8,880 research outputs found
Map online system using internet-based image catalogue
Digital maps carry along its geodata information such as coordinate that is important in one particular topographic and thematic map. These geodatas are meaningful especially in military field. Since the maps carry along this information, its makes the size of the images is too big. The bigger size, the bigger storage is required to allocate the image file. It also can cause longer loading time. These conditions make it did not suitable to be applied in image catalogue approach via internet environment. With compression techniques, the image size can be reduced and the quality of the image is still guaranteed without much changes. This report is paying attention to one of the image compression technique using wavelet technology. Wavelet technology is much batter than any other image compression technique nowadays. As a result, the compressed images applied to a system called Map Online that used Internet-based Image Catalogue approach. This system allowed user to buy map online. User also can download the maps that had been bought besides using the searching the map. Map searching is based on several meaningful keywords. As a result, this system is expected to be used by Jabatan Ukur dan Pemetaan Malaysia (JUPEM) in order to make the organization vision is implemented
A Generative Model of Natural Texture Surrogates
Natural images can be viewed as patchworks of different textures, where the
local image statistics is roughly stationary within a small neighborhood but
otherwise varies from region to region. In order to model this variability, we
first applied the parametric texture algorithm of Portilla and Simoncelli to
image patches of 64X64 pixels in a large database of natural images such that
each image patch is then described by 655 texture parameters which specify
certain statistics, such as variances and covariances of wavelet coefficients
or coefficient magnitudes within that patch.
To model the statistics of these texture parameters, we then developed
suitable nonlinear transformations of the parameters that allowed us to fit
their joint statistics with a multivariate Gaussian distribution. We find that
the first 200 principal components contain more than 99% of the variance and
are sufficient to generate textures that are perceptually extremely close to
those generated with all 655 components. We demonstrate the usefulness of the
model in several ways: (1) We sample ensembles of texture patches that can be
directly compared to samples of patches from the natural image database and can
to a high degree reproduce their perceptual appearance. (2) We further
developed an image compression algorithm which generates surprisingly accurate
images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how
our approach can be used for an efficient and objective evaluation of samples
generated with probabilistic models of natural images.Comment: 34 pages, 9 figure
Pairwise Quantization
We consider the task of lossy compression of high-dimensional vectors through
quantization. We propose the approach that learns quantization parameters by
minimizing the distortion of scalar products and squared distances between
pairs of points. This is in contrast to previous works that obtain these
parameters through the minimization of the reconstruction error of individual
points. The proposed approach proceeds by finding a linear transformation of
the data that effectively reduces the minimization of the pairwise distortions
to the minimization of individual reconstruction errors. After such
transformation, any of the previously-proposed quantization approaches can be
used. Despite the simplicity of this transformation, the experiments
demonstrate that it achieves considerable reduction of the pairwise distortions
compared to applying quantization directly to the untransformed data
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
With ever-increasing volumes of scientific data produced by HPC applications,
significantly reducing data size is critical because of limited capacity of
storage space and potential bottlenecks on I/O or networks in writing/reading
or transferring data. SZ and ZFP are the two leading lossy compressors
available to compress scientific data sets. However, their performance is not
consistent across different data sets and across different fields of some data
sets: for some fields SZ provides better compression performance, while other
fields are better compressed with ZFP. This situation raises the need for an
automatic online (during compression) selection between SZ and ZFP, with a
minimal overhead. In this paper, the automatic selection optimizes the
rate-distortion, an important statistical quality metric based on the
signal-to-noise ratio. To optimize for rate-distortion, we investigate the
principles of SZ and ZFP. We then propose an efficient online, low-overhead
selection algorithm that predicts the compression quality accurately for two
compressors in early processing stages and selects the best-fit compressor for
each data field. We implement the selection algorithm into an open-source
library, and we evaluate the effectiveness of our proposed solution against
plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results
on three data sets representing about 100 fields show that our selection
algorithm improves the compression ratio up to 70% with the same level of data
distortion because of very accurate selection (around 99%) of the best-fit
compressor, with little overhead (less than 7% in the experiments).Comment: 14 pages, 9 figures, first revisio
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