摘要
The exponential growth of cultural heritage documentation videos calls for new compression methods that preserve critical details while reducing storage.For static scenes,traditional frame-based compression methods struggle with the trade-off between semantic redundancy and detail preservation.To improve compression efficiency,a novel dual-mode semantic compression framework for static object videos based on neural radiance fields(NeRF)was proposed in this paper.By integrating semantic segmentation with COLMAP technology,the proposed system decouples the video stream into two semantic layers,which are the central object containing critical details and the dynamic background rich in semantic redundancy,respectively.In the proposed dual-mode framework,the focus-priority(FP)mode is designed for scenarios with high-efficiency demands,where only the NeRF-based neural representation of the primary object is preserved and compressed.For scenarios that require additional environmental context,the panorama-compatible(PC)mode synchronously compresses the H.264-encoded background streams and the primary object streams to reconstruct the full scene.Experimental results on single-artifact video data demonstrate that the proposed framework achieves a storage reduction of 20%compared with conventional methods,thus providing a flexible and controllable solution for the compression of cultural heritage documentation videos.
基金
supported by the National Key Research and Development Program of China(2022YFB2902100)。
