1 / 12

Astronomical Tiled Image Compression

Astronomical Tiled Image Compression. How & Why. Authors:. Rob Seaman, NOAO Bill Pence, NASA/GSFC Rick White, STScI Mark Dickinson, NOAO Frank Valdes, NOAO Nelson Z á rate, NOAO. Statement of problem. No one compression is always best

darice
Download Presentation

Astronomical Tiled Image Compression

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Astronomical Tiled Image Compression How & Why

  2. Authors: • Rob Seaman, NOAO • Bill Pence, NASA/GSFC • Rick White, STScI • Mark Dickinson, NOAO • Frank Valdes, NOAO • Nelson Zárate, NOAO

  3. Statement of problem • No one compression is always best • New instruments and survey programs will dwarf data sets that have come before • Observatories' data storage costs • Transport latency & bandwidth challenge not just budgets, but technology and human patience • The bottom line is data handling throughput, not static storage

  4. Host level compression • Per-file gzip compression • Contents of file are opaque • Speed of compression • Speed of decompression • Size of output • Limited support for on-the-fly decompression

  5. How • FITS tile compression convention • Provides a general framework • Supports any compression algorithm that can operate on multidimensional image sections • FITS headers remain readable • Access to individual FITS HDUs • Files are still FITS

  6. Limitations • Only partially supported by IRAF • Supported by CFITSIO, but caveats: • Not idempotent, even a losslessly compressed file would suffer keyword changes • Original convention covered only per-HDU issues, e.g., compressing a SIF produced same binary table as MEF original • Only application was the limited imcopy example program • Unsupported algorithms

  7. Improvements • fpack compression tool • Compress images in-place • Multi-image archives for efficiency • Idempotent • Supports FITS Checksum • Applications layered on CFITSIO access compressed files and file archives transparently • Support for Hcompress • General purpose option for adaptively scaling input data.

  8. fpack / funpack fpack, a FITS tile-compression engine. Version 0.8.2 (25 September 2006) usage: fpack [-r|-p|-g|-h] [-w|-t <axes>] [-n <bits>] [-v] [-Etc] <FITS> Flags must appear (separately) before filenames: -r Rice compression [default], or -p PLIO compression, or -g GZIP (per-tile) compression -h Hcompress compression -w override tile size to be whole image, or -t <axes> comma separated list of tile sizes [default=row] -n <bits> noise bits to preserve for real pixels [default=4] -v verbose -F clobber output [default overwrites input in-place] -K keep (don't delete, overwrite or change) input files -A <file> write (append or clobber) output to single file, or -P <pre> prepend <pre> to create separate output filenames -L list and validate contents, files unchanged -H print this message -V print version number <FITS> FITS files or extensions to pack

  9. … & Why • Preserve the scientific integrity of processed astronomical data sets • Native integer data products permit lossless compression techniques for neutral effect, or • May benefit from lossy compression for high compression factors • Processing, pipeline or hands-on, often creates floating point • Choose lossy compression, or • Scale data into integers

  10. Compression statistics Additional cost for gzip’ed floating point output from pipeline is $2.86 per image versus Rice compressed integers.

  11. Benefits • Reduced: • Diskspace • Bandwidth • Latency • Remove need to decompress • Pack multiple files for efficient transport • Headers remain readable • Individual HDUs are accessible • Choice of algorithm isn’t fixed

  12. DMS architecture • Benefits NSA, NHPP, NVO portal • No need for ASCII header files • Smaller footprint • Faster replication • Files remain FITS throughout • Extends upstream into domes • Extends downstream to users • Compression can be free or better than free

More Related