OpenDMC: An Open-Source Library and Performance Evaluation for Deep-learning-based Multi-frame Compression

Abstract

Video streaming has become an essential component of our everyday routines. Nevertheless, video data imposes a significant strain on data usage, demanding substantial bandwidth and storage resources for effective transmission. To suit explosively increasing video transmission and storage requirements, deep-learning-based video compression has developed rapidly in the past few years. New methods have mushroomed in order to achieve better Rate-Distortion (RD) performance. However, the absence of an algorithm library that can effectively sort, classify, and conduct extensive benchmark testing on existing algorithms remains a challenge. In this paper, we present an open-source algorithm library called OpenDMC, which integrates a variety of end-to-end video compression methods in cross-platform environments. We provide comprehensive descriptions of the algorithms used in the library, including their contributions and implementation details. We perform a thorough benchmarking test to evaluate the performance of the algorithms. We meticulously compare and analyze each algorithm based on various metrics, including RD performance, running time, and GPU memory usage. The open-source library for OpenDMC is available at https://openi.pcl.ac.cn/OpenDMC/.