Color Image Compression Using Block Singular Value Decomposition

2013 ◽  
Vol 303-306 ◽  
pp. 2122-2125
Author(s):  
Peng Fei Xu ◽  
Hong Bin Zhang ◽  
Xin Feng Wang ◽  
Zheng Yong Yu
Keyword(s):  
Singular Value Decomposition ◽  
Image Compression ◽  
Basic Principle ◽  
Color Image ◽  
Singular Value ◽  
Matrix Structure ◽  
Compression Scheme ◽  
The Matrix ◽  
Value Decomposition ◽  
Color Image Compression

This paper looks at the application of Singular Value Decomposition (SVD) to color image compression. Based on the basic principle and characteristics of SVD, combined with the image of the matrix structure. A block SVD-based image compression scheme is demonstrated and the usage feasibility of Block SVD-based image compression is proved.

Lossy image compression using singular value decomposition and wavelet difference reduction

2014 ◽  
Vol 24 ◽  
pp. 117-123 ◽  
Author(s):  
Awwal Mohammed Rufai ◽  
Gholamreza Anbarjafari ◽  
Hasan Demirel
Keyword(s):  
Singular Value Decomposition ◽  
Image Compression ◽  
Singular Value ◽  
Value Decomposition

scholarly journals A Note on Full-Rank Factorization of Matrix

2019 ◽  
Vol 15 (2) ◽  
pp. 152-154
Author(s):  
Gyan Bahadur Thapa ◽  
J. López-Bonilla ◽  
R. López-Vázquez
Keyword(s):  
Singular Value Decomposition ◽  
Full Rank ◽  
Singular Value ◽  
The Matrix ◽  
Rank Factorization ◽  
Value Decomposition

We exhibit that the Singular Value Decomposition of a matrix Anxm implies a natural full-rank factorization of the matrix.

Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions

2016 ◽  
Vol 231 (22) ◽  
pp. 4186-4196 ◽  
Author(s):  
Mourad Kedadouche ◽  
Zhaoheng Liu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Support Vector ◽  
Rolling Bearings ◽  
The Matrix ◽  
Value Decomposition

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

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