3 research outputs found
Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization
We present an approach for hierarchical super resolution (SR) using neural
networks on an octree data representation. We train a hierarchy of neural
networks, each capable of 2x upscaling in each spatial dimension between two
levels of detail, and use these networks in tandem to facilitate large scale
factor super resolution, scaling with the number of trained networks. We
utilize these networks in a hierarchical super resolution algorithm that
upscales multiresolution data to a uniform high resolution without introducing
seam artifacts on octree node boundaries. We evaluate application of this
algorithm in a data reduction framework by dynamically downscaling input data
to an octree-based data structure to represent the multiresolution data before
compressing for additional storage reduction. We demonstrate that our approach
avoids seam artifacts common to multiresolution data formats, and show how
neural network super resolution assisted data reduction can preserve global
features better than compressors alone at the same compression ratios
Toward Feature-Preserving 2D and 3D Vector Field Compression
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 vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type change 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