Systematic evaluation of super-resolution using classification

Vinay P. Namboodiri, Vincent De Smet, Luc Van Gool

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

6 Citations (SciVal)

Abstract

Currently two evaluation methods of super-resolution (SR) techniques prevail: The objective Peak Signal to Noise Ratio (PSNR) and a qualitative measure based on manual visual inspection. Both of these methods are sub-optimal: The latter does not scale well to large numbers of images, while the former does not necessarily reflect the perceived visual quality. We address these issues in this paper and propose an evaluation method based on image classification. We show that perceptual image quality measures like structural similarity are not suitable for evaluation of SR methods. On the other hand a systematic evaluation using large datasets of thousands of real-world images provides a consistent comparison of SR algorithms that corresponds to perceived visual quality. We verify the success of our approach by presenting an evaluation of three recent super-resolution algorithms on standard image classification datasets.

Original languageEnglish
Title of host publication2011 IEEE Visual Communications and Image Processing, VCIP 2011
PublisherIEEE
ISBN (Print)9781457713200
DOIs
Publication statusPublished - 29 Dec 2011
Event2011 IEEE Visual Communications and Image Processing, VCIP 2011 - Tainan, Taiwan
Duration: 6 Nov 20119 Nov 2011

Publication series

Name2011 IEEE Visual Communications and Image Processing, VCIP 2011

Conference

Conference2011 IEEE Visual Communications and Image Processing, VCIP 2011
Country/TerritoryTaiwan
CityTainan
Period6/11/119/11/11

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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