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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/62274


    Title: Further 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: Data compression;Graphics processing units;Quantization
    Date: 2011-11
    Issue Date: 2011-10-18 13:06:59 (UTC+8)
    Publisher: Society of Photo-Optical Instrumentation Engineers (SPIE)
    Abstract: For the ultraspectral sounder data which features thousands of channels at each observation location, lossless compression is desirable to save storage space and transmission time without losing precision in retrieval of geophysical parameters. 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. In our previous work, the two most time consuming stages of linear prediction and vector quantization were identified for GPU implementation. For GIFTS data, using a spectral division strategy for sharing the compression workload among four GPUs, a speedup of ~42x was achieved. To further enhance the speedup, this work will explore a spatial division strategy for sharing workload in processing the six parts of a GIFTS datacube. As result, the total processing time of a GIFTS datacube on four GPUs can be less than 13 seconds which is equivalent to a speedup of ~72x. The use of multiple GPUs for PPVQ compression is thus promising as a low-cost and effective compression solution for ultraspectral sounder data for rebroadcast use. © (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
    Relation: Proc. SPIE 8157, Satellite Data Compression, Communications, and Processing VII, 815704
    DOI: 10.1117/12.894390
    10.1117/12.894390
    Appears in Collections:[Graduate Institute & Department of Information Management] Proceeding

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