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|a 10.1109/TVCG.2023.3345373
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|a eng
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|a Han, Jun
|e verfasserin
|4 aut
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|a KD-INR
|b Time-Varying Volumetric Data Compression via Knowledge Distillation-Based Implicit Neural Representation
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|a Date Revised 05.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Traditional deep learning algorithms assume that all data is available during training, which presents challenges when handling large-scale time-varying data. To address this issue, we propose a data reduction pipeline called knowledge distillation-based implicit neural representation (KD-INR) for compressing large-scale time-varying data. The approach consists of two stages: spatial compression and model aggregation. In the first stage, each time step is compressed using an implicit neural representation with bottleneck layers and features of interest preservation-based sampling. In the second stage, we utilize an offline knowledge distillation algorithm to extract knowledge from the trained models and aggregate it into a single model. We evaluated our approach on a variety of time-varying volumetric data sets. Both quantitative and qualitative results, such as PSNR, LPIPS, and rendered images, demonstrate that KD-INR surpasses the state-of-the-art approaches, including learning-based (i.e., CoordNet, NeurComp, and SIREN) and lossy compression (i.e., SZ3, ZFP, and TTHRESH) methods, at various compression ratios ranging from hundreds to ten thousand
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|a Zheng, Hao
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|a Bi, Chongke
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|t IEEE transactions on visualization and computer graphics
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|g 30(2024), 10 vom: 21. Sept., Seite 6826-6838
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|u http://dx.doi.org/10.1109/TVCG.2023.3345373
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