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.本数据集包含用于有损图像压缩的经典主成分分析（PCA）与基于神经网络的PCA方法对应的Matlab脚本、函数及图像文件。 脚本（需依赖Matlab深度学习工具箱及下述辅助函数）： - CPCA_compression.m：采用经典PCA方法的图像压缩/解压缩程序 - APCA_compression.m：采用神经线性自编码器的图像压缩/解压缩程序 - GPCA_compression.m：采用广义赫布算法（GHA）神经网络的图像压缩/解压缩程序 辅助函数： - prep_diff_image.m：基于原始图像矩阵生成向量化差分图像帧矩阵 - prep_decompr_image.m：基于重构（解压缩）后的向量化差分图像帧矩阵生成重构（解压缩）图像矩阵 - calc_error_coeffs.m：计算图像压缩误差的各项指标 - gha.m：用于训练GHA神经网络的Matlab函数，该函数由David Gleich共享于https://www.cs.purdue.edu/homes/dgleich/projects/pca_neural_nets_website/neural-pca-ica.zip 图像文件： - baboon.tif - barbara.tif - lighthouse.tif