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 st...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.展开更多
As conventional communication systems based on classic information theory have closely approached Shannon capacity,semantic communication is emerging as a key enabling technology for the further improvement of communi...As conventional communication systems based on classic information theory have closely approached Shannon capacity,semantic communication is emerging as a key enabling technology for the further improvement of communication performance.However,it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission.In this paper,we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network.We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence.We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function.We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder,and obtain the corresponding rate distortion function.We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB2902100)。
文摘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partly supported by NSFC under grant No.62293481,No.62201505partly by the SUTDZJU IDEA Grant(SUTD-ZJU(VP)202102)。
文摘As conventional communication systems based on classic information theory have closely approached Shannon capacity,semantic communication is emerging as a key enabling technology for the further improvement of communication performance.However,it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission.In this paper,we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network.We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence.We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function.We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder,and obtain the corresponding rate distortion function.We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information.
