» New Image Compression Algorithm Based on Adaptive Wavelet Decompositon

Presenter: Petr Pata

This paper deals with a compression of image data in astronomy applications. Astronomical images are typical with their specific properties – high grayscale bit depth, size, noise occurrence and special processing algorithms. They belong to the class of scientific images. Their processing and compression is quite different from the classic approach to the processing of multimedia images. Database of images from BOOTES (Burst Observer and Optical Transient Exploring System) has been chosen as a source of the testing signal. BOOTES is a Czech – Spanish robotic telescope for observing of AGN (active galactic nuclei) and optical transient of GRB (gamma ray bursts) searching. There is discussed an approach based on analysis of statistical properties of image data in this paper. The statistical distribution of image functions in astronomical images from wide field and deep sky cameras is compared with Gaussian and Laplacean probability density function (pdf). The comparison of two irrelevancy reduction methods is presented from a scientific (astrometry and photometry) point of view. First one is based on a statistical approach to data compression and it is suggested from the Karhunen-Loève transform (KLT) with uniform quantization in spectral domain. Second technique is derived from wavelet decomposition with adaptive choosing of used mother wavelet.

J. Schindler