Automatic Parameter Tuning of Conventional Piecewise Linear Compression Algorithms and Developed Compression Algorithm

2012 ◽  
Vol 132 (5) ◽  
pp. 804-810
Author(s):  
Yoshikazu Ishii ◽  
Kazunobu Morita
Keyword(s):  
Piecewise Linear ◽  
Parameter Tuning ◽  
Compression Algorithm ◽  
Compression Algorithms ◽  
Automatic Parameter Tuning ◽  
Linear Compression

Development of Automatic Parameter Tuning for Train Automatic Stop Control Device

2016 ◽  
Vol 136 (9) ◽  
pp. 629-634
Author(s):  
Atsushi Oda ◽  
Ikuo Niimi ◽  
Kentaro Maki
Keyword(s):  
Parameter Tuning ◽  
Control Device ◽  
Automatic Parameter Tuning

scholarly journals The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

2018 ◽  
Vol 173 ◽  
pp. 03071
Author(s):  
Wu Wenbin ◽  
Yue Wu ◽  
Jintao Li
Keyword(s):  
Image Compression ◽  
Clustering Algorithm ◽  
Compression Algorithm ◽  
Prediction Algorithm ◽  
Two Dimensional ◽  
Spectral Image ◽  
Compression Algorithms ◽  
Serial Processing ◽  
Compression Time ◽  
Hyper Spectral

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

Automatic parameter tuning for Evolutionary Algorithms using a Bayesian Case-Based Reasoning system

2014 ◽  
Vol 18 ◽  
pp. 185-195 ◽  
Author(s):  
E. Yeguas ◽  
M.V. Luzón ◽  
R. Pavón ◽  
R. Laza ◽  
G. Arroyo ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Parameter Tuning ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
Automatic Parameter Tuning ◽  
Case Based

scholarly journals Low Complex Forward Adaptive Loss Compression Algorithm and Its Application in Speech Coding

2011 ◽  
Vol 62 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Jelena Nikolić ◽  
Zoran Perić ◽  
Dragan Antić ◽  
Aleksandra Jovanović ◽  
Dragan Denić
Keyword(s):  
Speech Coding ◽  
Piecewise Linear ◽  
Frame Analysis ◽  
Compression Algorithm ◽  
Quantization Noise ◽  
Practical Implementation ◽  
Time Varying ◽  
High Quality ◽  
Laplacian Distribution ◽  
Low Complex

Low Complex Forward Adaptive Loss Compression Algorithm and Its Application in Speech CodingThis paper proposes a low complex forward adaptive loss compression algorithm that works on the frame by frame basis. Particularly, the algorithm we propose performs frame by frame analysis of the input speech signal, estimates and quantizes the gain within the frames in order to enable the quantization by the forward adaptive piecewise linear optimal compandor. In comparison to the solution designed according to the G.711 standard, our algorithm provides not only higher level of the average signal to quantization noise ratio, but also performs a reduction of the PCM bit rate for about 1 bits/sample. Moreover, the algorithm we propose completely satisfies the G.712 standard, since it provides overreaching the curve defined by the G.712 standard in the whole of variance range. Accordingly, we can reasonably believe that our algorithm will find its practical implementation in the high quality coding of signals, represented with less than 8 bits/sample, which as well as speech signals follow Laplacian distribution and have the time varying variances.

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