Here is the attractive property that resizing through a gamma space does have, and that resizing through a sigmoidal space does not:

Suppose (for simplicity) that the three channels of an image are proportional, for example that

R=rL

G=gL

B=bL

where L is a function of position, but r and g and b are constant.

Suppose also that the resampling operator is linear.

Then, if we resample by first transforming R, G and B with a power law (that is, if we map the colors to a "gamma color space"), apply the linear resampling operator, and then apply the inverse of the power transformation (that is, if we undo gamma), the resulting image will also have proportional channels.

Consequently, if (locally) having (approximately) proportional channels describes an important property of images (in an "anchor" color space), resampling through a gamma space is a better proposition than through a sigmoidal color space.

Note: For this to work out as intended in practice, we must have that L, R, G and B are >= 0.

**P.S.** This property, about the interaction of gamma transformations with linear filtering, has to be well known.