A Novel Approach to Flexible Multi-Resolution Image Compression using Deep Learning Based Autoencoders on Overlapping Image Patch

Abstract

Compressing images using a deep-learning-based autoencoder usually can only work for images of a fixed size. This causes problems since the images might be in various sizes. This paper proposes a novel approach that can be applied in real-life based on a patching algorithm for a given image. This algorithm aims to crop the image into smaller ones that can fit into our autoencoder model so that a single autoencoder model can be used for images with diverse sizes. The proposed approach was tested on a few autoencoder types (Vanilla, Deep Convolutional, and Variational Autoencoder), and it was found that different types of autoencoder impact the quality of the recreated images based on PSNR (Peak signal-to-noise ratio), MSE (Mean squared error), and SSIM (Structural Similarities). In our research we found that for PSNR and MSE, Variational Autoencoder got highest score among other design, with average of 21 PSNR and 1300 MSE Score and Deep Convolutional Autoencoder gives the best results in SSIM score among all other types of autoencoders with SSIM Score rate average at 0.5, when implemented in our system.