Comparison Of Image Compression Analysis Using Deep Autoencoder And Deep CNN Approach

Abstract

Image compression is a fundamental technique in digital image processing used to decrease the space used for storage of digital images and videos, which will help to increase the storage space and for efficient transmission. Nowadays many deep learning techniques have produced promising results on image compression field. However, traditional compression techniques have introduced many compression artifacts problem. To solve this problem we have compared two deep learning approaches for image compression. One method is based on Deep Autoencoder technique and other is based on deep convolutional neural network (deep CNN) approach. Autoencoder structure is a popular choice to do end-to-end compression and deep CNN is the most popular neural network model for the application of any basic deep learning technique. The performance of two methods are compared based on Peak signal-to-noise ratio (PSNR) and root mean square error (RMSE). Based on the performance evaluation methods result, it is evident that deep Autoencoder technique is more advantageous than deep CNN technique.