Singular value decomposition for approximate block matching in image coding

1995 ◽  
Vol 31 (25) ◽  
pp. 2164-2165 ◽  
Author(s):  
J.A. Robinson
Keyword(s):  
Singular Value Decomposition ◽  
Image Coding ◽  
Singular Value ◽  
Block Matching ◽  
Value Decomposition

scholarly journals A discrete wavelet transform-singular value decomposition system for image coding

2007 ◽  
Vol 5 (02) ◽  
Author(s):  
H. de Jesús Ochoa-Domí­nguez ◽  
K. R. Rao
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Vector Quantization ◽  
Image Coding ◽  
Decision Criterion ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Scalar Quantization ◽  
Value Decomposition ◽  
Decomposition System

A system that combines techniques of wavelet transform (DWT) and singular value decomposition (SVD) to encode images is presented. The image is divided into tiles or blocks of 64x64 pixels. The decision criterion as to which transform to use is based on the standard deviation of the 8x8 pixel subblocks of the tile to encode. A successive approximation quantizer is used to encode the subbands and vector quantization/scalar quantization is used to encode the SVD eigenvectors/eigenvalues, respectively. For coding color images, the RGB components are transformed into YCbCr before encoding in 4:2:0 format. Results show that the proposed system outperforms the JPEG and approaches the JPEG2000.

scholarly journals Singular value decomposition filtering in high-frame-rate cardiac vector flow imaging

2020 ◽  
Vol 9 (1) ◽  
pp. 171-179
Author(s):  
Michiya Mozumi ◽  
Ryo Nagaoka ◽  
Hideyuki Hasegawa
Keyword(s):  
Blood Flow ◽  
Singular Value Decomposition ◽  
Cardiac Function ◽  
Left Ventricular ◽  
Singular Value ◽  
Block Matching ◽  
Frame Rate ◽  
Accurate Estimation ◽  
Cardiac Blood ◽  
Value Decomposition

Dysfunction of the left ventricle (LV) weakens the cardiac function and affects the physical activity. Echocardiagraphy has been used to visualize the blood flow dynamics and to evaluate the cardiac function. However, the signal processing to suppress the clutter signals should be employed. In this study, we employed the singular value decomposition (SVD) clutter filtering to obtain the cardiac blood speckle images. We also employed the adaptive thresholding metric to determine the proper cutoff values at each phase during the cardiac cycle. Moreover, we employed a depth-dependent SVD clutter filter for more accurate estimation of the cardiac blood echo signals. The 2D blood flow velocity vectors were estimated by applying the block matching method to obtained blood speckle images. The obtained results show that the proposed filter suppressed the clutter signals from left ventricular wall significantly, and the contrast-to-noise ratio (CNR) was improved from -0.5 dB to 13.8 dB by the proposed SVD clutter filtering.

scholarly journals Singular Value Decomposition Based Image Coding for Achieving Additional Compression to JPEG Images

2012 ◽  
pp. 1-7
Author(s):  
Rehna. V. J ◽  
Jeyakumar. M. K
Keyword(s):  
Singular Value Decomposition ◽  
Image Compression ◽  
Image Coding ◽  
Weather Prediction ◽  
Singular Value ◽  
Underlying Structure ◽  
Storage Space ◽  
Compression Technique ◽  
Jpeg Images ◽  
Value Decomposition

Computer technology these days is most focused on storage space and speed. Considerable advancements in this direction can be achieved through the usage of digital image compression techniques. In this paper we present a well studied singular value decomposition based JPEG image compression technique. Singular Value Decomposition is a way of factorizing matrices into a series of linear approximations that expose the underlying structure of the matrix. SVD is extraordinarily useful and has many applications such as data analysis, signal processing, pattern recognition, objects detection and weather prediction. An attempt is made to implement this method of factorization to perform second round of compression on JPEG images to optimize storage space. Compression is further enhanced by the removal of singularity after the initial compression performed using SVD. MATLAB R2010a with image processing toolbox is used as the development tool for implementing the algorithm.

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