A Psychophysical Evaluation of Texture Degradation Descriptors

  • Jiří Filip
  • Pavel Vácha
  • Michal Haindl
  • Patrick R. Green
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

Delivering digitally a realistic appearance of materials is one of the most difficult tasks of computer vision. Accurate representation of surface texture can be obtained by means of view- and illumination-dependent textures. However, this kind of appearance representation produces massive datasets so their compression is inevitable. For optimal visual performance of compression methods, their parameters should be tuned to a specific material. We propose a set of statistical descriptors motivated by textural features, and psychophysically evaluate their performance on three subtle artificial degradations of textures appearance. We tested five types of descriptors on five different textures and combination of thirteen surface shapes and two illuminations. We found that descriptors based on a two-dimensional causal auto-regressive model, have the highest correlation with the psychophysical results, and so can be used for automatic detection of subtle changes in rendered textured surfaces in accordance with human vision.

Keywords

texture degradation statistical features BTF eye-tracking visual psychophysics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jiří Filip
    • 1
  • Pavel Vácha
    • 1
  • Michal Haindl
    • 1
  • Patrick R. Green
    • 2
  1. 1.Institute of Information Theory and Automation of the ASCRCzech Republic
  2. 2.School of Life SciencesHeriot-Watt UniversityEdinburghScotland

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