Image compression using linear and nonlinear principal component neural networks

Principal component analysis (PCA) is one of the famous statistical methods which eliminates the correlation between different data components and consequently decrease the size of data. In classical method covariance matrix of input data is used for extracting singular values and vectors. In this paper neural networks are used for extracting principal value components in order to compress image data. First, different principal component analysis neural networks are discussed. Then a nonlinear PCA neural network is used which ends up to better results as shown in simulation results.