Fluid data compression and ROI detection using run length method

Shota Ishikawa, Haiyuan Wu, Chongke Bi, Qian Chen, Hirokazu Taki, Kenji Ono

Research output: Contribution to journalConference article

Abstract

It is difficult to carry out visualization of the large-scale time-varying data directly, even with the supercomputers. Data compression and ROI (Region of Interest) detection are often used to improve efficiency of the visualization of numerical data. It is well known that the Run Length encoding is a good technique to compress the data where the same sequence appeared repeatedly, such as an image with little change, or a set of smooth fluid data. Another advantage of Run Length encoding is that it can be applied to every dimension of data separately. Therefore, the Run Length method can be implemented easily as a parallel processing algorithm. We proposed two different Run Length based methods. When using the Run Length method to compress a data set, its size may increase after the compression if the data does not contain many repeated parts. We only apply the compression for the case that the data can be compressed effectively. By checking the compression ratio, we can detect ROI. The effectiveness and efficiency of the proposed methods are demonstrated through comparing with several existing compression methods using different sets of fluid data.

Original languageEnglish
Pages (from-to)1284-1291
Number of pages8
JournalProcedia Computer Science
Volume35
Issue numberC
DOIs
Publication statusPublished - Jan 1 2014
EventInternational Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014 - Gdynia, Poland
Duration: Sep 15 2014Sep 17 2014

Fingerprint

Data compression
Visualization
Fluids
Supercomputers
Compaction
Processing

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Fluid data compression and ROI detection using run length method. / Ishikawa, Shota; Wu, Haiyuan; Bi, Chongke; Chen, Qian; Taki, Hirokazu; Ono, Kenji.

In: Procedia Computer Science, Vol. 35, No. C, 01.01.2014, p. 1284-1291.

Research output: Contribution to journalConference article

Ishikawa, Shota ; Wu, Haiyuan ; Bi, Chongke ; Chen, Qian ; Taki, Hirokazu ; Ono, Kenji. / Fluid data compression and ROI detection using run length method. In: Procedia Computer Science. 2014 ; Vol. 35, No. C. pp. 1284-1291.
@article{4be41d3d97ae44c09edf5f228e6ce8f2,
title = "Fluid data compression and ROI detection using run length method",
abstract = "It is difficult to carry out visualization of the large-scale time-varying data directly, even with the supercomputers. Data compression and ROI (Region of Interest) detection are often used to improve efficiency of the visualization of numerical data. It is well known that the Run Length encoding is a good technique to compress the data where the same sequence appeared repeatedly, such as an image with little change, or a set of smooth fluid data. Another advantage of Run Length encoding is that it can be applied to every dimension of data separately. Therefore, the Run Length method can be implemented easily as a parallel processing algorithm. We proposed two different Run Length based methods. When using the Run Length method to compress a data set, its size may increase after the compression if the data does not contain many repeated parts. We only apply the compression for the case that the data can be compressed effectively. By checking the compression ratio, we can detect ROI. The effectiveness and efficiency of the proposed methods are demonstrated through comparing with several existing compression methods using different sets of fluid data.",
author = "Shota Ishikawa and Haiyuan Wu and Chongke Bi and Qian Chen and Hirokazu Taki and Kenji Ono",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.procs.2014.08.228",
language = "English",
volume = "35",
pages = "1284--1291",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",
number = "C",

}

TY - JOUR

T1 - Fluid data compression and ROI detection using run length method

AU - Ishikawa, Shota

AU - Wu, Haiyuan

AU - Bi, Chongke

AU - Chen, Qian

AU - Taki, Hirokazu

AU - Ono, Kenji

PY - 2014/1/1

Y1 - 2014/1/1

N2 - It is difficult to carry out visualization of the large-scale time-varying data directly, even with the supercomputers. Data compression and ROI (Region of Interest) detection are often used to improve efficiency of the visualization of numerical data. It is well known that the Run Length encoding is a good technique to compress the data where the same sequence appeared repeatedly, such as an image with little change, or a set of smooth fluid data. Another advantage of Run Length encoding is that it can be applied to every dimension of data separately. Therefore, the Run Length method can be implemented easily as a parallel processing algorithm. We proposed two different Run Length based methods. When using the Run Length method to compress a data set, its size may increase after the compression if the data does not contain many repeated parts. We only apply the compression for the case that the data can be compressed effectively. By checking the compression ratio, we can detect ROI. The effectiveness and efficiency of the proposed methods are demonstrated through comparing with several existing compression methods using different sets of fluid data.

AB - It is difficult to carry out visualization of the large-scale time-varying data directly, even with the supercomputers. Data compression and ROI (Region of Interest) detection are often used to improve efficiency of the visualization of numerical data. It is well known that the Run Length encoding is a good technique to compress the data where the same sequence appeared repeatedly, such as an image with little change, or a set of smooth fluid data. Another advantage of Run Length encoding is that it can be applied to every dimension of data separately. Therefore, the Run Length method can be implemented easily as a parallel processing algorithm. We proposed two different Run Length based methods. When using the Run Length method to compress a data set, its size may increase after the compression if the data does not contain many repeated parts. We only apply the compression for the case that the data can be compressed effectively. By checking the compression ratio, we can detect ROI. The effectiveness and efficiency of the proposed methods are demonstrated through comparing with several existing compression methods using different sets of fluid data.

UR - http://www.scopus.com/inward/record.url?scp=84924122145&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924122145&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2014.08.228

DO - 10.1016/j.procs.2014.08.228

M3 - Conference article

AN - SCOPUS:84924122145

VL - 35

SP - 1284

EP - 1291

JO - Procedia Computer Science

JF - Procedia Computer Science

SN - 1877-0509

IS - C

ER -