Enhanced Data Preservation in Light Field Hyperspectral Images through Combined Sparse Discrete Wavelet and PRN
Keywords:
Hyperspectral, lossless compression, Sparse discrete wavelet transform, Poincare recurrence neural networkAbstract
Lossless compression is a critical technique for reducing the storage and transmission requirements of light field hyper-spectral images, which are high-dimensional and data-intensive. The proposed approach Sparse Wavelet Decomposition and PRN for Lossless Compression (SDWT-PRN) leverages the advantages of both Sparse Discrete Wavelets Transform (SDWT) and Poincare recurrence neural network (PRN) for efficient and effective compression of light field hyper-spectral images. The SDWT is applied to decompose the light field hyper-spectral images into wavelet coefficients, which capture the multi-resolution and multi-directional information in the images. The PRRN is then employed to exploit the temporal redundancy among the wavelet coefficients to further compress the data. The proposed approach is evaluated on light field hyper-spectral image datasets, and the results demonstrate its superior compression performance compared to existing approaches. The investigational outcomes display that the combined SDWT and PRRN approach achieves high compression ratios while maintaining lossless reconstruction, making it suitable for efficient storage and transmission of light field hyper-spectral images in various applications, such as remote sensing, medical imaging, and scientific data analysis.
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