A study of norms in convex optimization super-resolution from compressed sources

Advancements over the last decade in video acquisition and display technologies lead to a continuous increase of video content resolution. These aspects combined with the shift towards cloud multimedia services and the underway adoption of High Efficiency Video Coding standards (HEVC) create a lot of interest for Super-Resolution (SR) and video enhancing techniques. Recent works showed that proximal based convex optimization approaches provide a promising direction in video restoration. An important aspect in the definition of a SR model is the metric used in defining the objective function. Most techniques are based on the classical l(2) norm. In this paper we further investigate the use of other norms and their behavior w.r.t. multiple quality evaluation metrics. We show that significant gains of up to 0.5 dB can be obtained when using different norms.