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Related Experiment Video

Updated: Jul 7, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

New studies on adaptive predictive coding of images using multiplicative autoregressive models.

M Das1, N K Loh

  • 1Dept. of Electr. and Syst. Eng., Oakland Univ., Rochester, MI.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
Summary

Two new multiplicative autoregressive (MAR) models enhance adaptive predictive coding for digital images. These models ensure easy implementation, high signal-to-noise ratios, and stable predictive coding performance.

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Area of Science:

  • Digital image processing
  • Signal processing
  • Machine learning

Background:

  • Adaptive predictive coding is crucial for efficient image compression.
  • Existing methods may face challenges with implementation complexity or coding stability.
  • Optimizing signal-to-noise ratio (SNR) at moderate bit rates remains an active research area.

Purpose of the Study:

  • Introduce novel one-dimensional multiplicative autoregressive (MAR) models.
  • Develop an adaptive predictive coding scheme for digitized images.
  • Address limitations in current image coding techniques.

Main Methods:

  • Development of two distinct one-dimensional multiplicative autoregressive (MAR) models.
  • Implementation of an adaptive predictive coding scheme based on the proposed MAR models.

Related Experiment Videos

Last Updated: Jul 7, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

  • Extensive experimental evaluation of the models' performance.

    • Main Results:

    • The proposed MAR models demonstrate easy implementability.
    • Achieved a high signal-to-noise ratio (SNR) at moderate bit rates.
    • Guaranteed stability of the predictive coder was confirmed through experiments.

    Conclusions:

    • The novel MAR models offer a promising approach for adaptive predictive image coding.
    • The scheme provides a favorable trade-off between compression efficiency and image quality.
    • The findings suggest potential for improved digital image compression applications.