scholarly journals Application of Multi-Wavelet Analysis in Image Compression

2016 ◽  
Keyword(s):  
Wavelet Analysis ◽  
Image Compression

Image Compression Based on Wavelet Transform

2014 ◽  
Vol 568-570 ◽  
pp. 749-752
Author(s):  
Chang Li Yan ◽  
Quan Cai Deng ◽  
Xin Zhang
Keyword(s):  
Image Processing ◽  
Wavelet Analysis ◽  
Image Compression ◽  
Image Coding ◽  
Two Dimensional ◽  
Wavelet Coefficients ◽  
Wavelet Theory ◽  
Two Dimensional Image ◽  
Ezw Algorithm ◽  
Selection Of

The wavelet analysis has some important applications in image processing, including image compression, image de-noising and so on.Wavelet analysis for two-dimensional image compression is a key aspect in the field of its applications.This paper studied the application of wavelet analysis in BMP image coding, the characteristics of wavelet coefficients and wavelet subimage, these lay the foundation for further selection of wavelet coefficients and optimize, and analyzed the theory of EZW algorithm, illustrates the better results of the applications on using wavelet theory in image processing.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

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