Individual Differences in Image-Quality Estimations

Subjective image-quality estimation with high-quality images is often a preference-estimation task. Preferences are subjective, and individual differences exist. Individual differences are also seen in the eye movements of people. A task's subjectivity can result from people using different rules as a basis for their estimation. Using two studies, we investigated whether different preference-estimation rules are related to individual differences in viewing behaviour by examining the process of preference estimation of high-quality images. The estimation rules were measured from free subjective reports on important quality-related attributes (Study 1) and from estimations of the attributes’ importance in preference estimation (Study 2). The free reports showed that the observers used both feature-based image-quality attributes (e.g., sharpness, illumination) and abstract attributes, which include an interpretation of the image features (e.g., atmosphere and naturalness). In addition, the observers were classified into three viewing-strategy groups differing in fixation durations in both studies. These groups also used different estimation rules. In both studies, the group with medium-length fixations differed in their estimation rules from the other groups. In Study 1, the observers in this group used more abstract attributes than those in the other groups; in Study 2, they considered atmosphere to be a more important image feature. The study shows that individual differences in a quality-estimation task are related to both estimation rules and viewing strategies, and that the difference is related to the level of abstraction of the estimations.

[1]  Carrick C. Williams,et al.  Eye movements during information processing tasks: Individual differences and cultural effects , 2007, Vision Research.

[2]  C. Koch,et al.  Task-demands can immediately reverse the effects of sensory-driven saliency in complex visual stimuli. , 2008, Journal of vision.

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

[4]  K. Rayner The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search , 2009, Quarterly journal of experimental psychology.

[5]  D. Coppola,et al.  Idiosyncratic characteristics of saccadic eye movements when viewing different visual environments , 1999, Vision Research.

[6]  Wen-Chung Kao,et al.  Design considerations of color image processing pipeline for digital cameras , 2006, IEEE Transactions on Consumer Electronics.

[7]  Lina J. Karam,et al.  A No-Reference Image Blur Metric Based on the Cumulative Probability of Blur Detection (CPBD) , 2011, IEEE Transactions on Image Processing.

[8]  Stefan Winkler,et al.  Perceptual blur and ringing metrics: application to JPEG2000 , 2004, Signal Process. Image Commun..

[9]  K. Stanovich,et al.  Heuristics and Biases: Individual Differences in Reasoning: Implications for the Rationality Debate? , 2002 .

[10]  Jianping Zhou,et al.  Image Pipeline Tuning for Digital Cameras , 2007, 2007 IEEE International Symposium on Consumer Electronics.

[11]  Caleb Warren,et al.  Values and Preferences: Defining Preference Construction , 2011, Wiley interdisciplinary reviews. Cognitive science.

[12]  Raimondo Schettini,et al.  Color correction pipeline optimization for digital cameras , 2013, J. Electronic Imaging.

[13]  Alan C. Bovik,et al.  Objective quality assessment of multiply distorted images , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[14]  John M Henderson,et al.  Stable individual differences across images in human saccadic eye movements. , 2008, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[15]  Antje Nuthmann,et al.  Eye movement control during scene viewing: immediate effects of scene luminance on fixation durations. , 2013, Journal of experimental psychology. Human perception and performance.

[16]  Fjj Frans Blommaert,et al.  A computational approach to image quality , 2000 .

[17]  Brian Keelan,et al.  Handbook of Image Quality: Characterization and Prediction , 2002 .

[18]  Garrett M. Johnson,et al.  Color Imaging: Fundamentals and Applications , 2008 .

[19]  P. Sopp Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[20]  Lina J. Karam,et al.  A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) , 2009, IEEE Transactions on Image Processing.

[21]  M. F. Luce,et al.  Constructive Consumer Choice Processes , 1998 .

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

[23]  G. Loftus Picture perception: effects of luminance on available information and information-extraction rate. , 1985, Journal of experimental psychology. General.

[24]  A. L. I︠A︡rbus Eye Movements and Vision , 1967 .

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

[26]  G. Rhodes,et al.  Are you always on my mind? A review of how face perception and attention interact , 2007, Neuropsychologia.

[27]  Ingrid Heynderickx,et al.  Visual Attention in Objective Image Quality Assessment: Based on Eye-Tracking Data , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  James J. Clark,et al.  An inverse Yarbus process: Predicting observers’ task from eye movement patterns , 2014, Vision Research.

[29]  E.C.L. Vu,et al.  Visual Fixation Patterns when Judging Image Quality: Effects of Distortion Type, Amount, and Subject Experience , 2008, 2008 IEEE Southwest Symposium on Image Analysis and Interpretation.

[30]  Jukka Häkkinen,et al.  Why is quality estimation judgment fast? Comparison of gaze control strategies in quality and difference estimation tasks , 2014, J. Electronic Imaging.

[31]  L. Itti,et al.  Defending Yarbus: eye movements reveal observers' task. , 2014, Journal of vision.

[32]  Jukka Häkkinen,et al.  Content and quality: Interpretation-based estimation of image quality , 2008, TAP.

[33]  Benjamin W Tatler,et al.  The central fixation bias in scene viewing: selecting an optimal viewing position independently of motor biases and image feature distributions. , 2007, Journal of vision.

[34]  Michael D. Dodd,et al.  Examining the influence of task set on eye movements and fixations. , 2011, Journal of vision.

[35]  M. Castelhano,et al.  The relative contribution of scene context and target features to visual search in scenes , 2010, Attention, perception & psychophysics.

[36]  J. Hanley,et al.  Statistical analysis of correlated data using generalized estimating equations: an orientation. , 2003, American journal of epidemiology.

[37]  A. Kramer,et al.  Stable individual differences in search strategy? The effect of task demands and motivational factors on scanning strategy in visual search. , 2009, Journal of vision.

[38]  Brian Everitt,et al.  Cluster analysis , 1974 .

[39]  A. Kingstone,et al.  Saliency does not account for fixations to eyes within social scenes , 2009, Vision Research.

[40]  Benjamin Rahm,et al.  Eye movements and visuospatial problem solving: identifying separable phases of complex cognition. , 2009, Psychophysiology.

[41]  Marianne A. DeAngelus,et al.  Top-down control of eye movements: Yarbus revisited , 2009 .

[42]  George L. Malcolm,et al.  Searching in the dark: Cognitive relevance drives attention in real-world scenes , 2009, Psychonomic bulletin & review.

[43]  A. L. Yarbus,et al.  Eye Movements and Vision , 1967, Springer US.

[44]  Michelle R. Greene,et al.  Reconsidering Yarbus: A failure to predict observers’ task from eye movement patterns , 2012, Vision Research.

[45]  Eric C. Larson,et al.  Can visual fixation patterns improve image fidelity assessment? , 2008, 2008 15th IEEE International Conference on Image Processing.

[46]  Jukka Häkkinen,et al.  Concurrent explanations can enhance visual decision making. , 2014, Acta psychologica.

[47]  W.E. Snyder,et al.  Color image processing pipeline , 2005, IEEE Signal Processing Magazine.

[48]  John W. Payne,et al.  Measuring Constructed Preferences: Towards a Building Code , 1999 .

[49]  Thomas Martinetz,et al.  Variability of eye movements when viewing dynamic natural scenes. , 2010, Journal of vision.

[50]  G. Gigerenzer,et al.  Intuitive and Deliberate Judgments Are Based on Common Principles This Article Has Been Corrected. See Last Page , 2022 .