Abstract
The amount of data generated by ultraspectral sounders is so large that considerable savings in data storage and transmission bandwidth can be achieved using data compression. Due to this large amount of data, the data compression time is of utmost importance. Increasing the programmability of the commodity Graphics Processing Units (GPUs) offer potential for considerable increases in computation speeds in applications that are data parallel. In our experiments, we implemented a spectral image data compression method called Linear Prediction with Constant Coefficients (LP-CC) using NVIDIA's CUDA parallel computing architecture. LP-CC compression method represents a current state-of-the-art technique in lossless compression of ultraspectral sounder data. The method showed an average compression ratio of 3.39 when applied to publicly available NASA AIRS data. We achieved a speed-up of 86 compared to a single threaded CPU version. Thus, the commodity GPU was able to significantly decrease the computational time of a compression algorithm based on a constant coefficient linear prediction.
| Original language | English |
|---|---|
| Article number | 041774 |
| Journal | Journal of Applied Remote Sensing |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2010 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)
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Constant coefficients linear prediction for lossless compression of ultraspectral sounder data using a graphics processing unit. / Mielikäinen, Jarno; Honkanen, Risto; Huang, Bormin; Toivanen, Pekka J.; Lee, Chulhee.
In: Journal of Applied Remote Sensing, Vol. 4, No. 1, 041774, 01.12.2010.Research output: Contribution to journal › Article
TY - JOUR
T1 - Constant coefficients linear prediction for lossless compression of ultraspectral sounder data using a graphics processing unit
AU - Mielikäinen, Jarno
AU - Honkanen, Risto
AU - Huang, Bormin
AU - Toivanen, Pekka J.
AU - Lee, Chulhee
PY - 2010/12/1
Y1 - 2010/12/1
N2 - The amount of data generated by ultraspectral sounders is so large that considerable savings in data storage and transmission bandwidth can be achieved using data compression. Due to this large amount of data, the data compression time is of utmost importance. Increasing the programmability of the commodity Graphics Processing Units (GPUs) offer potential for considerable increases in computation speeds in applications that are data parallel. In our experiments, we implemented a spectral image data compression method called Linear Prediction with Constant Coefficients (LP-CC) using NVIDIA's CUDA parallel computing architecture. LP-CC compression method represents a current state-of-the-art technique in lossless compression of ultraspectral sounder data. The method showed an average compression ratio of 3.39 when applied to publicly available NASA AIRS data. We achieved a speed-up of 86 compared to a single threaded CPU version. Thus, the commodity GPU was able to significantly decrease the computational time of a compression algorithm based on a constant coefficient linear prediction.
AB - The amount of data generated by ultraspectral sounders is so large that considerable savings in data storage and transmission bandwidth can be achieved using data compression. Due to this large amount of data, the data compression time is of utmost importance. Increasing the programmability of the commodity Graphics Processing Units (GPUs) offer potential for considerable increases in computation speeds in applications that are data parallel. In our experiments, we implemented a spectral image data compression method called Linear Prediction with Constant Coefficients (LP-CC) using NVIDIA's CUDA parallel computing architecture. LP-CC compression method represents a current state-of-the-art technique in lossless compression of ultraspectral sounder data. The method showed an average compression ratio of 3.39 when applied to publicly available NASA AIRS data. We achieved a speed-up of 86 compared to a single threaded CPU version. Thus, the commodity GPU was able to significantly decrease the computational time of a compression algorithm based on a constant coefficient linear prediction.
UR - http://www.scopus.com/inward/record.url?scp=80052313236&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052313236&partnerID=8YFLogxK
U2 - 10.1117/1.3496907
DO - 10.1117/1.3496907
M3 - Article
VL - 4
JO - Journal of Applied Remote Sensing
JF - Journal of Applied Remote Sensing
SN - 1931-3195
IS - 1
M1 - 041774
ER -