  In recent decades, digital image processing has gained enormous popularity.
Consequently, a number of data compression strategies have been put forth, with
the goal of minimizing the amount of information required to represent images.
Among them, JPEG compression is one of the most popular methods that has been
widely applied in multimedia and digital applications. The periodic nature of
DFT makes it impossible to meet the periodic condition of an image's opposing
edges without producing severe artifacts, which lowers the image's perceptual
visual quality. On the other hand, deep learning has recently achieved
outstanding results for applications like speech recognition, image reduction,
and natural language processing. Convolutional Neural Networks (CNN) have
received more attention than most other types of deep neural networks. The use
of convolution in feature extraction results in a less redundant feature map
and a smaller dataset, both of which are crucial for image compression. In this
work, an effective image compression method is purposed using autoencoders. The
study's findings revealed a number of important trends that suggested better
reconstruction along with good compression can be achieved using autoencoders.
