81,228 research outputs found
Variable-resolution Compression of Vector Data
The compression of spatial data is a promising solution to reduce the space of data storage and to decrease the transmission time of spatial data over the Internet. This paper proposes a new method for variable-resolution compression of vector data. Three key steps are encompassed in the proposed method, namely, the simplification of vector data via the elimination of vertices, the compression of removed vertices, and the decoding of the compressed vector data. The proposed compression method was implemented and applied to compress vector data to investigate its performance in terms of the compression ratio, distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data, is able to achieve good compression ratios and maintains the main shape characteristics of the spatial objects within the compressed vector dat
Variable-resolution Compression of Vector Data
The compression of spatial data is a promising solution to reduce the space of data storage and to decrease the transmission time of spatial data over the Internet. This
paper proposes a new method for variable-resolution compression of vector data. Three key steps are encompassed in the proposed method, namely, the simplification of vector data via the elimination of vertices, the compression of removed vertices, and the decoding of the compressed vector data. The proposed compression method was implemented and applied to compress vector data to investigate its performance in terms of the compression ratio, distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data, is able to achieve good compression ratios and maintains the main shape characteristics of the spatial objects within the compressed vector data
Multiresolution vector quantization
Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes
High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression
Motivation: The high dimensionality of genomic data calls for the development
of specific classification methodologies, especially to prevent over-optimistic
predictions. This challenge can be tackled by compression and variable
selection, which combined constitute a powerful framework for classification,
as well as data visualization and interpretation. However, current proposed
combinations lead to instable and non convergent methods due to inappropriate
computational frameworks. We hereby propose a stable and convergent approach
for classification in high dimensional based on sparse Partial Least Squares
(sparse PLS). Results: We start by proposing a new solution for the sparse PLS
problem that is based on proximal operators for the case of univariate
responses. Then we develop an adaptive version of the sparse PLS for
classification, which combines iterative optimization of logistic regression
and sparse PLS to ensure convergence and stability. Our results are confirmed
on synthetic and experimental data. In particular we show how crucial
convergence and stability can be when cross-validation is involved for
calibration purposes. Using gene expression data we explore the prediction of
breast cancer relapse. We also propose a multicategorial version of our method
on the prediction of cell-types based on single-cell expression data.
Availability: Our approach is implemented in the plsgenomics R-package.Comment: 9 pages, 3 figures, 4 tables + Supplementary Materials 8 pages, 3
figures, 10 table
Multiresolution source coding using entropy constrained dithered scalar quantization
In this paper, we build multiresolution source codes using entropy constrained dithered scalar quantizers. We demonstrate that for n-dimensional random vectors, dithering followed by uniform scalar quantization and then by entropy coding achieves performance close to the n-dimensional optimum for a multiresolution source code. Based on this result, we propose a practical code design algorithm and compare its performance with that of the set partitioning in hierarchical trees (SPIHT) algorithm on natural images
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes
Multi-resolution VQ: parameter meaning and choice
In multi-resolution source coding, a single code is used to give an embedded data description that may be decoded at a variety of rates. Recent work in practical multi-resolution coding treats the optimal design of fixed- and variable-rate tree-structured vector quantizers for multi-resolution coding. In that work the codes are optimized for a designer-specified priority schedule over the system rates, distortions, or slopes. The method relies on a collection of parameters, which may be difficult to choose. This paper explores the meaning and choice of the multi-resolution source coding parameters
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