3D weather radar image compression using multiscale recurrent patterns

In this work we use the multidimensional multiscale parser (MMP) algorithm to compress three-dimensional data from weather radar. MMP is based on approximate multiscale pattern matching. MMP encodes segments of an input signal using expanded and contracted versions of patterns stored in a dictionary...

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Bibliographic Details
Main Authors: Frauche, A.L.V., de Carvalho, M.B., da Silva, E.A.B.
Format: Conference Proceeding
Language:English
Subjects:
3D radar data
Concatenated codes
Data compression
Dictionaries
Electrocardiography
Electromyography
Image coding
Image segmentation
Meteorological radar
Multidimensional systems
Multiscale Decomposition
Pattern matching
Recurrent Pattern Matching
Vector Quantization
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Description
Summary:In this work we use the multidimensional multiscale parser (MMP) algorithm to compress three-dimensional data from weather radar. MMP is based on approximate multiscale pattern matching. MMP encodes segments of an input signal using expanded and contracted versions of patterns stored in a dictionary. The dictionary is updated using concatenated and displaced versions of previously encoded segments, therefore MMP builds its own dictionary while the input data is being encoded. MMP can be adapted to compress signals of any number of dimensions, and has been successfully applied to compress two-dimensional image data and one-dimensional ECG and EMG signals. We show simulation results where our 3D version of MMP outperforms some of the best encoders in the literature.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2008.4711938