A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral compression of mesh geometry
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Volume Data and Wavelet Transforms
IEEE Computer Graphics and Applications
Lossless Compression of High-volume Numerical Data from Simulations
DCC '00 Proceedings of the Conference on Data Compression
Approximation and rendering of volume data using wavelet transforms
VIS '92 Proceedings of the 3rd conference on Visualization '92
Multi-resolution modeling of large scale scientific simulation data
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Fast Lossless Compression of Scientific Floating-Point Data
DCC '06 Proceedings of the Data Compression Conference
An Adaptive Sub-sampling Method for In-memory Compression of Scientific Data
DCC '09 Proceedings of the 2009 Data Compression Conference
A method of adaptive coarsening for compressing scientific datasets
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Spatially adaptive subsampling of image sequences
IEEE Transactions on Image Processing
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This paper focuses on developing effective and efficient algorithms for compressing scientific simulation data computed on structured and unstructured grids. A paradigm for lossy compression of this data is proposed in which the data computed on the grid is modeled as a graph, which gets decomposed into sets of vertices which satisfy a user defined error constraint ε. Each set of vertices is replaced by a constant value with reconstruction error bounded by ε. A comprehensive set of experiments is conducted by comparing these algorithms and other state-of-the-art scientific data compression methods. Over our benchmark suite, our methods obtained compression of 1% of the original size with average PSNR of 43.00 and 3% of the original size with average PSNR of 63.30. In addition, our schemes outperform other state-of-the-art lossy compression approaches and require on the average 25% of the space required by them for similar or better PSNR levels.