In the big data era, we have witnessed the explosive growth of deep learning based image and video compression technologies. Such end-to-end learning-based compression frameworks have demonstrated promising efficiency for compact representation of original image data, and attracted a vast attention from both academia and industry. A systematic review of transformation, quantization, entropy coding, and loss function used in end-to-end learning-based image compression framework is introduced in this work. The research progress and key technologies are briefly introduced, as well as the comparative studies of coding performance for existing methods with leading efficiency.