Classical and neural PCA for image compression - Matlab files

Matlab scripts, functions and image files for the classical and neural network-based PCA aproaches for lossy image compression. Scripts (require Matlab Deep Learning Toolbox and the helper functions given below): - pca_classical.m: image compression/decompression procedure using the classical PCA approach, - pca_neural_autoencoder.m: image compression/decompression procedure using the neural linear autoencoder, - pca_neural_gha.m: image compression/decompression procedure using the GHA neural network. Functions: - prep_diff_image.m: preparing the difference image frame matrix based on the original image matrix, - prep_decompr_image.m: preparing the reconstructed (decompressed) image matrix based on the reconstructed (decompressed) image frame matrix, - calc_error_coeffs.m: calculating different indicators of the image compression error, - gha.m: training the GHA neural network (Matlab function shared by David Gleich in https://www.cs.purdue.edu/homes/dgleich/projects/pca_neural_nets_website/neural-pca-ica.zip). Images: - baboon.tif, - barbara.tif, - lena.tif, - lighthouse.tif.