# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 2 Related Work
### Deep Learning-Based Hyperspectral Image Compression Method  
[PERSON] et al. [24] proposed a channel clustering strategy, which effectively simplifies the complexity of processing a large number of bands by reducing the computational requirements and shows good adaptability. However, the method is only applicable to hyperspectral image compression in a small number of bands, and it fails to effectively deal with the hyperspectral redundancy problem, which limits its application to hyperspectral images.  
[PERSON] et al. [25] designed an edge-guided hyperspectral compression network, which significantly improves the structure and detail preservation of images by capturing edge features. However, due to the overall compression of 50-band hyperspectral images, there are shortcomings in the extraction of hyperspectral information, and the network fails to fully explore the correlation between bands.  
[PERSON] et al. [26] proposed a Transformer-based autoencoder for pixel-level hyperspectral image compression by modeling remote spectral dependencies. Although the method effectively leverages the hyperspectral correlation between adjacent bands, the spatial redundancy treatment of single-band images is weak, which affects spatial information compression efficiency.  
[PERSON] et al. [27] introduced three-dimensional convolution into a hyperspectral image compression encoder, which can eliminate spatial redundancy and retain spectral information to some extent. However, the high computational complexity and the large number of model parameters of this method limit its application in resource-constrained scenarios.  
Considering the shortcomings of the existing hyperspectral image compression and modelling methods, the core idea of neural video representation is introduced into the spectral segment modelling of hyperspectral images, which captures the corresponding relationship between spectral segments by using neural networks to jointly model the spectral correlation and spatial properties and at the same time better preserves the key information of the image in the compression process.  
To present the results more clearly, we have summarized them in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** & **Applicability** & **Limitations** & **Performance Indicators** \\ \hline [PERSON] et al. (2022) [19] & Enables efficient channel reuse & Less effective for hyperspectral images & Natural images & Not fully applicable to hyperspectral images & Improved compression efficiency \\ \hline [PERSON] et al. (2023) [20] & Combines a CNN and Transformer for optimal spatial redundancy reduction & High computational complexity & Natural images & High model parameter count & High compression rates \\ \hline [PERSON] et al. (2023) [21] & Adapts dynamically to image content with GLLMM & Complex model structure & Natural images & Model adaptability & Enhanced adaptability \\ \hline [PERSON] et al. (2024) & Improved high-frequency and low-frequency feature hyperspectral images & Frequency domain techniques may not suit hyperspectral images & Remote sensing images & Limited application in hyperspectral images & Enhanced feature representation \\ \hline [PERSON] et al. (2024) & Mitigates information loss using diffusion models at ground stations & Dependent on infrastructure & Remote sensing satellite images & Computational and infrastructural dependency & Loss minimization \\ \hline [PERSON] et al. (2023) [24] & Reduces computational requirements through channel clustering & Inefficient for large band numbers & Small-band hyperspectral images & Interspectral redundancy handling is limited & Complexity reduction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brief summary of methods in related work section.