Literature study of learning-based video compression

Abstract

Developments in telecommunications technology today, such as cellular with the fifth generation (5G), the development of IoT prototypes, and the migration of analog TV to digital TV starting in 2022. The development of various research using machine learning. The problem with video format information is that the video file size is quite large, so the transmission process requires a large bandwidth. In addition, sharing services such as Video on Demand (VoD) and Video Broadcasting are sensitive to delay. In comparison, the transmission media has limited capacity, such as terrestrial TV, Ethernet/Fast Ethernet, and wireless cellular data such as 2G, 3G HSPA, 4G, etc. Based on reports from Cisco, the development of internet users has increased by 10% per year, with 80% of total traffic using video. Developments in various video compression standards, such as the most recent H.264 and H.265, produce high-quality, low-bitrate video. Much research has been carried out with various proposed compression methods based on machine learning. Either uses singular block learning based or end-to-end. This research focuses on the literature study of video compression with machine learning.