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An image-computable psychophysical spatial vision model.

Heiko H Schütt1,2, Felix A Wichmann1,3,4

  • 1Neural Information Processing Group, University of Tübingen, Tübingen, Germany.

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|October 21, 2017
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Summary
This summary is machine-generated.

This study presents an image-computable model of early spatial vision, successfully explaining human contrast detection and discrimination across various datasets. The model reveals insights into efficient coding and visual processing, with applications in image quality assessment.

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Area of Science:

  • Visual neuroscience
  • Computational vision
  • Psychophysics

Background:

  • Classical visual psychophysics aimed to understand initial pattern encoding in the human visual system.
  • A standard model of early spatial vision involves frequency/orientation-specific channels, nonlinearities, and contrast gain-control.
  • Previous models lacked image-computability, limiting their application to arbitrary visual inputs.

Purpose of the Study:

  • To implement an image-computable model of early spatial vision based on established psychophysical principles.
  • To validate the model against diverse psychophysical datasets, including contrast detection, discrimination, and natural image masking.
  • To explore implications for efficient coding theories and the spatial/frequency characteristics of visual processing.

Main Methods:

  • Developed an image-computable model incorporating spatial frequency and orientation-specific channels, accelerating nonlinearity, and divisive normalization (contrast gain-control).
  • Tested the model against classical psychophysical data (contrast detection, discrimination, oblique masking) and recent natural image datasets.
  • Analyzed model performance across different presentation durations and investigated the spatial characteristics of normalization.

Main Results:

  • The image-computable model accurately explains classical psychophysical data with a single parameter set.
  • The model shows reasonable performance on natural image masking data, though parameter adjustments are needed for different presentation durations.
  • Fitted parameters demonstrate sparse encoding of luminance information, aligning with efficient coding principles.
  • Nonlinear processing requires denser spatial frequency/orientation sampling than optimal coding predicts, and normalization is spatially local.

Conclusions:

  • The image-computable model provides a robust framework for understanding early spatial vision and its relation to efficient coding.
  • The model's success across varied datasets validates the core components of the standard early spatial vision model.
  • Insights into sampling density and normalization locality offer new directions for computational vision research.
  • The model serves as a valuable tool for quantitative analysis, stimulus optimization, and potential image quality assessment applications.