Literature DB >> 26356981

Fixed-Rate Compressed Floating-Point Arrays.

Peter Lindstrom.   

Abstract

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.

Year:  2014        PMID: 26356981     DOI: 10.1109/TVCG.2014.2346458

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  3 in total

1.  A framework for constraining image SNR loss due to MR raw data compression.

Authors:  Matthew C Restivo; Adrienne E Campbell-Washburn; Peter Kellman; Hui Xue; Rajiv Ramasawmy; Michael S Hansen
Journal:  MAGMA       Date:  2018-10-25       Impact factor: 2.310

2.  Efficient compressed database of equilibrated configurations of ring-linear polymer blends for MD simulations.

Authors:  Katsumi Hagita; Takahiro Murashima; Masao Ogino; Manabu Omiya; Kenji Ono; Tetsuo Deguchi; Hiroshi Jinnai; Toshihiro Kawakatsu
Journal:  Sci Data       Date:  2022-02-08       Impact factor: 6.444

3.  Lossy compression of statistical data using quantum annealer.

Authors:  Boram Yoon; Nga T T Nguyen; Chia Cheng Chang; Ermal Rrapaj
Journal:  Sci Rep       Date:  2022-03-09       Impact factor: 4.379

  3 in total

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