Image is one of the important ways to obtain information. With the increasing demand of image transmission and storage， especially in bandwidth limited or cloud storage situations， compressing images at extremely low bitrates is of great significance for improving transmission efficiency and saving storage space. Based on this， this paper presents a systematic review of very low bitrate compression techniques for lossy images. Firstly， on the basis of problems of image compression derivative algorithms based on generative adversarial network （GAN） in terms of high-resolution image compression， generating image blur， and neglecting semantic and texture information， the latest very low bitrate image compression methods are introduced. Then， this paper elaborates image compression methods that achieve very low bitrate using non-GAN models such as layered compression， object based， and region of interest. After that， the commonly used datasets and image quality evaluation methods under lossy compression conditions are described. Finally， a summary of very low bitrate lossy image compression techniques are made， and an outlook on their subsequent development is given.