Subjective experience of image quality: attributes, definitions, and decision making of subjective image quality

Leisti, Tuomas; Radun, Jenni; Virtanen, Toni; Halonen, Raisa; Nyman, Göte

doi:10.1117/12.807142

Cited by 17 publications

(22 citation statements)

References 10 publications

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“…In this research, preference, x can be calculated by how far away from the average, μ with multiplying standard deviation, σ. If z is 3, x is away from μ As far as function (8). It can be calculated the probability about 0.13% of normalized distribution by assuming the provability is the general audience's preference.…”

Section: Statistical Analysis Resultsmentioning

confidence: 99%

Digital Image Quality Assessment Based on Standard Normal Deviation

Park¹,

Har²

2015

International Journal of Contents

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Section: Statistical Analysis Resultsmentioning

confidence: 99%

Digital Image Quality Assessment Based on Standard Normal Deviation

Park¹,

Har²

2015

International Journal of Contents

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show abstract

“…If image A is of higher quality than B, and image B is of higher quality than C, then image C is of higher quality than A. Cyclic relations can occur, especially when images are similar and contain multi-type distortions and manipulations [14]. It is not clear how the images with cyclic relations should be ranked or how the results should be presented.…”

Section: Cyclic Relationsmentioning

confidence: 90%

A new method for evaluating the subjective image quality of photographs: dynamic reference

Nuutinen

Virtanen

Leisti

et al. 2014

Multimed Tools Appl

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The Dynamic Reference (DR) method has been developed for subjective image quality experiments in which original or undistorted images are unavailable. The DR method creates reference image series from test images. Reference images are presented to observers as a slide show prior to evaluating their quality. As the observers view the set of reference images, they determine the overall variation in quality within the set of test images. This study compared the performance of the DR method to that of the standardized absolute category rating (ACR) and paired comparison (PC) methods. We measured the performance of each method in terms of time effort and discriminability. The results showed that the DR method is faster than the PC method and more accurate than the ACR method. The DR method is especially suitable for experiments that require highly accurate results in a short time.

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“…Keelan 5 approached the image quality attributes through measurability viewpoint and created hierarchical classification of image quality attributes by dividing them into personal, aesthetic, preferential, and artifactual attributes but also considering whether the attributes have objective tractability, first and third party rater correlation, and system dependence. Based on subjective interview experiments for printed photographs, Leisti et al 22 classified attributes to have two levels: low level and high level. The most important low-level attributes for printed images are brightness of color, sharpness, graininess, brightness, color quality, gloss, contrast, and lightness.…”

Section: Introductionmentioning

confidence: 99%

Image quality wheel

Virtanen

Nuutinen

2019

J. Electron. Imag.

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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.

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Subjective experience of image quality: attributes, definitions, and decision making of subjective image quality

Cited by 17 publications

References 10 publications

Digital Image Quality Assessment Based on Standard Normal Deviation

Digital Image Quality Assessment Based on Standard Normal Deviation

A new method for evaluating the subjective image quality of photographs: dynamic reference

Image quality wheel

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