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

Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)