Digital Image Processing using Singular Value Decomposition
| Autor: | Jay Prakash Pandey, Lokendra Singh Umrao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Singular value Digital image Computer Science::Computer Vision and Pattern Recognition Compression (functional analysis) Digital image processing Singular value decomposition Compression ratio Computer vision Artificial intelligence business Image compression |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3350278 |
| Popis: | This paper consists theory of linear algebra called “Singular Value Decomposition (SVD)” to digital image processing. Two specific areas of digital image processing (DIP) are tested and investigated. SVD method can transform a matrix A into product USV T , which allow us for refactoring a digital image in 3 matrices. which can preserve useful features of the original image, but use less storage space in the memory and achieve the image compression process. The experiments with different singular value are performed and the compression results are assessed by compression ratio and quality measurement. In this paper, It is discussed that how SVD is applied to images, the technique of image compression and maintaining the quality of the image using SVD. The algorithm to compress an image using MATLAB is also applied. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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