Color image compression using PCA and backpropagation learning

摘要

The RGB components of a color image contain redundant information that can be reduced using a new hybrid neural-network model based upon Sanger's algorithm for representing an image in terms of principal components and a backpropagation algorithm for restoring the original representation. The PCA method produces a black and white image with the same number of pixels as the original color image, but with each pixel represented by a scalar value instead of a three-dimensional vector of RGB components. Experimental results show that as our hybrid learning method adapts to local (spatial) image characteristics it outperforms the YIQ and YUV standard compression methods. Our experiments also show that it is feasible to apply training results from one image to previously unseen images.