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A Complexity-Based Image Analysis to Investigate Interference Between Distortions and Image Contents in Image Quality Assessment

  • Gianluigi Ciocca
  • Silvia Corchs
  • Francesca Gasparini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10213)

Abstract

In this paper we investigate how distortion and image content interfere within image quality assessment. To this end we analyze how full reference metrics behave within three different groups of images. Given a dataset of images, these are first classified as high, medium or low complexity and the FR methods are applied within each group separately. We consider images from LIVE, CSIQ and LIVE multi-distorted databases. We evaluate 17 full reference quality metrics available in the literature on each of these the high, medium and low complexity groups. We observe that within these groups the metrics better correlate subjective data. In particular, the signal based metrics are the ones that show the highest improvements. Moreover for the LIVE multi-distorted database the gain in performance is evident for all the metrics considered.

Keywords

Image complexity Image Quality Assessment Full Reference metrics 

Supplementary material

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gianluigi Ciocca
    • 1
    • 2
  • Silvia Corchs
    • 1
    • 2
  • Francesca Gasparini
    • 1
    • 2
  1. 1.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversity of Milano-BicoccaMilanItaly
  2. 2.NeuroMi - Milan Center for NeuroscienceMilanItaly

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