3D-Based Compression Framework for High Quality Video Streaming

Video compression plays a pivotal role in managing the storage and transmission of multimedia content, especially in bandwidth-constrained environments. Nowadays, volumetric video has emerged as an attractive multimedia application, which provides highly immersive watching experiences. How- ever, streaming the volumetric video demands prohibitively high bandwidth. Thus, effectively compressing its underlying point cloud frames is essential to deploying the volumetric videos. The existing compression techniques are either 3D-based or 2D-based, but they still have drawbacks when being deployed in practice. The 2D-based methods compress the videos in an effective but slow manner, while the 3D-based methods feature high coding speeds but low compression ratios. In this paper, we propose a 3D-based compression framework that reaches both a high compression ratio and a real-time decoding speed. This project presents an integrated hybrid video compression framework that combines traditional techniques such as motion estimation and Discrete Cosine Transform (DCT) coding with machine learning methodologies. The framework aims to improve compression efficiency and maintain high-quality video output through intelligent prediction and optimization.