74,158 research outputs found
Lossless compression of image data products on th e FIFE CD-ROM series
How do you store enough of the key data sets, from a total of 120 gigabytes of data collected for a scientific experiment, on a collection of CD-ROM's, small enough to distribute to a broad scientific community? In such an application where information loss in unacceptable, lossless compression algorithms are the only choice. Although lossy compression algorithms can provide an order of magnitude improvement in compression ratios over lossless algorithms the information that is lost is often part of the key scientific precision of the data. Therefore, lossless compression algorithms are and will continue to be extremely important in minimizing archiving storage requirements and distribution of large earth and space (ESS) data sets while preserving the essential scientific precision of the data
Information-Preserving Markov Aggregation
We present a sufficient condition for a non-injective function of a Markov
chain to be a second-order Markov chain with the same entropy rate as the
original chain. This permits an information-preserving state space reduction by
merging states or, equivalently, lossless compression of a Markov source on a
sample-by-sample basis. The cardinality of the reduced state space is bounded
from below by the node degrees of the transition graph associated with the
original Markov chain.
We also present an algorithm listing all possible information-preserving
state space reductions, for a given transition graph. We illustrate our results
by applying the algorithm to a bi-gram letter model of an English text.Comment: 7 pages, 3 figures, 2 table
Optimal Compression of Floating-point Astronomical Images Without Significant Loss of Information
We describe a compression method for floating-point astronomical images that
gives compression ratios of 6 -- 10 while still preserving the scientifically
important information in the image. The pixel values are first preprocessed by
quantizing them into scaled integer intensity levels, which removes some of the
uncompressible noise in the image. The integers are then losslessly compressed
using the fast and efficient Rice algorithm and stored in a portable FITS
format file. Quantizing an image more coarsely gives greater image compression,
but it also increases the noise and degrades the precision of the photometric
and astrometric measurements in the quantized image. Dithering the pixel values
during the quantization process can greatly improve the precision of
measurements in the images. This is especially important if the analysis
algorithm relies on the mode or the median which would be similarly quantized
if the pixel values are not dithered. We perform a series of experiments on
both synthetic and real astronomical CCD images to quantitatively demonstrate
that the magnitudes and positions of stars in the quantized images can be
measured with the predicted amount of precision. In order to encourage wider
use of these image compression methods, we have made available a pair of
general-purpose image compression programs, called fpack and funpack, which can
be used to compress any FITS format image.Comment: Accepted PAS
A Compact Index for Order-Preserving Pattern Matching
Order-preserving pattern matching was introduced recently but it has already
attracted much attention. Given a reference sequence and a pattern, we want to
locate all substrings of the reference sequence whose elements have the same
relative order as the pattern elements. For this problem we consider the
offline version in which we build an index for the reference sequence so that
subsequent searches can be completed very efficiently. We propose a
space-efficient index that works well in practice despite its lack of good
worst-case time bounds. Our solution is based on the new approach of
decomposing the indexed sequence into an order component, containing ordering
information, and a delta component, containing information on the absolute
values. Experiments show that this approach is viable, faster than the
available alternatives, and it is the first one offering simultaneously small
space usage and fast retrieval.Comment: 16 pages. A preliminary version appeared in the Proc. IEEE Data
Compression Conference, DCC 2017, Snowbird, UT, USA, 201
A joint motion & disparity motion estimation technique for 3D integral video compression using evolutionary strategy
3D imaging techniques have the potential to establish a future mass-market in the fields of entertainment and communications. Integral imaging, which can capture true 3D color images with only one camera, has been seen as the right technology to offer stress-free viewing to audiences of more than one person. Just like any digital video, 3D video sequences must also be compressed in order to make it suitable for consumer domain applications. However, ordinary compression techniques found in state-of-the-art video coding standards such as H.264, MPEG-4 and MPEG-2 are not capable of producing enough compression while preserving the 3D clues. Fortunately, a huge amount of redundancies can be found in an integral video sequence in terms of motion and disparity. This paper discusses a novel approach to use both motion and disparity information to compress 3D integral video sequences. We propose to decompose the integral video sequence down to viewpoint video sequences and jointly exploit motion and disparity redundancies to maximize the compression. We further propose an optimization technique based on evolutionary strategies to minimize the computational complexity of the joint motion disparity estimation. Experimental results demonstrate that Joint Motion and Disparity Estimation can achieve over 1 dB objective quality gain over normal motion estimation. Once combined with Evolutionary strategy, this can achieve up to 94% computational cost saving
On the Use of Compressed Polyhedral Quadrature Formulas in Embedded Interface Methods
The main idea of this paper is to apply a recent quadrature compression technique to algebraic quadrature formulas on complex polyhedra. The quadrature compression substantially reduces the number of integration points but preserves the accuracy of integration. The compression is easy to achieve since it is entirely based on the fundamental methods of numerical linear algebra. The resulting compressed formulas are applied in an embedded interface method to integrate the weak form of the Navier--Stokes equations. Simulations of flow past stationary and moving interface problems demonstrate that the compressed quadratures improve the efficiency of performing the weak form integration, while preserving accuracy and order of convergence
Compression Bases in Unital Groups
We study unital groups with a distinguished family of compressions called a
compression base. A motivating example is the partially ordered additive group
of a von Neumann algebra with all Naimark compressions as the compression base.Comment: 8 page
Using wavelets for compression and detecting events in anomalous network traffic
Monitoring and measuring various metrics of highdata
rate networks produces a vast amount of information over
a long period of time making the storage of the monitored data
a serious issue. Furthermore, for the collected monitoring data
to be useful to network analysts, these measurements need to be
processed in order to detect interesting characteristics.
In this paper wavelet analysis is used as a multi-resolution
analysis tool for compression of data rate measurements. Two
known thresholds are suggested for lossy compression and event
detection purposes. Results show high compression ratios while
preserving the quality (quantitative and visual aspects) and
the energy of the signal and detection of sudden changes are
achievable
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