I think the reason the example with the photo is useful is because it shows how the colourmap creates features where none exist in the data (due to the nonlinea

I think the reason the example with the photo is useful is because it shows how the colourmap creates features where none exist in the data (due to the nonlinear gradient). When looking at the photo with Jet I can see discrete areas of flat colour, like the Obama 'Hope' poster, but really these boundaries are just artifacts of the colourmap. I think the same thing happens in the arteries example in that SciPy talk. Sure it could be useful in some scenarios to highlight specific parts of the spectrum (like you mentioned with small deviations from the zero point) but at the end of the day the colourmap doesn't know anything about the data and shouldn't make any assumptions. Highlighting certain small intervals in the data could be better achieved with preprocessing, i.e. some polynomial scaling function.