Image Compression Using Artificial Neural Networks

This documentation presents the implementation of "Image Compression Using Artificial Neural Networks," a technically advanced approach to image processing. The MATLAB 6.5-based code is fully compatible with Windows platforms and contains a complete set of implementation files.

The core functionality revolves around two main scripts: Train.m implements the neural network training process using backpropagation algorithms to optimize compression weights, while Codec.m utilizes the trained network parameters to perform actual image compression and decompression operations. The system employs multilayer perceptron architecture where the hidden layer acts as a bottleneck for dimensionality reduction.

This implementation demonstrates a complete workflow for neural network-based image compression, highlighting key aspects such as platform compatibility, file organization, and the training-compression pipeline. Through this methodology, users can achieve efficient image compression with maintained visual quality using artificial neural networks.