Strategies of SIMD Computing for Image Coding in GPU

Pablo Enfedaque, Francesc Auli-Llinas, Juan C. Moure

Research output: Contribution to journalLiterature reviewResearchpeer-review

1 Citation (Scopus)


The main difficulty to implement modern image coding systems in a GPU is that the algorithms employed in the core of the coding scheme are inherently sequential. We recently proposed bitplane image coding with parallel coefficient processing (BPC-PaCo), a coding scheme that, contrarily to most systems, permits the processing of multiple coefficients of the image in parallel. This enables the use of SIMD computing, ideal for its implementation in a GPU. This paper introduces and evaluates the GPU implementation of BPC-PaCo employing two different strategies that tradeoff computational throughput and compression efficiency. The proposed implementation is compared to the best CPU and GPU implementations of JPEG2000, the state-of-The-Art image compression standard. Experimental results indicate that BPC-PaCo achieves a computational throughput that is an order of magnitude superior to that achieved with such implementations with a small reduction in coding efficiency.

Original languageAmerican English
Pages (from-to)345-354
Number of pages10
JournalProceedings - 22nd IEEE International Conference on High Performance Computing, HiPC 2015
Publication statusPublished - 2 Feb 2016


  • GPU
  • Image Coding
  • Parallel Architectures
  • SIMD computing

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