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, decreasing bitrate by a factor of two. Interestingly, the technology does not change radically between codecs generations. Instead, the same principles are re-used and pushed further. There have 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, with speculation arising as to whether it could be a 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.
- Print ISSN
- 1545-0279
- Electronic ISSN
- 2160-2492
- Published
- 2024-04
- Content type
- Original Research
- Keywords
- video compression, video codec, mpeg-2, h.264, avc, hevc, vvc, artificial intelligence
- DOI
- 10.5594/JMI.2024/IPYX8877