{"title":"基于视频压缩的大时变数据多分辨率体绘制","authors":"Chia-Lin Ko, Horng-Shyang Liao, Tsaipei Wang, Kuang-Wei Fu, Ching-Yao Lin, Jung-Hong Chuang","doi":"10.1109/PACIFICVIS.2008.4475469","DOIUrl":null,"url":null,"abstract":"We present a new framework that combines the hierarchical multi-resolution representation with video-based compression to manage and render large scale time-varying data. In the preprocessing step, the proposed method first constructs a multi-resolution hierarchy using octree structure for each individual time step, and then applies a motion-compensation-based prediction to compress the octree nodes. During rendering stage, the data is decompressed on-the-fly and rendered using hardware texture mapping. The proposed approach eliminates the hierarchical decompression dependency commonly found in the conventional hierarchical wavelet representation methods, which leads to a more efficient reconstruction of data along the time axis. The system provides the user with a spatial region-of-interest (ROI) to adjust the spatial level-of-detail (LOD) selection, and a temporal ROI which is a sub-region only for frequent update during playback. With a suitable control of both ROIs, our system can reach an interactive playback frame rate. This allows the user to observe the dynamic nature of large time-varying data sets.","PeriodicalId":364669,"journal":{"name":"2008 IEEE Pacific Visualization Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Multi-resolution Volume Rendering of Large Time-Varying Data using Video-based Compression\",\"authors\":\"Chia-Lin Ko, Horng-Shyang Liao, Tsaipei Wang, Kuang-Wei Fu, Ching-Yao Lin, Jung-Hong Chuang\",\"doi\":\"10.1109/PACIFICVIS.2008.4475469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a new framework that combines the hierarchical multi-resolution representation with video-based compression to manage and render large scale time-varying data. In the preprocessing step, the proposed method first constructs a multi-resolution hierarchy using octree structure for each individual time step, and then applies a motion-compensation-based prediction to compress the octree nodes. During rendering stage, the data is decompressed on-the-fly and rendered using hardware texture mapping. The proposed approach eliminates the hierarchical decompression dependency commonly found in the conventional hierarchical wavelet representation methods, which leads to a more efficient reconstruction of data along the time axis. The system provides the user with a spatial region-of-interest (ROI) to adjust the spatial level-of-detail (LOD) selection, and a temporal ROI which is a sub-region only for frequent update during playback. With a suitable control of both ROIs, our system can reach an interactive playback frame rate. This allows the user to observe the dynamic nature of large time-varying data sets.\",\"PeriodicalId\":364669,\"journal\":{\"name\":\"2008 IEEE Pacific Visualization Symposium\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE Pacific Visualization Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACIFICVIS.2008.4475469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Pacific Visualization Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACIFICVIS.2008.4475469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-resolution Volume Rendering of Large Time-Varying Data using Video-based Compression
We present a new framework that combines the hierarchical multi-resolution representation with video-based compression to manage and render large scale time-varying data. In the preprocessing step, the proposed method first constructs a multi-resolution hierarchy using octree structure for each individual time step, and then applies a motion-compensation-based prediction to compress the octree nodes. During rendering stage, the data is decompressed on-the-fly and rendered using hardware texture mapping. The proposed approach eliminates the hierarchical decompression dependency commonly found in the conventional hierarchical wavelet representation methods, which leads to a more efficient reconstruction of data along the time axis. The system provides the user with a spatial region-of-interest (ROI) to adjust the spatial level-of-detail (LOD) selection, and a temporal ROI which is a sub-region only for frequent update during playback. With a suitable control of both ROIs, our system can reach an interactive playback frame rate. This allows the user to observe the dynamic nature of large time-varying data sets.