Review and Evaluation of End-to-End Video Compression with Deep-Learning

Abstract

Recent years have shown exponential growth in video processing and transfer through the Internet and other applications. With the restriction on bandwidth, processing, and storage, there is an extensive demand for end-to-end video compression. Many conventional methods have been developed to compress video. However, with the extensive use of Artificial Intelligence, AI, such as Deep Learning (DL) have emerged as a best-of-breed alternative for performing different tasks have also been used in the option of improving video compression in the last years, with the primary objective of reducing compression ratio while preserving the same video quality. Evolving video compression research based on Neural Networks (NNs) focuses on two distinct directions: First, enhancing current video codecs by better predictions integrated even in the same codec framework, and second, holistic end-to-end VC systems approach. Although some of the outcomes are optimistic and the results are well, no breakthrough has been reported previously. This paper reviews new research work, including samples of a few influential articles that demonstrate. Further, describe the various highlighted issues in the area of using DL for end-to-end video compression.