AI/ML Driven Video Compression

AI/ML techniques offer a paradigm shift from conventional codec-based approaches by leveraging both discriminative and generative deep learning to intelligently encode and decode video content. These methods enable cognitive analysis of video data, optimizing compression parameters based on content characteristics and network conditions. By integrating AI/ML into video compression pipelines, substantial improvements in compression ratios could be attained while preserving or even enhancing perceptual quality, thereby revolutionizing how multimedia content is stored, transmitted, and consumed across various platforms.

This research seeks to explore the theoretical foundations and practical implementations of AI/ML driven video compression, addressing challenges such as computational efficiency, scalability, and the generalization of AI/ML models across diverse video content types. By evaluating and comparing these techniques against traditional standards, we seek to provide insights into their potential to redefine the boundaries of video compression technology, paving the way for more efficient and adaptive multimedia systems in the future.