STILL IMAGE COMPRESSION USING NEURAL NETWORKS

Neural networks can be trained to represent certain sets of data. After decomposing an image using the discrete wavelet transform (DWT), a neural network may be able to represent the DWT coefficients in less space than the coefficients themselves. After splitting the image and the decomposition using several methods, neural networks were trained to represent the image blocks. By saving the weights and bias of each neuron, an image segment can be approximately recreated. Compression can be achieved using neural networks. Current results have been promising except for the amount of time needed to train a neural network. One method of speeding up code execution is discussed. However, plenty of future research work is available in this area.