Edge preserved multispectral image compression using PCA and hybrid transform
| Autor: | S. Thayammal, S. Priyadarsini, D. Selvathi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science business.industry Multispectral image 020207 software engineering Pattern recognition 02 engineering and technology Sparse approximation Peak signal-to-noise ratio Image (mathematics) Hardware and Architecture Computer Science::Computer Vision and Pattern Recognition Compression (functional analysis) Metric (mathematics) Principal component analysis Compression ratio 0202 electrical engineering electronic engineering information engineering Media Technology Artificial intelligence business Software |
| Zdroj: | Multimedia Tools and Applications. 79:20133-20148 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | In multispectral image compression, it is difficult to obtain sparse approximation for images with rich details. The existing methods produce better results under some constraints on image content. In order to obtain robust multispectral image compression, the Principal Component Analysis (PCA) method is used to reduce the spectral redundancy and the sparse approximation is obtained by using Extended Shearlet Transform (EST) and Tetrolet Transform(TT). The anisotropic property of EST is used to preserve smooth images with global structures of an image whereas the TT is used to preserve the local structures. The performance of proposed method is compared with the existing methods in terms of rate distortion and information preservation perspectives. The Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) are used as rate-distortion measure. The Mean Structural Similarity Index Metric (MSSIM) and Kappa coefficient (K) are information preservation measures. The simulation results show that the proposed EPMI-HT method outperforms the existing hybrid methods for all kinds of image content at high CR with retaining edge information. |
| Databáze: | OpenAIRE |
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