A comparison of image compression by neural networks and principal component analysis
Denis Anthony, E. Hines, J. Barham, D. Taylor · 1990
A set of images was compressed using artificial neural networks (ANNs) utilizing error back-propagation and by principal component analysis (PCA). The total sum square error (TSE) was compared for each method using the training data and a second set of test images. The difference between seen and unseen images with respect to TSSE is more pronounced in 32 by 16 pixel segments than in 8 by 8 segments in PCA compression. Further experimentation showed that the 8-by-8-pixel segments are optimal with regard to TSSE on unseen data. ANNs also seem to generalize less accurately on larger segments. Since the time taken for neural network compression learning is about an order of magnitude higher than PCA, and PCA is more repeatable in terms of the error magnitude and produces lower error for unseen segments, it would seem preferable to use PCA analysis rather than neural network methods to produce the reduced dimensional input to a diagnostic network