Objective quality assessment for image super-resolution: A natural scene statistics approach

There has been an increasing number of image super-resolution (SR) algorithms proposed recently to create images with higher spatial resolution from low-resolution (LR) images. Nevertheless, how to evaluate the performance of such SR and interpolation algorithms remains an open problem. Subjective assessment methods are useful and reliable, but are expensive, time-consuming, and difficult to be embedded into the design and optimization procedures of SR and interpolation algorithms. Here we make one of the first attempts to develop an objective quality assessment method of a given resolution-enhanced image using the available LR image as a reference. Our algorithm follows the philosophy behind the natural scene statistics (NSS) approach. Specifically, we build statistical models of frequency energy falloff and spatial continuity based on high quality natural images and use the departures from such models to quantify image quality degradations. Subjective experiments have been carried out that verify the effectiveness of the proposed approach.