OpenDIC: An Open-Source Library and Performance Evaluation for Deep-learning-based Image Compression
Wei Gao, Huiming Zheng, Chenhao Zhang, Kebing Zheng, Zhuozhen Yu, Yuan Li, Hua Ye, Yongchi 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/.