Using neural nets to generate and improve computer graphic procedures
Image compression using neural networks in the past has focused on just reducing the number of bytes that had to be stored even thought the bytes had no meaning. This study looks at a new process that reduces the number of bytes stored but also maintains meaning behind the bytes. The bytes of the compressed image will correspond to parameters of an existing graphic algorithm. After a brief review of common neural networks and graphic algorithms, the back propagation neural network was chosen to be tested for this new process. Both three layer and four layer networks were tested. The four layer network was used in further tests because of its improved response compared to the three layer network. Two different training sets were used, a normal training set which was small and an extended version which included extreme value sets. These two training sets were shown to the neural network in two forms. The first was the raw format with no preprocessing. The second form used a Fast Fourier Transform to preprocess the data in an effort to distribute the image data throughout the image plane. The neural network’s response was good on images that it was trained on but responded poorly to new images that were not used in the training sets.