Image super-resolution (SR) creates an enhanced high-resoultion (HR) image using multiple low-resolution (LR) images of the same object. A typical image formation model introduces blurring, aliasing, and added noise. Super-resolution is designed to jointly reduce or remove all three. While the first SR algorithm appeared over 20 years ago, only recently have people begun exploring the performance of these algorithms. However, only objective MSE performance has been considered. In this talk, we examine subjective and objective quality assessment for super-resolution.