Image compression and decompression using deep convolutional autoencoder

Abstract

In the last decades, image compression has become a very important task since the Images contribute to a significan tamoun to finterne ttraff ic,demand ingefficient solutions to reduce storage and transmission costs while maintaining image quality. This thesis explores the use of Convolutional Autoencoders for image compression and decompression. Convolutional Autoencoders are employed to learn compact, meaningful representations of input images, which are then used to reconstruct the original images with minimal loss of quality. The model was tested using the Peak Signal-to-Noise Ratio (PSNR) and achieved good results, demonstrating the effectivenes so fth ecompression. Along with decompressing images, a network was trained to classify them bypassing the need for image reconstruction. The trained model compresses, classifies, and saves images efficiently, thus showing that compressed images canstill beaccurately categorized.