Analysis of Lossless Compressors Applied to Integer and Floating-Point Astronomical Data

In this work, lossless compression algorithms are evaluated on a variety of real, current as-tronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial tele-scopes. Compression t...

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Bibliographic Details
Authors: Maireles González, Òscar|||0000-0003-4754-0373, Bartrina-Rapesta, Joan|||0000-0002-1551-3680, Hernández-Cabronero, Miguel|||0000-0001-9301-4337, Serra-Sagristà, Joan|||0000-0003-4729-9292
Format: book part
Publication Date:2022
Country:España
Institution:Universitat Autònoma de Barcelona
Repository:Dipòsit Digital de Documents de la UAB
Language:English
OAI Identifier:oai:ddd.uab.cat:304347
Online Access:https://ddd.uab.cat/record/304347
https://dx.doi.org/urn:doi:10.1109/DCC52660.2022.00047
Access Level:Open access
Keyword:Observatories
Image coding
Data compression
Compressors
Encoding
Compression algorithms
Compressor
Lossless Compression
Astronomical Data
Integer Data
Source Code
Compression Ratio
Celestial Bodies
Discrete Objects
Spatial Dimensions
Wavelet Transform
Spectral Properties
Technical Data
Image Compression
Coding Tree
High Compression Ratio
Dominant Background
Entropy Coding
Burrows-Wheeler Transform
Description
Summary:In this work, lossless compression algorithms are evaluated on a variety of real, current as-tronomical images. The test dataset comprises raw (integer) and processed (floating-point) images of discrete and extensive astronomical objects, captured by spatial or terrestrial tele-scopes. Compression techniques herein analyzed are chosen to be representative of the most recent algorithms devised for astronomical data, as well as the most commonly employed compressors employed in real observatories. Experimental results suggest that coding techniques such as RICE and HCOMPRESS, typically employed in world-class observatories such as Roque de los Muchachos, do not produce the best possible lossless compression results. Instead, JPEG-LS, LZMA and NDZIP yield the best compression ratio results for 16-bit data (2.72), floating-point data (2.38) and radio data (1.81), respectively. Therefore, the efficiency with which data are stored and transmitted by these observatories could be significantly improved by selectively employing the aforementioned algorithms.

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