The future of video compression - Moving beyond hybrid codecs with machine learning

The consumption of video content on the internet is increasing at a constant pace, along with an increase of video quality. As an answer to the ever-growing demand for high quality video, compression technology improves steadily. About every decade, a new major video compression standard is issued, providing a decrease of bitrate by a factor two. Interestingly, the technology does not change radically between codecs generations. Instead, the same principles are re-used and pushed further. There has been several attempts to depart from this model, but none achieved to be competitive. Recently, the research community has started focusing on deep learning-based strategies. Could it be the new contender to the classical approach? This paper analyzes the benefits and limitations of deep learning-based video compression methods, and investigates practical aspects such as rate control, delay, memory consumption and power consumption. Overlapping patch-based end-to-end video compression strategy is proposed to overcome memory limitations.

Published

2023-10

Content type

Original Research

Keywords

Video Compression, Video codec, MPEG-2, H.264, AVC, HEVC, VVC, artificial intelligence, machine learning, deep learning, end-to-end video encoding

DOI

10.5594/M002026

ISBN

978-1-61482-964-5