OpenDIC: An Open-Source Library and Performance Evaluation for Deep-learning-based Image Compression

Wei Gao, Huiming Zheng, Chenhao Zhang

Abstract: Deep learning technologies have been popular in the image compression field for some time. An increasing number of deep-learning-based models are proposed to improve Rate-Distortion (RD) performance. Previous algorithms are implemented in the specific platform and can not be applied in cross-platform environments. In this paper, we present an open-source algorithm library called OpenDIC, which integrates a variety of end-to-end image compression methods in cross-platform environments. The contribution and details of the algorithms used in the library are described. To evaluate the performance of these algorithms, we conduct a comprehensive performance test. We compare and analyze each algorithm according to RD performance, running time, and GPU memory occupancy. The algorithm library has been released at https://openi.pcl.ac.cn/OpenDIC/.