Viewpoint-Adaptive Collage-based Streaming for 4K Light Field Video

This paper presents a viewpoint-adaptive, collage-based light field (LF) video streaming method to optimize data transmission for real-time rendering. Traditional LF video rendering encounters bottlenecks due to the high data volume needed for real-time viewpoint tracking, making storage and GPU memory constraints a significant challenge. To address this, we propose a collage-based approach that transmits only the necessary data based on the user’s current viewpoint, reducing data load by converting 4-dimensional (4D) LF frames into 2-dimensional (2D) collage frames. Additionally, we introduce a collage-based LF stream-switching technique at the group-of-pictures (GOP) level, leveraging I-frames for efficient switching and minimizing latency. Experiments demonstrate the effectiveness of our method, achieving stable transmission and optimized memory usage for 4K LF videos without sacrificing real-time performance. The results confirm that small tile sizes and avoiding partitioning provide the best performance for real-time LF video streaming.