Complexity-Configurable Learning-based Genome Compression

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2021 Picture Coding Symposium (PCS)
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages241-245
ISBN (Electronic)978-1-6654-2545-2
ISBN (Print)978-1-6654-3078-4
Publication statusPublished - 2021

Publication series

Name
ISSN (Print)2330-7935
ISSN (Electronic)2472-7822

Conference

Title2021 Picture Coding Symposium (PCS 2021)
LocationVirtual
PlaceUnited Kingdom
CityBristol
Period29 June - 2 July 2021

Abstract

In this paper, we propose the complexity configurable learning-based genome data compression method, in an effort to achieve a good balance between coding complexity and performance in lossless DNA compression. In particular, we first introduce the concept of Group of Bases (GoB), which serves as the foundation and enables the parallel implementation of the learning-based genome data compression. Subsequently, the Markov model is introduced for modeling the initial content, and the learning-based inference is achieved for the remaining base data. The compression is finally achieved with efficient arithmetic coding, and based upon a set of configurations on compression ratios and inference speed, the proposed method is shown to be more efficient and provide more flexibility in real-world applications.

Research Area(s)

  • Genome compression, Markov model, Parallel implementation, Deep learning

Citation Format(s)

Complexity-Configurable Learning-based Genome Compression. / Sun, Zhenhao; Wang, Meng; Wang, Shiqi; Kwong, Sam.

2021 Picture Coding Symposium (PCS): Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 241-245 9477487.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review