Learning Based Video Compression

Learning-based video compression aims to improve video compression efficiency and quality by leveraging deep learning models, surpassing traditional methods in rate-distortion performance. Current research focuses on developing novel architectures, such as those employing conditional normalizing flows, hierarchical neural representations, and multi-mode prediction, to better handle diverse motion patterns and improve the accuracy of temporal prediction. These advancements address limitations in existing models, such as high computational complexity and the mismatch between learned and true data distributions, leading to more efficient and effective video compression for various applications, including video streaming and visual recognition. The resulting improvements in compression efficiency and quality have significant implications for bandwidth usage and user experience.