The impact of JPEG2000 lossy compression on the scientific quality of radio astronomy imagery

Abstract The sheer volume of data anticipated to be captured by future radio telescopes, such as, the Square Kilometer Array (SKA) and its precursors present new data challenges, including the cost and technical feasibility of data transport and storage. Image and data compression are going to be important techniques to reduce the data size. We provide a quantitative analysis of the effects of JPEG2000’s lossy wavelet image compression algorithm on the quality of the radio astronomy imagery data. This analysis is completed by evaluating the completeness, soundness and source parameterisation of the Duchamp source finder using compressed data. Here we found the JPEG2000 image compression has the potential to denoise image cubes, however this effect is only significant at high compression rates where the accuracy of source parameterisation is decreased.

[1]  Jin Li Image Compression: the Mathematics of JPEG 2000 , 2002 .

[2]  P. J. Teuben,et al.  A retrospective view of Miriad , 1995 .

[3]  R.M. Gray Image compression , 1991, [1991] Proceedings. Data Compression Conference.

[4]  Andrew P. Bradley Shift-invariance in the Discrete Wavelet Transform , 2003, DICTA.

[5]  Chen Wu,et al.  SkuareView: client-server framework for accessing extremely large radio astronomy image data. , 2012, Astro-HPC '12.

[6]  David R. DeBoer,et al.  Australian SKA Pathfinder: A High-Dynamic Range Wide-Field of View Survey Telescope , 2009, Proceedings of the IEEE.

[7]  Supercomputing,et al.  Predictions for ASKAP neutral hydrogen surveys , 2012, 1208.5592.

[8]  Touradj Ebrahimi,et al.  The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..

[9]  Tobias Westmeier,et al.  Comparison of Potential ASKAP Hi Survey Source Finders , 2012, Publications of the Astronomical Society of Australia.

[10]  Ben Humphreys,et al.  Source-Finding for the Australian Square Kilometre Array Pathfinder , 2012, Publications of the Astronomical Society of Australia.

[11]  L. Floer,et al.  Using Negative Detections to Estimate Source-Finder Reliability , 2011, Publications of the Astronomical Society of Australia.

[12]  An Overview of the Square Kilometre Array , 2013, 1311.4288.

[13]  David Taubman,et al.  Astronomical imagery: Considerations for a contemporary approach with JPEG2000 , 2014 .

[14]  Matthew T. Whiting,et al.  duchamp: a 3D source finder for spectral‐line data , 2012, 1201.2710.

[15]  Basic Testing of the duchamp Source Finder , 2011, Publications of the Astronomical Society of Australia.

[16]  Fionn Murtagh,et al.  Image restoration with noise suppression using a wavelet transform and a multiresolution support constraint , 1994, Optics & Photonics.

[17]  Jun Sun,et al.  Ringing Artifact Reduction for JPEG2000 Images , 2007, ICIC.

[18]  Russell Jurek,et al.  The Characterised Noise Hi Source Finder: Detecting Hi Galaxies Using a Novel Implementation of Matched Filtering , 2011, Publications of the Astronomical Society of Australia.

[19]  B. Winkel,et al.  2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys , 2011, Publications of the Astronomical Society of Australia.

[20]  Mark R. Calabretta,et al.  Representations of world coordinates in FITS , 2002, astro-ph/0207407.

[21]  Yvon Voisin,et al.  An Evaluation Framework and a Benchmark for Multi/Hyperspectral Image Compression , 2011, Int. J. Comput. Vis. Image Process..

[22]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2013, The Kluwer international series in engineering and computer science.

[23]  Mohamed-Jalal Fadili,et al.  The Undecimated Wavelet Decomposition and its Reconstruction , 2007, IEEE Transactions on Image Processing.

[24]  M. Meyer,et al.  Exploring the HI Universe with ASKAP , 2009, 0912.2167.