Image quality wheel

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

Kuvaus

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

Alkuperäiskielienglanti
Artikkeli013015
LehtiJournal of Electronic Imaging
Vuosikerta28
Numero1
Sivumäärä11
ISSN1017-9909
DOI - pysyväislinkit
TilaJulkaistu - 30 tammikuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä, vertaisarvioitu

Tieteenalat

  • 515 Psykologia

Lainaa tätä

Virtanen, Toni ; Nuutinen, Mikko ; Häkkinen, Jukka . / Image quality wheel. Julkaisussa: Journal of Electronic Imaging. 2019 ; Vuosikerta 28, Nro 1.
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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",
keywords = "515 Psychology, image quality, attributes, lexicon, subjective evaluation, diagram, *nesses, DIMENSIONS",
author = "Toni Virtanen and Mikko Nuutinen and Jukka H{\"a}kkinen",
year = "2019",
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day = "30",
doi = "10.1117/1.JEI.28.1.013015",
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journal = "Journal of Electronic Imaging",
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publisher = "SPIE, The International Society for Optical Engineering",
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Image quality wheel. / Virtanen, Toni; Nuutinen, Mikko; Häkkinen, Jukka .

julkaisussa: Journal of Electronic Imaging, Vuosikerta 28, Nro 1, 013015, 30.01.2019.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinenvertaisarvioitu

TY - JOUR

T1 - Image quality wheel

AU - Virtanen, Toni

AU - Nuutinen, Mikko

AU - Häkkinen, Jukka

PY - 2019/1/30

Y1 - 2019/1/30

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

AB - 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

KW - 515 Psychology

KW - image quality

KW - attributes

KW - lexicon

KW - subjective evaluation

KW - diagram

KW - nesses

KW - DIMENSIONS

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DO - 10.1117/1.JEI.28.1.013015

M3 - Article

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JO - Journal of Electronic Imaging

JF - Journal of Electronic Imaging

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