A GPU-based implementation of predictive partitioned vector quantization for compression of ultraspectral sounder data

Shih-Chieh Wei; Bormin Huang

doi:10.1117/12.863275

24 August 2010 A GPU-based implementation of predictive partitioned vector quantization for compression of ultraspectral sounder data

Shih-Chieh Wei, Bormin Huang

Author Affiliations +

Proceedings Volume 7810, Satellite Data Compression, Communications, and Processing VI; 781017 (2010) https://doi.org/10.1117/12.863275
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

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 a GPU-based implementation. For the voluminous ultraspectral sounder data, 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 partition, 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 speedup of 42x has been achieved in our GPU-based implementation of the PPVQ compression scheme.

Citation Download Citation

Shih-Chieh Wei and Bormin Huang "A GPU-based implementation of predictive partitioned vector quantization for compression of ultraspectral sounder data", Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 781017 (24 August 2010); https://doi.org/10.1117/12.863275

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