The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.
X. Liang et al., "Toward Feature-Preserving Vector Field Compression," IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, Jan 2022.
The definitive version is available at https://doi.org/10.1109/TVCG.2022.3214821
Keywords and Phrases
Critical points; Data visualization; Error correction; Image coding; Iterative methods; Jacobian matrices; lossy compression; Three-dimensional displays; Topology; vector field visualization
International Standard Serial Number (ISSN)
Article - Journal
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01 Jan 2022