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Accelerating the prediction-based lower triangular transform for data compression using Intel MIC 5

Accelerating the prediction-based lower triangular transform...

引用

Conference on High-Performance Computing in Remote Sensing V

作者： Wei, Shih-Chieh Huang, Bormin Tamkang Univ Dept Informat Management Tamsui 25137 Taiwan Univ Wisconsin CIMSS SSEC Madison WI 53706 USA

ISBN: (纸本)9781628418569

With the same decorrelation and coding gain capabilities as the Karhunen-Loeve transform, the prediction-based lower triangular transform (PLT) can apply its perfect reconstruction property for lossless compression of ultraspectral sounder data. As the compression process requires computation of the covariance matrix, the LDU decomposition, and the transform kernel and coefficient matrices, it will be beneficial to introduce parallel processing technology in the PLT implementation. In this work, the recent Intel Many Integrated Core (MIC) architecture will be used which can exploit up to 60 cores with 4 hardware threads per core. Both threading and vectorization techniques will be explored for performance improvement. In our experiment, the total processing time of an AIRS granule can have a speedup of similar to 4.6x. With the offload mode, the MIC architecture provides a convenient and efficient tool for parallelization of the PLT compression scheme.

关键词： prediction-based lower triangular transform lossless data compression Intel MIC architecture

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Further GPU Implementation of prediction-based lower triangular transform Using a Zero-Order Entropy Coder for Ultraspectral Sounder Data Compression

Further GPU Implementation of Prediction-Based Lower Triangu...

引用

Conference on High-Performance Computing in Remote Sensing II

作者： Wei, Shih-Chieh Huang, Bormin Univ Wisconsin Ctr Space Sci & Engn 1225 W Dayton St Madison WI 53706 USA Tamkang Univ Dept Informat Management Tamsui 25137 Taiwan

ISBN: (纸本)9780819492791

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.

关键词： prediction-based lower triangular transform graphics processing unit entropy coder

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Decorrelating transforms for spectral vector quantization

Decorrelating transforms for spectral vector quantization

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18th International Conference on Digital Signal Processing (DSP)

作者： Ramirez, Miguel Arjona Univ Sao Paulo Escola Politecn Dept Elect Syst Engn Sao Paulo Brazil

ISBN: (纸本)9781467358057

Split vector quantization (SVQ) performs well and efficiently for line spectral frequency (LSF) quantization, but misses some component dependencies. Switched SVQ (SSVQ) can restore some advantage due to nonlinear dependencies through Gaussian Mixture Models (GMM). Remaining linear dependencies or correlations between vector components can be used to advantage by transform coding. The Karhunen-Loeve transform (KLT) is normally used but eigendecomposition and full transform matrices make it computationally complex. However, a family of transforms has been recently characterized by the capability of generalized triangular decomposition (GTD) of the source covariance matrix. The prediction-based lower triangular transform (PLT) is the least complex of such transforms and is a component in the implementation of all of them. This paper proposes a minimum noise structure for PLT SVQ. Coding results for 16-dimensional LSF vectors from wideband speech show that GMM PLT SSVQ can achieve transparent quantization down to 41 bit/frame with distortion performance close to GMM KLT SSVQ at about three-fourths as much operational complexity. Other members of the GTD family, such as the geometric mean decomposition (GMD) transform and the bidiagonal (BID) transform, fail to capitalize on their advantageous features due to the low bit rate per component in the range tested.

关键词： Gaussian mixture models generalized triangular decomposition Karhunen-Loeve transform linear prediction line spectral frequencies prediction-based lower triangular transform switched split vector quantization

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