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

Andrei Purica (1) , Benoit Boyadjis (2, 1) , Béatrice Pesquet-Popescu (1) , Frederic Dufaux (3)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 THALES COMMUNICATIONS & SECURITY
3 L2S - Laboratoire des signaux et systèmes

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 I2 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.

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Andrei Purica, Benoit Boyadjis, Béatrice Pesquet-Popescu, Frederic Dufaux. A study of norms in convex optimization super-resolution from compressed sources. 19th International Workshop on Multimedia Signal Processing (MMSP), Oct 2017, London-Luton, United Kingdom. ⟨10.1109/mmsp.2017.8122216⟩. ⟨hal-01567378⟩