Image quality wheel

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We have collected a large dataset of subjective image quality "*nesses," such as sharpness or colorfulness. The dataset comes from seven studies and contains 39,415 quotations from 146 observers who have evaluated 62 scenes either in print images or on display. We analyzed the subjective evaluations and formed a hierarchical image quality attribute lexicon for *nesses, which is visualized as image quality wheel (IQ-Wheel). Similar wheel diagrams for attributes have become industry standards in other sensory experience fields such as flavor and fragrance sciences. The IQ-Wheel contains the frequency information of 68 attributes relating to image quality. Only 20% of the attributes were positive, which agrees with previous findings showing a preference for negative attributes in image quality evaluation. Our results also show that excluding physical attributes of paper gloss, observers then use similar terminology when evaluating images with printed images or images viewed on a display. IQ-Wheel can be used to guide the selection of scenes and distortions when designing subjective experimental setups and creating image databases. (C) 2019 SPIE and IS&T

Original languageEnglish
Article number013015
JournalJournal of Electronic Imaging
Volume28
Issue number1
Number of pages11
ISSN1017-9909
DOIs
Publication statusPublished - 30 Jan 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 515 Psychology
  • image quality
  • attributes
  • lexicon
  • subjective evaluation
  • diagram
  • *nesses
  • DIMENSIONS

Cite this

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title = "Image quality wheel",
abstract = "We have collected a large dataset of subjective image quality {"}*nesses,{"} such as sharpness or colorfulness. The dataset comes from seven studies and contains 39,415 quotations from 146 observers who have evaluated 62 scenes either in print images or on display. We analyzed the subjective evaluations and formed a hierarchical image quality attribute lexicon for *nesses, which is visualized as image quality wheel (IQ-Wheel). Similar wheel diagrams for attributes have become industry standards in other sensory experience fields such as flavor and fragrance sciences. The IQ-Wheel contains the frequency information of 68 attributes relating to image quality. Only 20{\%} of the attributes were positive, which agrees with previous findings showing a preference for negative attributes in image quality evaluation. Our results also show that excluding physical attributes of paper gloss, observers then use similar terminology when evaluating images with printed images or images viewed on a display. IQ-Wheel can be used to guide the selection of scenes and distortions when designing subjective experimental setups and creating image databases. (C) 2019 SPIE and IS&T",
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author = "Toni Virtanen and Mikko Nuutinen and Jukka H{\"a}kkinen",
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language = "English",
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}

Image quality wheel. / Virtanen, Toni; Nuutinen, Mikko; Häkkinen, Jukka .

In: Journal of Electronic Imaging, Vol. 28, No. 1, 013015, 30.01.2019.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Image quality wheel

AU - Virtanen, Toni

AU - Nuutinen, Mikko

AU - Häkkinen, Jukka

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N2 - We have collected a large dataset of subjective image quality "*nesses," such as sharpness or colorfulness. The dataset comes from seven studies and contains 39,415 quotations from 146 observers who have evaluated 62 scenes either in print images or on display. We analyzed the subjective evaluations and formed a hierarchical image quality attribute lexicon for *nesses, which is visualized as image quality wheel (IQ-Wheel). Similar wheel diagrams for attributes have become industry standards in other sensory experience fields such as flavor and fragrance sciences. The IQ-Wheel contains the frequency information of 68 attributes relating to image quality. Only 20% of the attributes were positive, which agrees with previous findings showing a preference for negative attributes in image quality evaluation. Our results also show that excluding physical attributes of paper gloss, observers then use similar terminology when evaluating images with printed images or images viewed on a display. IQ-Wheel can be used to guide the selection of scenes and distortions when designing subjective experimental setups and creating image databases. (C) 2019 SPIE and IS&T

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