Comprehensive Review of End-to-End Video Compression
Liangfan Shi, Huimin Lu · 2024
In recent years, end-to-end video compression has emerged as a new and promising solution for video compression. This paper provides a comprehensive review of the development and current state of end-to-end video encoding and decoding technologies, detailing the fundamental principles of both traditional hybrid encoders and end-to-end encoders. It extensively discusses the evolution from traditional video compression algorithms to innovative methods based on deep learning, including the development of various optimization strategies and key technologies using Deep Neural Networks (DNN). The paper especially focuses on the advancement of end-to-end video compression frameworks such as DVC, DCVC, and Transformer-based models, highlighting their impact on enhancing the efficiency and quality of video compression. Additionally, it summarizes the latest research findings in this field and offers a brief outlook on future directions.