Abstract
Abstract It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate, the decompressed image gets really fuzzy or indistinguishable. To overcome this problem, artificial neural networks ANNs techniques are used. In this paper, we propose a bipolar sigmoidal backpropagation BBP algorithm to train a feedforward autoassociative neural network. The proposed method includes steps to break down large images into smaller windows for image compression/ decompression processes. A number of experiments have been achieved, the results obtained, such as compression ratio and peak signal to noise ratio PSNR are compared with the performance of linear backpropagation LBP and standard (sigmoidal) backpropagation SBP schemes
