEG mode, holds detail in the gray card at 3+ stops of overexposure in this case.

This is because when Canon says these JPEGs are “sRGB,” that defines their correct profile for display, but not necessarily their exact encoding. The encoding profile — the color adjustments and tone curve used to convert the linear raw sensor data to a viewable image — may be based on the sRGB curve, but it has some subjectivity baked into it; likely a little bit of s-curve contrast, and some highlight rolloff.

And that’s with the “Standard” Picture Profile, sRGB, and JPEG — likely the least dynamic range this camera would ever present. A raw file, log video, or even a less-contrasty profile could offer a significantly gentler highlight treatment.

If you work in linear-light, you’re doing things right — but if you want your results to look pleasing and/or photographed, an sRGB lookup alone is not good enough.

sRGB and Gamma Visualized

Before we skewer the sRGB “gamma” as a view transform, let’s examine what it actually is.

First, some terminology. Strictly-speaking, gamma is a power function. A gamma of 2.2 is the same as raising the pixel value, on a 0.0–1.0 scale, to the power of 1/2.2. But the term gamma has been broadened by some to include any kind of 1D tone curve applied to, or characteristic of, an image. Life is easier with this relaxed definition, so that’s how I use it.

Gamma Management is the term I use for a workflow that uses 1D lookups/conversions between formats. Magic Bullet Looks 5 and Supercomp 1.5 use Gamma Management rather than full color management.

You can absolutely gamma-manage your workflow using the pure gamma-2.2 and its inverse. But if your imagery is sRGB, it’s slightly more accurate to use the sRGB curve. The sRGB tone curve is a very close match to a pure gamma 2.2, but it has a little kink at the bottom to solve an old problem.

A pure gamma curve has a slope of 1.0 or 0.0 at its base, i.e. as the values in the image approach zero, the gamma curve approaches a flat line. This means that calculations on the darkest pixels in your image could be inaccurate, and those inaccuracies could compound through multiple steps of linearization and de-linearization.

sRGB has a steep, but not infinitely steep, linear slope at the very bottom, and then the rest of the curve uses a gamma of 2.4 squished to fit in the remaining range. The clever result is that the curve is smooth at the transition and robust through multiple generations of processing, even if the processing is not done in floating-point.

It’s easy to see how similar the gamma 2.2 and sRGB curves are by graphing them: