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Optimal colors can predict luminosity thresholds in natural scenes.

Killian Duay1, Takehiro Nagai1

  • 1Department of Information and Communications Engineering, School of Engineering, Institute of Science Tokyo, Yokohama, Kanagawa, Japan.

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Summary

Human vision uses an internal "physical gamut" to judge when surfaces appear self-luminous. This study confirms optimal colors accurately predict these luminosity thresholds even in natural settings.

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Area of Science:

  • Visual Perception
  • Color Science
  • Computational Imaging

Background:

  • Luminosity thresholds mark the boundary between illuminated and self-luminous perception.
  • The human visual system infers these thresholds using an internal "physical gamut" of realizable surface colors.
  • Optimal colors model physical gamuts, previously predicting thresholds in abstract stimuli.

Purpose of the Study:

  • To validate the physical gamut theory and optimal color predictions in naturalistic viewing conditions.
  • To investigate the relationship between luminosity thresholds, naturalness, and the physical gamut.
  • To explore implications for augmented/virtual reality (AR/MR) and image perception.

Main Methods:

  • Utilized optimal colors to model and compute physical gamuts under varying illumination.
  • Presented participants with naturalistic visual stimuli to assess luminosity thresholds.
  • Compared predicted thresholds using optimal colors with empirically observed thresholds.

Main Results:

  • Optimal colors accurately predicted luminosity thresholds in naturalistic viewing conditions, confirming prior findings.
  • Luminosity thresholds appear to integrate concepts of self-luminosity and naturalness.
  • The physical gamut concept may extend to encompass both naturalness and all non-emissive colors in a scene.

Conclusions:

  • The physical gamut, visualized by optimal colors, serves as a valid internal reference for luminosity perception in natural settings.
  • Luminosity thresholds and physical gamuts may encompass notions of naturalness, impacting realistic rendering in AR/MR.
  • Findings enhance understanding of natural image perception and provide a framework for virtual object integration.