Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures. Academic Article uri icon

Overview

abstract

  • Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a 'variance is salience' hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The 'variance is salience' hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

publication date

  • August 3, 2020

Research

keywords

  • Light
  • Pattern Recognition, Visual

Identity

PubMed Central ID

  • PMC7494356

Scopus Document Identifier

  • 85090093668

Digital Object Identifier (DOI)

  • 10.7554/eLife.54347

PubMed ID

  • 32744505

Additional Document Info

volume

  • 9