Extending the Modelfest image/threshold database into the spatio-temporal domain

Academic Article


  • Models that predict human performance on narrow classes of visual stimuli abound in the vision science literature. However, the vision and the applied imaging communities need robust general-purpose, rather than narrow, computational human visual system (HVS) models to evaluate image fidelity and quality and ultimately improve imaging algorithms. Of the general-purpose early HVS models that currently exist, direct model comparisons on the same data sets are rarely made. The Modelfest group was formed several years ago to solve these and other vision modeling issues. The group has developed a database of static spatial test images with threshold data that is posted on the WEB for modellers to use in HVS model design and testing. The first phase of data collection was limited to detection thresholds for static gray scale 2D images. The current effort will extend the database to include thresholds for selected grayscale 2D spatio-temporal image sequences. In future years, the database will be extended to include discrimination (masking) for dynamic, color and gray scale image sequences. The purpose of this presentation is to invite the Electronic Imaging community to participate in this effort and to inform them of the developing data set, which is available to all interested researchers. This paper presents the display specifications, psychophysical methods and stimulus definitions for the second phase of the project, spatio-temporal detection. The threshold data will be collected by each of the authors over the next year and presented on the WEB along with the stimuli. For the final stimulus specifications, the latest results and other Modelfest group activities visit: http://neurometrics.com/projects/or http://vision.arc.nasa.gov/modelfest/. © 2002 SPIE - The International Society for Optical Engineering.
  • Authors

    Published In

    Digital Object Identifier (doi)

    Author List

  • Carney T; Klein SA; Beutter B; Norcia A; Chen CC; Tyler CW; Makous W; Watson A; Cropper SJ; Popple AV
  • Start Page

  • 138
  • End Page

  • 148
  • Volume

  • 4662
  • Issue

  • 1