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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.
Summary
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.
- 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.
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