Ordinal regression based subpixel shift estimation for video super-resolution

Mithun Das Gupta, Shyamsundar Rajaram, Thomas S. Huang, Nemanja Petrovic

Research output: Contribution to journalArticlepeer-review


We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video super-resolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework. The ranking formulation is a variant of classification and regression formulation, in which the ordering present in class labels namely, the shift between patches is explicitly taken into account. Finally,we demonstrate the applicability of our approach on superresolving synthetically generated images with global subpixel shifts and enhancing real video frames by accounting for both local integer and subpixel shifts.

Original languageEnglish (US)
Article number85963
JournalEurasip Journal on Advances in Signal Processing
StatePublished - 2007

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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