Principal Component Approximation Network for Image Compression

Abstract

In this work, we propose a novel principal component approximation network (PCANet) for image compression. The proposed network is based on the assumption that a set of images can be decomposed into several shared feature matrices, and an image can be reconstructed by the weighted sum of these matrices. The proposed PCANet is specifically devised to learn and approximate these feature matrices and weight vectors, which are used to encode images for compression. Unlike previous deep learning-based methods, a distinctive aspect of our approach is its consideration of network size in the bit-rate computation. Despite this inclusion, our proposed method yields promising results. Through extensive experiments conducted on standard datasets, we demonstrate the effectiveness of our approach in comparison to state-of-the-art techniques. To the best of our knowledge, this is the first machine learning approach that includes the size of networks during bitrate computation in image compression.