Further GPU implementation of prediction-based lower triangular
transform using a zero-order entropy coder for ultraspectral
sounder data compression

Shih-Chieh Wei; Bormin Huang

doi:10.1117/12.979000

8 November 2012 Further GPU implementation of prediction-based lower triangular transform using a zero-order entropy coder for ultraspectral sounder data compression

Shih-Chieh Wei, Bormin Huang

Author Affiliations +

Proceedings Volume 8539, High-Performance Computing in Remote Sensing II; 85390G (2012) https://doi.org/10.1117/12.979000
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

The ultraspectral sounder data consists of two dimensional pixels, each containing thousands of channels. In retrieval of geophysical parameters, the sounder data is sensitive to noises. Therefore lossless compression is highly desired for storing and transmitting the huge volume data. The prediction-based lower triangular transform (PLT) features the same de-correlation and coding gain properties as the Karhunen-Loeve transform (KLT), but with a lower design and implementational cost. In previous work, we have shown that PLT has the perfect reconstruction property which allows its direct use for lossless compression of sounder data. However PLT is time-consuming in doing compression. To speed up the PLT encoding scheme, we have recently exploited the parallel compute power of modern graphics processing unit (GPU) and implemented several important transform stages to compute the transform coefficients on GPU. In this work, we further incorporated a GPU-based zero-order entropy coder for the last stage of compression. The experimental result shows that our full implementation of the PLT encoding scheme on GPU shows a speedup of 88x compared to its original full implementation on CPU.

Citation Download Citation

Shih-Chieh Wei and Bormin Huang "Further GPU implementation of prediction-based lower triangular transform using a zero-order entropy coder for ultraspectral sounder data compression", Proc. SPIE 8539, High-Performance Computing in Remote Sensing II, 85390G (8 November 2012); https://doi.org/10.1117/12.979000

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