Geometric Prediction for Compression

Isosurfaces;Geometry;Simplification;Compression;Prediction

Ibarria, Lorenzo ; Computing

University:Georgia Institute of Technology

Department:Computing

关键词: Isosurfaces;    Geometry;    Simplification;    Compression;    Prediction;   

Others  :  https://smartech.gatech.edu/bitstream/1853/16162/1/ibarria_lorenzo_200708_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This thesis proposes several new predictors for the compression of shapes, volumes and animations. To compress frames in triangle-mesh animations with fixed connectivity, we introduce the ELP (Extended Lorenzo Predictor) and the Replica predictors that extrapolate the position of each vertex in frame $i$ from the position of each vertex in frame $i-1$ and from the position of its neighbors in both frames. For lossy compression we have combined these predictors with a segmentation of the animation into clips and a synchronized simplification of all frames in a clip. To compress 2D and 3D static or animated scalar fields sampled on a regular grid, we introduce the Lorenzo predictor well suited for scanline traversal and the family of Spectral predictors that accommodate any traversal and predict a sample value from known samples in a small neighborhood. Finally, to support the compressed streaming of isosurface animations, we have developed an approach that identifies all node-values needed to compute a given isosurface and encodes the unknown values using our Spectral predictor.