  We present a new approach to the numerical upscaling for elliptic problems
with rough diffusion coefficient at high contrast. It is based on the
localizable orthogonal decomposition of $H^1$ into the image and the kernel of
some novel stable quasi-interpolation operators with local $L^2$-approximation
properties, independent of the contrast. We identify a set of sufficient
assumptions on these quasi-interpolation operators that guarantee in principle
optimal convergence without pre-asymptotic effects for high-contrast
coefficients. We then give an example of a suitable operator and establish the
assumptions for a particular class of high-contrast coefficients. So far this
is not possible without any pre-asymptotic effects, but the optimal convergence
is independent of the contrast and the asymptotic range is largely improved
over other discretisation schemes. The new framework is sufficiently flexible
to allow also for other choices of quasi-interpolation operators and the
potential for fully robust numerical upscaling at high contrast.
