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.

翻译：近几十年来，数字图像处理获得了极大的普及。因此，人们提出了多种数据压缩策略，旨在最小化表示图像所需的信息量。其中，JPEG压缩是多媒体和数字应用中应用最广泛的方法之一。DFT的周期性使其无法满足图像相对边缘的周期性条件，从而产生严重伪影，降低图像的感知视觉质量。另一方面，深度学习近年来在语音识别、图像压缩和自然语言处理等应用中取得了卓越成果。卷积神经网络（CNN）比大多数其他类型的深度神经网络受到了更多关注。在特征提取中使用卷积可生成冗余度更低的特征图及更小的数据集，这两者对图像压缩均至关重要。本文提出了一种基于自编码器的高效图像压缩方法。研究结果表明，自编码器能够在实现良好压缩的同时，获得更优的图像重建效果。