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- W4214806324 abstract "Hyper Spectral Image compression has attracted a lot of concentration in recent years due to massive data volumes. Thousands of gigabytes make up hyper spectral datasets. The amount of important information included in these hyper spectral images is quite limited due to redundancy. The collected HSI data sets are dominated by spatial and spectral correlations. Many transform coding approaches are available for compressing hyper spectral images by reducing spatial dimensions. These algorithms were originally designed for grayscale or color images and have been extended to hyper spectral images. Tensor decomposition methods are developed to minimize the size of an N-dimensional function. The spatial and spectral dimensions of a hyper spectral image can be reduced by modeling it as a 3rd order tensor. In this proposed hybrid method, a Discrete Wavelet Transform is used to process the hyper spectral image, which is then decomposed into Core Tensor and Component Matrices using the 3 Way Singular Value Decomposition Tucker Decomposition method. Four datasets are used to evaluate the proposed method and observed SSIM, PCC, and PSNR performance factors are good with high compression ratio for datasets over the existing techniques." @default.
- W4214806324 created "2022-03-05" @default.
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- W4214806324 date "2021-11-01" @default.
- W4214806324 modified "2023-09-28" @default.
- W4214806324 title "Hybrid Compression Method for Hyper Spectral Images using 3-Way SVD Tensor Decomposition and Discrete Wavelet Transform" @default.
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- W4214806324 doi "https://doi.org/10.1109/icissgt52025.2021.00032" @default.
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