Fluid data compression and ROI detection using run length method

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

研究成果: ジャーナルへの寄稿会議記事査読


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.

ジャーナルProcedia Computer Science
出版ステータス出版済み - 2014
イベントInternational Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014 - Gdynia, ポーランド
継続期間: 9月 15 20149月 17 2014

!!!All Science Journal Classification (ASJC) codes

  • コンピュータサイエンス一般


「Fluid data compression and ROI detection using run length method」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。