Fast Backpropagation Neural Network algorithm for reducing convergence time of BPNN image compression

Abstract

Artificial neural networks (ANNs) especially Backpropagation Neural Network (BPNN) was used largely in image processing. The backpropagation neural network algorithm (BP) was used for training the BPNN for image compression/decompression. The BP requires long time to train the BPNN with small error. Therefore, in this research, a three layered BPNN was designed for building image compression system. The Fast backpropagation neural network algorithm (FBP) was used for training the designed BPNN to reduce the training time (convergence time) of BPNN as possible as. Many techniques were used to improve the use of FBP for BPNN training. This is done by using different architecture of BPNN by changing the number of input layer neurons and number of hidden layer neurons. Also we trained the BPNN with different FBP parameters. Finally, FBP results such as compression ratio (CR) and peak signal to noise ratio (PSNR) are computed and compared with BP results. From the results, we noticed that the use of FBP improve the BPNN training by reducing the convergence time of image compression learning process.