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):
- CPCA_compression.m: image compression/decompression procedure using the classical PCA approach,
- APCA_compression.m: image compression/decompression procedure using the neural linear autoencoder,
- GPCA_compression.m: image compression/decompression procedure using the GHA neural network.

Functions:
- prep_diff_image.m: preparing the vectorized 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) vectorized difference 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,
- lighthouse.tif.