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Degradation Based Blind Image Quality Evaluation

  • Ville Ojansivu
  • Leena Lepistö
  • Martti Ilmoniemi
  • Janne Heikkilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

In this paper, we propose a novel framework for blind image quality evaluation. Unlike the common image quality measures evaluating compression or transmission artifacts this approach analyzes the image properties common to non-ideal image acquisition such as blur, under or over exposure, saturation, and lack of meaningful information. In contrast to methods used for adjusting imaging parameters such as focus and gain this approach does not require any reference image. The proposed method uses seven image degradation features that are extracted and fed to a classifier that decides whether the image has good or bad quality. Most of the features are based on simple image statistics, but we also propose a new feature that proved to be reliable in scene invariant detection of strong blur. For the overall two-class image quality grading, we achieved ≈ 90 % accuracy by using the selected features and the classifier. The method was designed to be computationally efficient in order to enable real-time performance in embedded devices.

Keywords

image artifacts blur exposure no-reference quality measurement 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ville Ojansivu
    • 1
  • Leena Lepistö
    • 2
  • Martti Ilmoniemi
    • 2
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision GroupUniversity of OuluFinland
  2. 2.Nokia CorporationTampereFinland

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