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