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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/62273


    Title: GPU acceleration of predictive partitioned vector quantization for ultraspectral sounder data compression
    Authors: Wei, Shih-chieh;Huang, Bormin
    Contributors: 淡江大學資訊管理學系
    Keywords: Graphics processing unit;Vector quantization;Training;Instruction sets;Vectors;Kernel;Pixel
    Date: 2011-09
    Issue Date: 2011-10-18 13:03:25 (UTC+8)
    Publisher: Piscataway: Institute of Electrical and Electronics Engineers
    Abstract: For the large-volume ultraspectral sounder data, compression is desirable to save storage space and transmission time. To retrieve the geophysical paramters without losing precision the ultraspectral sounder data compression has to be lossless. Recently there is a boom on the use of graphic processor units (GPU) for speedup of scientific computations. By identifying the time dominant portions of the code that can be executed in parallel, significant speedup can be achieved by using GPU. Predictive partitioned vector quantization (PPVQ) has been proven to be an effective lossless compression scheme for ultraspectral sounder data. It consists of linear prediction, bit depth partitioning, vector quantization, and entropy coding. Two most time consuming stages of linear prediction and vector quantization are chosen for GPU-based implementation. By exploiting the data parallel characteristics of these two stages, a spatial division design shows a speedup of 72x in our four-GPU-based implementation of the PPVQ compression scheme.
    Relation: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4(3), pp.677-682
    DOI: 10.1109/JSTARS.2011.2132117
    Appears in Collections:[Graduate Institute & Department of Information Management] Journal Article

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