scholarly journals Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression

2019 ◽  
pp. 23-30
Author(s):  
A I Maksimov ◽  
M V Gashnikov
Keyword(s):  
Dimension Reduction ◽  
Parameter Space ◽  
Compression Ratio ◽  
Space Dimension ◽  
Computational Experiments ◽  
Parameter Range ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

We propose a new adaptive multidimensional signal interpolator for differential compression tasks. To increase the efficiency of interpolation, we optimize its parameters space by the minimum absolute interpolation error criterion. To reduce the complexity of interpolation optimization, we reduce the dimension of its parameter range. The correspondence between signal samples in a local neighbourhood is parameterized. Besides, we compare several methods for such parameterization. The developed adaptive interpolator is embedded in the differential compression method. Computational experiments on real multidimensional signals confirm that the use of the proposed interpolator can increase the compression ratio.

scholarly journals Adaptive interpolation of multidimensional signals for compression on board an aircraft

2019 ◽  
pp. 97-102
Author(s):  
N I Glumov ◽  
M V Gashnikov
Keyword(s):  
Compression Ratio ◽  
Real World ◽  
Adaptive Algorithm ◽  
Computational Experiments ◽  
Interpolation Algorithm ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Hierarchical Compression ◽  
Adaptive Interpolator

We consider the compression of multidimensional signals on the aircraft board. We describe the data of such signals as a hypercube, which is "rotated" in a special way. To compress this hypercube, we use a hierarchical compression method. As one of the stages of this method, we use an adaptive interpolation algorithm. The adaptive algorithm automatically switches between different interpolating functions at each signal point. We perform computational experiments in real-world multidimensional signals. Computational experiments confirm that the use of proposed adaptive interpolator allows increasing (up to 31%) the compression ratio of the “rotated” hypercube corresponding to multidimensional hyperspectral signals.

scholarly journals Adaptive interpolation based on optimization of the decision rule in a multidimensional feature space

2020 ◽  
Vol 44 (1) ◽  
pp. 101-108
Author(s):  
M.V. Gashnikov
Keyword(s):  
Decision Tree ◽  
Decision Rule ◽  
Feature Space ◽  
Computational Experiments ◽  
Arbitrary Form ◽  
Multidimensional Signals ◽  
Adaptive Interpolation ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

An adaptive multidimensional signal interpolator is proposed, which selects an interpolating function at each signal point by means of the decision rule optimized in a multidimensional feature space using a decision tree. The search for the dividing boundary when splitting the decision tree vertices is carried out by a recurrence procedure that allows, in addition to the search for the boundary, selecting the best pair of interpolating functions from a predetermined set of functions of an arbitrary form. Results of computational experiments in nature multidimensional signals are presented, confirming the effectiveness of the adaptive interpolator.

scholarly journals Interpolation of multidimensional signals using the reduction of the dimension of parametric spaces of decision rules

2019 ◽  
pp. 31-40
Author(s):  
M V Gashnikov
Keyword(s):  
Decision Rule ◽  
Optimization Problem ◽  
Decision Rules ◽  
Compression Method ◽  
Parametric Space ◽  
Archive File ◽  
Multidimensional Signals ◽  
Reduced Dimension ◽  
Hierarchical Compression ◽  
Adaptive Interpolator

In this paper, we consider the interpolation of multidimensional signals problem. We develop adaptive interpolators that select the most appropriate interpolating function at each signal point. Parameterized decision rule selects the interpolating function based on local features at each signal point. We optimize the adaptive interpolator in the parameter space of this decision rule. For solving this optimization problem, we reduce the dimension of the parametric space of the decision rule. Dimension reduction is based on the parameterization of the ratio between local differences at each signal point. Then we optimize the adaptive interpolator in parametric space of reduced dimension. Computational experiments to investigate the effectiveness of an adaptive interpolator are conducted using real-world multidimensional signals. The proposed adaptive interpolator used as a part of the hierarchical compression method showed a gain of up to 51% in the size of the archive file compared to the smoothing interpolator.

scholarly journals Interpolation based on context modeling for hierarchical compression of multidimensional signals

2018 ◽  
Vol 42 (3) ◽  
pp. 468-475 ◽  
Author(s):  
M. V. Gashnikov
Keyword(s):  
Arbitrary Dimension ◽  
Computational Experiments ◽  
Context Modeling ◽  
Interpolation Algorithm ◽  
Optimal Parameters ◽  
Compression Method ◽  
Local Neighborhood ◽  
Multidimensional Signals ◽  
Hierarchical Compression

Context algorithms for interpolation of multidimensional signals in the compression problem are researched. A hierarchical compression method for arbitrary dimension signals is considered. For this method, an interpolation algorithm based on the context modeling is proposed. The algorithm is based on optimizing parameters of the interpolating function in a local neighborhood of the interpolated sample. At the same time, locally optimal parameters found for more decimated scale signal levels are used to interpolate samples of less decimated scale signal levels. The context interpolation algorithm is implemented programmatically as part of a hierarchical compression method. Computational experiments have shown that using a context interpolator instead of an average interpolator makes it possible to significantly improve the efficiency of hierarchical compression.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

TWO NEURON CNNS: SEARCH FOR LIMIT CYCLES

2010 ◽  
Vol 20 (04) ◽  
pp. 1137-1173 ◽  
Author(s):  
XAVIER VILASÍS-CARDONA ◽  
MIREIA VINYOLES-SERRA
Keyword(s):  
Parameter Space ◽  
Limit Cycles ◽  
Continuous Time ◽  
Sufficient Conditions ◽  
Parameter Range ◽  
Different Types

In this paper, we show sufficient conditions for the existence of limit cycles in the general continuous time two-neuron autonomous CNN. We find that different types of limit cycles correspond to different regions in the template parameter space. Actually, we are able to predict the CNN behavior from the template values for the full parameter range, except for two small bounded regions.

scholarly journals Optimization of the multidimensional signal interpolator in a lower dimensional space

2019 ◽  
Vol 43 (4) ◽  
pp. 653-660 ◽  
Author(s):  
M.V. Gashnikov
Keyword(s):  
Experimental Study ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Signal Sample ◽  
Lower Dimensional Space ◽  
Lower Dimensional ◽  
Selection Of

Adaptive multidimensional signal interpolators are developed. These interpolators take into account the presence and direction of boundaries of flat signal regions in each local neighborhood based on the automatic selection of the interpolating function for each signal sample. The selection of the interpolating function is performed by a parameterized rule, which is optimized in a parametric lower dimensional space. The dimension reduction is performed using rank filtering of local differences in the neighborhood of each signal sample. The interpolating functions of adaptive interpolators are written for the multidimensional, three-dimensional and two-dimensional cases. The use of adaptive interpolators in the problem of compression of multidimensional signals is also considered. Results of an experimental study of adaptive interpolators for real multidimensional signals of various types are presented.

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