Mass transportation theory studies a distance between probability distributions which can be understood as the minimum energy required to move a pile of sand representing the first distribution to a pile of sand representing the second distribution. This metric is known in differential geometry as the Wasserstein metric. In this talk, I will introduce the Wasserstein space briefly and otherwise concentrate on applications in computer graphics, in particular, color transformations in video via a curvature-flow method. Toward these applications, I will present an efficient approximation of the Wasserstein barycenter of probability measures using one-dimensional projections.