51,474 research outputs found
3D oil reservoir visualisation using octree compression techniques utilising logical grid co-ordinates
Octree compression techniques have been used for several years for compressing large three dimensional
data sets into homogeneous regions. This compression technique is ideally suited to datasets
which have similar values in clusters. Oil engineers represent reservoirs as a three dimensional grid
where hydrocarbons occur naturally in clusters. This research looks at the efficiency of storing these
grids using octree compression techniques where grid cells are broken into active and inactive regions.
Initial experiments yielded high compression ratios as only active leaf nodes and their ancestor, header
nodes are stored as a bitstream to file on disk. Savings in computational time and memory were possible
at decompression, as only active leaf nodes are sent to the graphics card eliminating the need of
reconstructing the original matrix. This results in a more compact vertex table, which can be loaded
into the graphics card quicker and generating shorter refresh delay times
On the Use of Suffix Arrays for Memory-Efficient Lempel-Ziv Data Compression
Much research has been devoted to optimizing algorithms of the Lempel-Ziv
(LZ) 77 family, both in terms of speed and memory requirements. Binary search
trees and suffix trees (ST) are data structures that have been often used for
this purpose, as they allow fast searches at the expense of memory usage.
In recent years, there has been interest on suffix arrays (SA), due to their
simplicity and low memory requirements. One key issue is that an SA can solve
the sub-string problem almost as efficiently as an ST, using less memory. This
paper proposes two new SA-based algorithms for LZ encoding, which require no
modifications on the decoder side. Experimental results on standard benchmarks
show that our algorithms, though not faster, use 3 to 5 times less memory than
the ST counterparts. Another important feature of our SA-based algorithms is
that the amount of memory is independent of the text to search, thus the memory
that has to be allocated can be defined a priori. These features of low and
predictable memory requirements are of the utmost importance in several
scenarios, such as embedded systems, where memory is at a premium and speed is
not critical. Finally, we point out that the new algorithms are general, in the
sense that they are adequate for applications other than LZ compression, such
as text retrieval and forward/backward sub-string search.Comment: 10 pages, submited to IEEE - Data Compression Conference 200
New Algorithms and Lower Bounds for Sequential-Access Data Compression
This thesis concerns sequential-access data compression, i.e., by algorithms
that read the input one or more times from beginning to end. In one chapter we
consider adaptive prefix coding, for which we must read the input character by
character, outputting each character's self-delimiting codeword before reading
the next one. We show how to encode and decode each character in constant
worst-case time while producing an encoding whose length is worst-case optimal.
In another chapter we consider one-pass compression with memory bounded in
terms of the alphabet size and context length, and prove a nearly tight
tradeoff between the amount of memory we can use and the quality of the
compression we can achieve. In a third chapter we consider compression in the
read/write streams model, which allows us passes and memory both
polylogarithmic in the size of the input. We first show how to achieve
universal compression using only one pass over one stream. We then show that
one stream is not sufficient for achieving good grammar-based compression.
Finally, we show that two streams are necessary and sufficient for achieving
entropy-only bounds.Comment: draft of PhD thesi
Recommended from our members
Parallel data compression
Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested
PDF/A-3u as an archival format for Accessible mathematics
Including LaTeX source of mathematical expressions, within the PDF document
of a text-book or research paper, has definite benefits regarding
`Accessibility' considerations. Here we describe three ways in which this can
be done, fully compatibly with international standards ISO 32000, ISO 19005-3,
and the forthcoming ISO 32000-2 (PDF 2.0). Two methods use embedded files, also
known as `attachments', holding information in either LaTeX or MathML formats,
but use different PDF structures to relate these attachments to regions of the
document window. One uses structure, so is applicable to a fully `Tagged PDF'
context, while the other uses /AF tagging of the relevant content. The third
method requires no tagging at all, instead including the source coding as the
/ActualText replacement of a so-called `fake space'. Information provided this
way is extracted via simple Select/Copy/Paste actions, and is available to
existing screen-reading software and assistive technologies.Comment: This is a post-print version of original in volume: S.M. Watt et al.
(Eds.): CICM 2014, LNAI 8543, pp.184-199, 2014; available at
http://link.springer.com/search?query=LNAI+8543, along with supplementary
PDF. This version, with supplement as attachment, is enriched to validate as
PDF/A-3u modulo an error in white-space handling in the pdfTeX version used
to generate i
Holographic and 3D teleconferencing and visualization: implications for terabit networked applications
Abstract not available
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