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1 Introduction

From year to year, the quantity of astronomical data increases at an ever growing rate. In part this is due to very large digitized sky surveys in the optical and near infrared, which in turn owes its origin to the development of digital imaging arrays such as CCDs. The size of digital arrays continually increases following the demands of astronomical research for obtaining larger quantities of data in shorter time periods. Currently, projects such as the European DENIS and American 2MASS infrared sky surveys, or the Franco-Canadian MEGACAM Survey and the American SLOAN Digital Sky Survey, will each produce of the order of 10 Tbytes of image data. The routine and massive digitization of photographic plates have been made possible by the advent of automatic plate scanning machines (MAMA, APM, COSMOS, SuperCOSMOS, APS, PMM, PDSs) (Richter 1998). These machines allow for the quantification of the truly enormous amount of useful astronomical data represented in a photograph of the sky, and they have realized the full potential of the large area photographic sky surveys. Clearly the storage and manipulation of astronomical data always requires the latest innovation in archiving techniques (12'' or $5\frac{1}{4}''$ WORM in the past, CD WORMS or even magnetic disks with RAID technology now, hopefully DVD in the near future). In addition, the simple transfer of such amounts of data over computer networks becomes too cumbersome and in some cases practically impossible. The transmission of a high resolution Schmidt plate image over the Internet would take of the order of 50 hours. Facing this extraordinary increase in pixel volumes, and taking into account the fact that catalogues produced by extraction of information from the pixels can always be locally wrong or incomplete, the needs of the astronomer follow two very different directions: Thus, the astronomical community is confronted with a rather desperate need for data compression techniques. Several techniques have in fact been used, or even developed, in the field of astronomy. Véran & Wright (1994) studied lossless techniques. White et al. (1992) developed HCOMPRESS, based on the H-transform, Press et al. (1992) developed FITSPRESS based on the Daubechies wavelet transform. JPEG, a general purpose standard has been tested by us. Compression based on the multiresolution pyramidal median transform (PMT) algorithm has been developed by Starck et al. (1996). Huang & Bijaoui (1991) introduced MathMorph for astronomical image processing.

This issue of data compression has become more important to us at the Centre de Données astronomiques de Strasbourg (CDS) in the context of the ALADIN project, an all-sky "clickable map'' service provided by Strasbourg Observatory (Bartlett et al. 1996), which has been developed with the goal of storing and providing network access to a full sky archive of digitized Schmidt plates. Currently we provide access to this archive on the Web, allowing users of the CDS services to retrieve images of objects starting from occurence of names in astronomical catalogues, the SIMBAD database, and bibliographical references such as titles or abstracts.

In previous papers (Starck et al. 1996; Murtagh et al. 1998) we reported our findings concerning the effects of three compression algorithms on astrometry and photometry. The three methods considered were HCOMPRESS, FITSPRESS and the video standard JPEG. Concerning the signal-to-noise ratio, the photometry and astrometry, JPEG and HCOMPRESS produce images of equivalent quality, but FITSPRESS is worse than the other two methods. The conclusion of this study is that the standard JPEG method was, ultimately, not so bad, even if block artifacts appear. However such block artifacts certainly appeared to be prohibitive for compression rates above 40.

In the present work, we extend our research to more realistic astronomical settings by considering stars with pixel values closer to the background, and by examining different criteria for position and magnitude determination. In addition to the three previous compression methods we studied two other methods: PMT, based on multiresolution analysis, and another one based on mathematical morphology, both implemented in the MR/1 package (MR/1 1998). We used a sample of nearly 2000 stars from an ESO Schmidt plate (not one, in fact, belonging to the survey plates) centred on the globular cluster M 5. The results indicate that PMT can give compression ratios of up to 5 times the maximum ratio obtained from the other methods, when the regions are not too dense.

The next section contains a brief and general description of image compression techniques, and of the four compression software packages, FITSPRESS, HCOMPRESS, JPEG and PMT. This is followed in Sect. 3 by a presentation of the data and calibrations used for our study (and a discussion of our approach to testing the astronomical quality assesments of the compressed images), and a presentation of our results. We then conclude in Sect. 4.


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