End-to-end image compression has achieved satisfactory results in recent years. However, existing methods suffer from the high complexity of complicated neural networks and cannot be directly deployed on mobile devices due to the limitations of computation and storage. Therefore, considering the resource and computing ability constrains of the mobile devices, we make a trade-off in this paper between rate-distortion (R-D) performance, inference time, and model complexity. Then we design a novel lightweight perceptual image compression framework to alleviate the storage and complexity burden of mobile devices. Moreover, we design a hardware-friendly deployment scheme to apply the proposed compression framework on high-end mobile devices, which can achieve efficient image compression. Based on the above structures, we propose the first mobile system that achieves image compression on mobile devices. The runtime screen recording of our application on OPPO PFFM10 device are shown below.