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Hybrid optimal algorithm-based 2D discrete wavelet transform for image compression using fractional KCA
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DOI：10.1007/s00530-020-00681-6.png)
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Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen-Loeve transform (KLT) is used to remove the spatial redundancies. In the second stage, 2D DWT is used to eliminate the intraband spatial redundancies. In the third stage, Fractional KCA (FKCA) is applied to improve the post-transformation process. FKCA is connected to the band of all wavelet sub-bands to minimize the spatial redundancy between intra sub-bands. Finally, the Hybrid Optimal algorithm (HOA) based FKCA is used to eliminate the residual and information redundancy among the neighboring bands. The experimental analysis of proposed 2D-DWT based Fractional KCA shows that the model improves the performance of compression data in terms of PSNR, MSSI, and VIF. Also, the multispectral image dataset shows the proposed compression model outperforms the existing compression models such as FKLT + PCA, ADWT + OADL, and DWT + DCT
Keyword:
Discrete wavelet transform
Remote sensing
Spatial redundancy
Multispectral images
Image compression
Fractional KCA
Hybrid optimal algorithm
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