Image compression method using convolutional autoencoder with low computational resources

Abstract

In recent years, nano-satellites such as CubeSat have been developed by various organizations. Since nano-satellites have limited communication capacity and computational resources, image compression must be performed with reduced computational resources for transmission and reception of captured satellite images. In this study, the designs of computationally resource-efficient convolutional autoencoder that can be installed in a nano-satellite is a compared in terms of recovery accuracy by using different downsampling methods. Three models were evaluated by SSIM and PSNR for images after compression: a model with pooling layers, a model using convolutional layers with wide stride widths instead of pooling layers, and a model using only convolutional layers with wide stride widths. The results showed that using convolutional layers with wide stride widths instead of pooling layers improved the restoration accuracy and better preserved the edges of the image.