A survey of display device properties and visual acuity for visualization

The advent of computers with high processing power has led to the generation of huge datasets containing large numbers of elements, where each element is often characterized by multiple attributes. This has led to a critical need for ways to explore and analyze large, multidimensional information spaces. Visualization lends itself well to this challenge by enabling users to visually explore, analyze, and discover patterns within their data. Most visualization techniques are based on the assumption that the display device has sufficient resolution, and that our visual acuity is adequate to complete the analysis tasks. This may not be true however, particularly for specialized display devices (e.g., PDAs, or large-format projection walls). This paper discusses which properties of a display device need to be considered when visualizing large, multidimensional datasets. We also investigate the strengths and limitations of our visual system, in particular to understand how basic visual properties like color, texture, and motion are distinguished. These findings will form the basis for new research on how to best match a visualization design to a display’s physical characteristics and a viewer’s visual abilities.

[1]  Christopher G. Healey,et al.  A Perceptual Colour Segmentation Algorithm , 1996 .

[2]  A. Chapanis,et al.  Readability of Dials at Different Distances with Constant Visual Angle1 , 1967, Human factors.

[3]  J. J. Koenderink,et al.  Spatial properties of the visual detectability of moving spatial white noise , 2004, Experimental Brain Research.

[4]  Bernice E. Rogowitz,et al.  How not to lie with visualization , 1996 .

[5]  Mary J. Bravo,et al.  A global process in motion segregation , 1998, Vision Research.

[6]  Susan L. Franzel,et al.  Binocularity and visual search , 1988, Perception & psychophysics.

[7]  D R Proffitt,et al.  Updating displays after imagined object and viewer rotations. , 2000, Journal of experimental psychology. Learning, memory, and cognition.

[8]  Haim Levkowitz,et al.  Color scales for image data , 1992, IEEE Computer Graphics and Applications.

[9]  Terri Simmons.  What's the Optimum Computer Display Size? , 2001 .

[10]  Ivan Poupyrev,et al.  3D User Interfaces: Theory and Practice , 2004 .

[11]  Anne Treisman,et al.  Preattentive processing in vision , 1985, Computer Vision Graphics and Image Processing.

[12]  D R Proffitt,et al.  Comparing viewer and array mental rotations in different planes , 2001, Memory & cognition.

[13]  Deborah J. Aks,et al.  Visual search for size is influenced by a background texture gradient. , 1996, Journal of experimental psychology. Human perception and performance.

[14]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[15]  C Ware,et al.  Using Color Dimensions to Display Data Dimensions , 1988, Human factors.

[16]  李幼升,et al.  Ph , 1989 .

[17]  S. Mateeff,et al.  Temporal thresholds and reaction time to changes in velocity of visual motion , 1995, Vision Research.

[18]  Christopher G. Healey,et al.  Choosing effective colours for data visualization , 1996, Proceedings of Seventh Annual IEEE Visualization '96.

[19]  James T. Enns,et al.  Effective visualization of large multidimensional datasets , 1996 .

[20]  A. Ravishankar Rao,et al.  Identifying High Level Features of Texture Perception , 1993, CVGIP Graph. Model. Image Process..

[21]  Amit Prakash Sawant.  Dynamic Visualization of the Relationship Between Multiple Representations of an Abstract Information Space , 2003 .

[22]  D. G. Green,et al.  Monocular versus Binocular Visual Acuity , 1965, Nature.

[23]  Andrew S. Glassner,et al.  Principles of Digital Image Synthesis , 1995 .

[24]  A. Ravishankar Rao,et al.  Towards a texture naming system: Identifying relevant dimensions of texture , 1993, Vision Research.

[25]  Daniel Eric Huber.  Simple Motion in Glyph-Based Visualization , 2004 .

[26]  Christopher G. Healey,et al.  Perceptual Colors and Textures for Scientific Visualization , 1998 .

[27]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[28]  Christopher G. Healey,et al.  Formalizing Artistic Techniques and Scientific Visualization for Painted Renditions of Complex Information Spaces , 2001, IJCAI.

[29]  Sarat Mohan Kocherlakota.  Perception Driven Search Strategies For Effective Multi-Dimensional Visualization , 2003 .

[30]  Desney S. Tan,et al.  Women take a wider view , 2002, CHI.

[31]  T. Callaghan.  Dimensional interaction of hue and brightness in preattentive field segregation , 1984, Perception & psychophysics.

[32]  T. Callaghan.  Interference and dominance in texture segregation: Hue, geometric form, and line orientation , 1989, Perception & psychophysics.

[33]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[34]  D. A. Duce,et al.  Visualization in Scientific Computing , 1994, Focus on Computer Graphics.

[35]  Desney S. Tan,et al.  With similar visual angles, larger displays improve spatial performance , 2003, CHI '03.

[36]  A. Treisman,et al.  Illusory words: the roles of attention and of top-down constraints in conjoining letters to form words. , 1986, Journal of experimental psychology. Human perception and performance.

[37]  J. J. Koenderink,et al.  Temporal properties of the visual detectability of moving spatial white noise , 2004, Experimental Brain Research.

[38]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[39]  Z. Pylyshyn,et al.  Multiple parallel access in visual attention. , 1994, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.