# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 2 Related Work
### Deep Learning-Based Compression Methods for Natural and Remote Sensing Images  
Traditional image compression methods (e.g., JPEG [8] and JPEG2000 [9]) mainly rely on classical transform coding techniques, such as discrete cosine transform (DCT [17]) or discrete wavelet transform (DWT [18]), and then perform the quantization and entropy coding of the transform coefficients to achieve compression. These methods were originally designed primarily for the compression of natural images, relying on hand-crafted rules and predetermined transformation strategies. However, with the increase in image complexity and the demand for higher compression rates and quality, the limitations of these traditional methods have gradually become apparent. In contrast, learning-based image compression methods use an end-to-end deep learning framework to automatically learn feature extraction and compression laws from data. These methods typically construct encoders by stacking convolutional blocks, which are used to extract latent feature representations of the input image. These latent representations exhibit a high degree of density and representational power by capturing the spatial structure and content information of the image through the encoder. After obtaining the latent representations, these methods model them by using entropy models. Entropy models typically assume that the distribution of the latent representation is Gaussian (or mixed Gaussian) and model it by introducing a learnable prior distribution. This approach allows the latent representation to be reconstructed more accurately in the decoding stage, while significantly improving compression efficiency. [PERSON] et al. [19] proposed a spatial channel context adaptive model by combining the inhomogeneous group entropy model with the context model, which successfully solved the problem of reusing channels with low entropy distribution. [PERSON] et al. [20] proposed a parallel Transformer-CNN hybrid module, which combines the local modelling capability of CNNs and the non-local modelling capability of Transformers and optimizes the codec to optimize and solve the spatial redundancy problem of natural images in the compression process. [PERSON] et al. [21] proposed a more flexible discrete Gaussian-Laplace Logistic Mixed Model (GLLMM), which can more accurately and efficiently adapt to different content in different images and regions with the same complexity. On the other hand, for remote sensing images, [PERSON] et al. [22] used the discrete wavelet transform (DWT) to divide the image features into high-frequency and low-frequency feature components, they also introduced a frequency domain coding-decoding module to improve the ability of the model to represent the high-frequency and low-frequency features. [PERSON] et al. [23] proposed a method of using diffusion models through ground stations to solve the problem of information loss that may occur in the compressed remote sensing satellite images. The problem of information loss in remote sensing satellite images after compression is solved by deploying a diffusion model at the ground station. However, although the above methods have achieved excellent performance in natural and remote sensing image compression, they face certain challenges in hyperspectral image compression. The spectral information represented by each band (or channel) of hyperspectral images has large differences, and the importance of each channel is not the same, which makes some compression techniques (e.g., the spatial channel context adaptive model) for natural images not fully applicable to hyperspectral images.