Neural network image compression using Gabor primitives
Mary P. Anderson, David G. Brown, Alexander C. Schneider · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
A back propagation neural network was used to compress simulated nuclear medicine liver images with and without simulated lesions. The network operated on the Gabor representation of the image, in order to take advantage of the apparent similarity between that representation and the natural image processing of the human visual system. The quality of the compression scheme was assessed objectively by comparing the original images to the compressed/reconstructed images through calculation of an index shown to track with human observers for this class of image, the Hotelling trace. Task performance was measured pre- and post-compression for the task of classifying normal versus abnormal livers. Compression of even 2:1 was found to result in significant performance degradation in comparison with other means of compression, but produced a visually pleasing image.