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

中文翻译：

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基于混合最优算法的二维离散小波变换用于使用分数 KCA 的图像压缩

由于传统压缩模型的低压缩性能，我们开发了一种新的基于 HOA 的带 2D-DWT 的分数 KCA，以提高多光谱图像质量。在本文中，我们提出了一种新颖的多光谱图像压缩方法，通过保持重建质量并减少多光谱图像的存储大小来提高复杂性。最初，Karhunen-Loeve 变换 (KLT) 用于去除空间冗余。在第二阶段，2D DWT 用于消除带内空间冗余。在第三阶段，分数 KCA (FKCA) 用于改进后转换过程。FKCA 连接到所有小波子带的频带以最小化子带内之间的空间冗余。最后，基于混合最优算法（HOA）的FKCA用于消除相邻频段之间的残差和信息冗余。所提出的基于 2D-DWT 的分数 KCA 的实验分析表明，该模型在 PSNR、MSSI 和 VIF 方面提高了压缩数据的性能。此外，多光谱图像数据集显示，所提出的压缩模型优于现有的压缩模型，例如 FKLT + PCA、ADWT + OADL 和 DWT + DCT