36 research outputs found

    Lossy data compression for imaginginterferometer data using a wavelet transform-based image compression algorithm

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    Data compression on future space-based imaging interferometers can be used to reduce high telemetry costs, provided the performance is acceptable. This paper investigates lossy data compression of imaging interferometer datacubes using a wavelet transform-based compression algorithm, the Set Partitioning in Hierarchical Trees (SPIHT) image compression algorithm. Compression is performed on individual frames of the interferogram datacubes. Simulated datacubes from the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) are modified to produce new complex GIFTS datacubes used to perform the experiments. Separate programs are written for the encoder and decoder in C++. The encoder and decoder are simulated to the bit-level, meaning they simulate the exact bit streams that would be generated by hardware implementations. All compression ratios reported are based on the actual file size of the encoded data. The simulations indicate very high performance of the algorithm in the interferogram domain, with average errors of less than one least significant bit (LSB) for the GIFTS long-wave band and just over one LSB for the GIFTS short/mid-wave band at compression ratios as high as 13.7:1 and 15.4:1, respectively. At the same compression ratios, errors in the spectral radiance domain are comparable to the simulated instrument noise and RMS temperature profile retrieval errors of less than 1 K are achieved using a University of Wisconsin-Madison prototype retrieval algorithm

    CCA performance of anew source list/EZW hybrid compression algorithm

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    A new data compression algorithm for encoding astronomical source lists is presented. Two experiments in combined compression and analysis (CCA) are described, the first using simulated imagery based upon a tractable source list model, and the second using images from SPIRIT III, a spaceborne infrared sensor. A CCA system consisting of the source list compressor followed by a zerotree-wavelet residual encoder is compared to alternatives based on three other astronomical image compression algorithms. CCA performance is expressed in terms of image distortion along with relevant measures of point source detection and estimation quality. Some variations of performance with compression bit rate and point source flux are characterized. While most of the compression algorithms reduce high-frequency quantum noise at certain bit rates, conclusive evidence is not found that such denoising brings an improvement in point source detection or estimation performance of the CCA systems. The proposed algorithm is a top performer in every measure of CCA performance; the computational complexity is relatively high, however
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