The proposed Bits-to-Photon (B2P) scheme can be extended to support point cloud video compression by leveraging temporal redundancy across frames in a video sequence. This can be achieved through several strategies:
Temporal Context Utilization: By incorporating features from previously decoded frames, the B2P framework can exploit the temporal coherence inherent in video data. For instance, the features extracted from a point cloud in one frame can serve as a context for encoding the next frame, allowing the model to predict and compress only the changes or differences between consecutive frames. This predictive coding can significantly reduce the amount of data that needs to be transmitted.
Hierarchical Temporal Coding: Similar to the hierarchical spatial coding used in the current B2P framework, a hierarchical approach can be applied to temporal data. By encoding the point clouds at different temporal resolutions, the system can adaptively allocate bits based on the motion and complexity of the scene. For example, static scenes may require fewer bits, while dynamic scenes with significant changes may need more detailed encoding.
Motion Estimation and Compensation: Integrating motion estimation techniques can help identify and encode the movement of objects within the point cloud. By predicting the motion vectors and compensating for them, the B2P scheme can focus on encoding the residuals, which are typically smaller in size compared to the full point cloud data.
Temporal Scalability: The B2P framework can be designed to support scalable temporal layers, where different levels of detail can be transmitted based on the available bandwidth. This would allow for real-time streaming of point cloud video, where viewers can adjust the quality based on their network conditions.
By implementing these strategies, the B2P scheme can effectively compress point cloud video data, enhancing its applicability in real-time volumetric video streaming applications.