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Gloss discrimination: Toward an image-based perceptual model.

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This study establishes a framework for measuring perceived gloss by accounting for scene variables. Human judgments of gloss discrimination are consistent and predictable, enabling bounds on visual discriminability estimation.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Perception Science

Background:

  • Gloss perception is crucial for surface characterization but difficult to quantify due to scene influences.
  • Existing methods struggle to establish perceptual gloss standards because they don't fully account for viewing conditions.
  • Predicting gloss discrimination is essential for applications ranging from computer graphics to material science.

Purpose of the Study:

  • To develop an experimental framework for reliably measuring perceived gloss discrimination.
  • To investigate how scene variables (shape, illumination, viewpoint, roughness) affect gloss perception.
  • To create a basis for a perceptual standard of gloss that accounts for real-world viewing conditions.

Main Methods:

  • Rendered a diverse set of images varying shape, illumination, viewpoint, and surface roughness.
  • Created image pairs with fixed differences in surface roughness to represent high and low gloss.
  • Collected human judgments (N=150) using a paired comparison task to identify largest apparent gloss differences.

Main Results:

  • Human gloss discrimination rankings were consistent across participants, independent of physical reflectance.
  • Perceived gloss differences were accurately predicted by a Visual Differences Predictor model.
  • The framework allows for estimating visual discriminability bounds across various viewing conditions.

Conclusions:

  • A robust experimental framework for measuring perceived gloss discrimination has been established.
  • Scene variables significantly influence gloss perception, and this influence can be modeled.
  • The findings pave the way for developing perceptual gloss standards and improving visual realism in digital content.