test
server time: root: http://itiis.org
current_path: /journals/tiis/digital-library/manuscript/1747
current_url: http://itiis.org/journals/tiis/digital-library/manuscript/1747
Parallel Implementation of the Recursive Least Square for Hyperspectral Image Compression on GPUs
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Parallel Implementation of the Recursive Least Square for Hyperspectral Image Compression on GPUs


Abstract

Compression is a very important technique for remotely sensed hyperspectral images. The lossless compression based on the recursive least square (RLS), which eliminates hyperspectral images’ redundancy using both spatial and spectral correlations, is an extremely powerful tool for this purpose, but the relatively high computational complexity limits its application to time-critical scenarios. In order to improve the computational efficiency of the algorithm, we optimize its serial version and develop a new parallel implementation on graphics processing units (GPUs). Namely, an optimized recursive least square based on optimal number of prediction bands is introduced firstly. Then we use this approach as a case study to illustrate the advantages and potential challenges of applying GPU parallel optimization principles to the considered problem. The proposed parallel method properly exploits the low-level architecture of GPUs and has been carried out using the compute unified device architecture (CUDA). The GPU parallel implementation is compared with the serial implementation on CPU. Experimental results indicate remarkable acceleration factors and real-time performance, while retaining exactly the same bit rate with regard to the serial version of the compressor.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
C. Li, "Parallel Implementation of the Recursive Least Square for Hyperspectral Image Compression on GPUs," KSII Transactions on Internet and Information Systems, vol. 11, no. 7, pp. 3543-3557, 2017. DOI: 10.3837/tiis.2017.07.013.

[ACM Style]
Changguo Li. 2017. Parallel Implementation of the Recursive Least Square for Hyperspectral Image Compression on GPUs. KSII Transactions on Internet and Information Systems, 11, 7, (2017), 3543-3557. DOI: 10.3837/tiis.2017.07.013.