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.

[1]  Jukka Häkkinen,et al.  VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations , 2016, Behavior research methods.

[2]  Peter Schelkens,et al.  Qualinet White Paper on Definitions of Quality of Experience , 2013 .

[3]  Göte Nyman,et al.  Measuring multivariate subjective image quality for still and video cameras and image processing system components , 2008, Electronic Imaging.

[4]  Jon Y. Hardeberg,et al.  Attributes of image quality for color prints , 2010, J. Electronic Imaging.

[5]  D. Valentin,et al.  Perceptual dimensions of tactile textures. , 2003, Acta psychologica.

[6]  Søren Bech,et al.  Enhancing colour image quality in television displays , 1999 .

[7]  Luke Chengwu Cui.  Do experts and naive observers judge printing quality differently? , 2003, IS&T/SPIE Electronic Imaging.

[8]  Lauri Carlson,et al.  FinnWordNet - WordNet på finska via översättning , 2010 .

[9]  Göte Nyman,et al.  Subjective experience of image quality: attributes, definitions, and decision making of subjective image quality , 2009, Electronic Imaging.

[10]  Walter J. Riker.  A Review of J , 2010 .

[11]  Kees Teunissen,et al.  Rapid perceptual image description (RaPID) method , 1996, Electronic Imaging.

[12]  C. E. Dalgliesh,et al.  BEER FLAVOUR TERMINOLOGY1 , 1979 .

[13]  Pauline Faye,et al.  Perceptive free sorting and verbalization tasks with naive subjects: an alternative to descriptive mappings , 2004 .

[14]  Peter G. Engeldrum,et al.  A Short Image Quality Model Taxonomy , 2004, Journal of Imaging Science and Technology.

[15]  Lydia J.R. Lawless,et al.  THE MCCORMICK SPICE WHEEL: A SYSTEMATIC AND VISUAL APPROACH TO SENSORY LEXICON DEVELOPMENT , 2012 .

[16]  Olli Rummukainen,et al.  Categorization of Natural Dynamic Audiovisual Scenes , 2014, PloS one.

[17]  Pirkko Oittinen,et al.  Process perspective on image quality evaluation , 2008, Electronic Imaging.

[18]  Judith Redi,et al.  Interactions of visual attention and quality perception , 2011, Electronic Imaging.

[19]  R. Gawel,et al.  A "Mouth-feel Wheel": terminology for communicating the mouth-feel characteristics of red wine , 2000 .

[20]  Jukka Häkkinen,et al.  Evaluating the multivariate visual quality performance of image-processing components , 2008, TAP.

[21]  Manuel Zar.  Understanding the underlying dimensions in perfumers' odor perception space as a basis for developing meaningful odor maps , 2009 .

[22]  Jari Takatalo,et al.  Evaluation of stereoscopic image quality for mobile devices using interpretation based quality methodology , 2009, Electronic Imaging.

[23]  Fjj Frans Blommaert,et al.  Image Quality Semantics , 1997, Journal of Imaging Science and Technology.

[24]  Bernard Chen,et al.  Wineinformatics: Applying Data Mining on Wine Sensory Reviews Processed by the Computational Wine Wheel , 2014, 2014 IEEE International Conference on Data Mining Workshop.

[25]  Joyce E. Farrell,et al.  Handbook of Image Quality: Characterization and Prediction , 2004 .

[26]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[27]  Göte Nyman,et al.  Audiovisual quality estimation of mobile phone video cameras with interpretation-based quality approach , 2007, Electronic Imaging.

[28]  Göte Nyman,et al.  Forming valid scales for subjective video quality measurement based on a hybrid qualitative/quantitative methodology , 2008, Electronic Imaging.

[29]  Jukka Häkkinen,et al.  Evaluation of the visual performance of image processing pipes: information value of subjective image attributes , 2010, Electronic Imaging.

[30]  Gail Vance Civille,et al.  Developing Lexicons: A Review , 2013 .