A JBIG compliant, quadtree based, lossless image compression algorithm is described. In terms of the number of arithmetic coding operations required to code an image, this algorithm is significantly faster than previous JBIG algorithm variations. Based on this criterion, our algorithm achieves an average speed increase of more than 9 times with only a 5% decrease in compression when tested on the eight CCITT bi-level test images and compared against the basic non-progressive JBIG algorithm. The fastest JBIG variation that we know of, using "PRES" resolution reduction and progressive buildup, achieved an average speed increase of less than 6 times with a 7% decrease in compression, under the same conditions.
This paper presents an analytical and computational framework for the compression of particle data resulting from hierarchical approximate treecodes such as the Barnes-Hut and Fast Multipole Methods. Due to the approximations introduced by hierarchical methods, the position (as well as velocity and acceleration) of a particle can be bounded by a distortion radius. We develop storage schemes that maintain this distortion radii while maximizing compression. Our schemes make extensive use of spatial and temporal coherence of particle behavior and yield compression ratios higher than 12:1 over raw data, and 6:1 over gzipped (LZ78) raw data. We demonstrate that for uniform distributions with 100K particles, storage requirements can be reduced from 1200KB (100K × 12B) to about 99KB (under 1 byte per particle per timestep). This is significant because it enables faster storage/retrieval, better temporal resolution, and improved analysis. Our results are shown to scale from small systems (2K particles) to much larger systems (over 100K particles). The associated algorithm is optimal (O(n)) in both storage and computation with small constants.
This paper presents an analytical and computational framework for the compression of particle data resulting from hierarchical approximate treecodes such as the Barnes-Hut and Fast Multipole Methods. Due to the approximations introduced by hierarchical methods, the position (as well as velocity and acceleration) of a particle can be bounded by a distortion radius. We develop storage schemes that maintain this distortion radii while maximizing compression. Our schemes make extensive use of spatial and temporal coherence of particle behavior and yield compression ratios higher than 12:1 over raw data, and 6:1 over gzipped (LZ78) raw data. We demonstrate that for uniform distributions with 100K particles, storage requirements can be reduced from 1200KB (100K × 12B) to about 99KB (under 1 byte per particle per timestep). This is significant because it enables faster storage/retrieval, better temporal resolution, and improved analysis. Our results are shown to scale from small systems (2K particles) to much larger systems (over 100K particles). The associated algorithm is optimal (O(n)) in both storage and computation with small constants.
This article presents an analytical and computational framework for the compression of particle data resulting from hierarchical approximate treecodes such as the Barnes-Hut and Fast Multipole Methods. Due to approximations introduced by hierarchical methods, various parameters (such as position, velocity, acceleration, potential) associated with a particle can be bounded by distortion radii. Using this distortion radii, we develop storage schemes that guarantee error bounds while maximizing compression. Our schemes make extensive use of spatial and temporal coherence of particle behavior and yield compression ratios higher than 12:1 over raw data, and 6:1 over gzipped (LZ) raw data for selected simulation instances. We demonstrate that for uniform distributions with 2M particles, storage requirements can be reduced from 24 MB to about 1.8 MB (about 7 bits per particle per timestep) for storing particle positions. This is significant because it enables faster storage/retrieval, better temporal resolution, and improved analysis. Our results are shown to scale from small systems (2K particles) to much larger systems (over 2M particles). The associated A. Grama's work was supported in part by National Science Foundation (NSF) grants EIA-9806741, ACI-9875899, and ACI-9872101. V. Sarin's work was supported in part by NSF grant CCR-9972533 Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this worked owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 1515 Broadway, New York, NY 10036 USA, fax +1 (212) algorithm is asymptotically optimal in computation time (O(n)) with a small constant. Our implementations are demonstrated to run extremely fast-much faster than the time it takes to compute a single time-step advance. In addition, our compression framework relies on a natural hierarchical representation upon which other analysis tasks such as segmented and window retrieval can be built.
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