Review of image compression technology: from traditional methods to deep learning

With the continuous development of imaging technology, image data has been growing exponentially and has been widely applied in fields such as digital media, medical imaging, and remote sensing. Given the massive volume of image data, achieving higher compression ratios and higher reconstruction quality has gradually become one of the hot research topics. By analyzing and organizing relevant literature from recent years, this paper summarizes and analyzes image compression methods, dividing them into traditional image compression methods and deep learning-based image compression methods. Representative examples of both approaches are listed, and the core ideas, advantages, and disadvantages of these methods are introduced. Finally, the future development trends of image compression are summarized and discussed.