English

Abstract

In this paper, new multilayer perceptron's feed forward back propagation Neural Network (NN) performance using BFGS quasi newton , Levenberg -Marquardt (LM), Gradient descent back propagation with adaptive learning rate(GDA) Algorithms are being anticipated with the project detached to develop a lossless image solidity technique using NN and to design and contrivance image compression using Neural network to achieve maximum peak signal to noise ratio (PSNR), and low mean square error (MSE) and compression levels. This paper presents a NN based procedure that may be practical to data compression and breaks down large images into smaller blocks (1x64) and eradicates redundant information. Lastly, this technique uses a NN training functions like and conversion of block codes to vector codes and vice versa. Results obtained with proposed techniques leads to better compression material. Finally, this technique uses a NN training functions like and adaptation of block codes to vector codes and vice versa. Consequencesacquired with proposed techniques leads to better compression ratio at the same time conserving the image quality. The investigational result shows that the BFG quasi newton algorithm is best among the three proposed algorithm which offers better PSNR value and also reduces the MSE value.