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Nonlinear vector prediction using feed-forward neural networks.

S A Rizvi1, L C Wang, N M Nasrabadi

  • 1Coll. of Staten Island, City Univ. of New York, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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Nonlinear predictors using neural networks, like multilayer perceptrons, improve edge block prediction accuracy compared to linear methods. This enhances image compression by better exploiting higher-order correlations.

Area of Science:

  • Digital image processing
  • Machine learning for signal processing

Background:

  • Classical linear vector predictors are limited by their inability to capture complex data correlations.
  • Accurate prediction of edge blocks is crucial for efficient image compression.

Purpose of the Study:

  • To investigate neural network architectures for nonlinear vector prediction.
  • To compare the accuracy of nonlinear predictors against linear predictors for edge blocks.

Main Methods:

  • Implemented nonlinear vector predictors using multilayer perceptron (MLP), functional link (FL) network, and radial basis function (RBF) network architectures.
  • Evaluated predictor performance on edge-containing blocks.

Main Results:

  • Neural network-based nonlinear predictors demonstrated higher accuracy in predicting edge blocks.

Related Experiment Videos

  • Nonlinear predictors effectively exploit higher-order correlations missed by linear methods.

    • Conclusions:

    • Neural networks offer a superior approach for nonlinear vector prediction in image processing.
    • Nonlinear prediction significantly improves accuracy for challenging edge blocks compared to linear methods.