The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Fixed-Rate Compressed Floating-Point Arrays
Open accessAbstract
Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4(d) values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.
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Cites
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
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Embedded image coding using zerotrees of wavelet coefficients
Shapiro, J. M. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258085 · Cited by 4,815
Factoring wavelet transforms into lifting steps
Daubechies, I., & Sweldens, W. (2005) · Lecture Notes in Earth Sciences · doi:10.1007/bfb0011095 · Cited by 2,015
Factoring wavelet transforms into lifting steps
Daubechies, I., & Sweldens, W. (1998) · The Journal of Fourier Analysis and Applications · doi:10.1007/bf02476026 · Cited by 1,858
Comparing Clusterings by the Variation of Information
Meilă, M. (2003) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-45167-9_14 · Cited by 604
Engel, K., Hadwiger, M., Kniss, J., Rezk-Salama, C., & Weiskopf, D. (2006) · A K Peters/CRC Press eBooks · doi:10.1201/b10629 · Cited by 575
Fast and Efficient Compression of Floating-Point Data
Lindstrom, P., & Isenburg, M. (2006) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2006.143 · Cited by 442
Self-citationExploring Traditional and Emerging Parallel Programming Models Using a Proxy Application
Karlin, I., Bhatele, A., Keasler, J., Chamberlain, B. L., Cohen, J., Devito, Z., Haque, R., Laney, D., Luke, E., Wang, F., et al. (2013) · 2013 IEEE 27th International Symposium on Parallel and Distributed Processing · doi:10.1109/ipdps.2013.115 · Cited by 194
Fast Lossless Compression of Scientific Floating-Point Data
Ratanaworabhan, P., Ke, J., & Burtscher, M. (2006) · Data Compression Conference (DCC'06) · doi:10.1109/dcc.2006.35 · Cited by 159
Compression domain volume rendering
Schneider, J., & Westermann, R. (2003) · Visualization · doi:10.1109/visual.2003.1250385 · Cited by 145
Cited by
What is the State of Neural Network Pruning?
Blalock, D., Ortiz, J. J. G., Frankle, J., & Guttag, J. (2020) · arXiv · doi:10.48550/arxiv.2003.03033 · Cited by 560
Tao, D., Di, S., Chen, Z., & Cappello, F. (2017) · 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps.2017.115 · Cited by 292
Metadata
- Authors
- Lindstrom, P.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2014-12-31
- Volume
- 20
- Issue
- 12
- Pages
- 2674-2683
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W2052440657
- Cited by
- 676
- References
- 16
- FWCI
- 14.24
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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