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Efficiency of a model human image code.

A B Watson1

  • 1Perception and Cognition Group, NASA Ames Research Center, Moffett Field, California 94035.

Journal of the Optical Society of America. A, Optics and Image Science
|December 1, 1987
PubMed
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This study introduces an efficient image coding scheme based on primate visual cortex simple cells. The proposed Cortex transform achieves near-optimal compression, reaching approximately 1 bit per pixel.

Area of Science:

  • Computational Neuroscience
  • Image Processing
  • Human Vision

Background:

  • Neural representations of visual information can be modeled as explicit image codes.
  • Evaluating these codes requires testing for sufficiency (retaining all perceived information) and necessity (retaining only perceived information) for efficiency.

Purpose of the Study:

  • To explore an image coding scheme inspired by the simple cells of the primate striate cortex.
  • To assess the efficiency and sufficiency of this novel coding method.

Main Methods:

  • Developed the Cortex transform to map digital images into bandpass spatial frequency and orientation subimages (layers).
  • Optimized sampling to minimize data while avoiding aliasing.
  • Quantized samples using human vision's contrast-masking properties.

Related Experiment Videos

  • Computed sample entropy for a lower bound on code size.
  • Reconstructed images from the code and used psychophysical methods for evaluation.

    • Main Results:

    • The Cortex transform generates bandpass layers sampled efficiently and quantized based on human visual perception.
    • Entropy computation provides a lower bound for the code size.
    • Psychophysical evaluation of reconstructed images assesses code sufficiency.
    • Achieved an image-code size of approximately 1 bit/pixel at the threshold for detecting artifacts.

    Conclusions:

    • The developed Cortex transform offers an efficient neural-inspired image coding scheme.
    • The code demonstrates high efficiency, approaching theoretical limits for visual information representation.
    • This model provides a framework for understanding neural coding and developing advanced image compression techniques.