# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 1 Introduction
A hyperspectral image consists of multiple bands, each of which can be viewed as a two-dimensional image with specific spectral information. These images are arranged in spectral dimensions to form a three-dimensional data cube. In practice, the data transmission of hyperspectral imagery is usually performed in a format similar to video streaming. This is because band data are often processed and delivered sequentially during transmission, a structure that is very similar to the temporal relationship between frames in a video stream. Therefore, the relationship between spectral bands in hyperspectral images can be mapped to the conversion between frames in a video stream. The efficient representation of hyperspectral imagery can be achieved in a similar way by introducing a neural video representation based on neural radiation fields (NeRFs) [13]. NeRFs show great potential for modeling complex data with continuous spatial and temporal variations due to their high fitting capability and flexibility. Recent studies have shown that the NeRF framework has achieved significant results in 3D scene reconstruction [14] and video representation [15; 16]. This provides important insights for the efficient processing of hyperspectral images. In this study, starting from the spectral characteristics of hyperspectral images themselves and introducing the mapping relationship in video representation, an innovative modelling and compression method for hyperspectral images is proposed. This method provides a new solution for efficient data representation. The main contributions are detailed below:  
1. A framework for the video-based processing of hyperspectral images  
We innovatively consider hyperspectral images to be a form of data similar to video streaming for efficient network-based processing. Two video stream partitioning methods are proposed: single-channel video stream and three-channel video stream. The former treats each spectral band as an independent frame, which can capture the small variations between spectra in a fine-grained way; the latter combines adjacent bands into pseudo-RGB frames, which achieves a better trade-off between coding efficiency and storage space;
2. Hyperspectral image modelling based on neural video representation  
We use neural video representation to efficiently model hyperspectral images and analyze in detail the influence of the input form on the modelling effect. Experiments show that it is difficult to reconstruct high quality images by using only spectral indices (band order numbers) as input. Therefore, we propose combining spectral indices with the 2D spatial coordinates of pixels as the neural network inputs, which enhances the ability to capture spatial features and accurately model complex correlations between spectral bands. In this framework, the information of hyperspectral images is effectively embedded in the network parameters. This allows for efficient image compression and storage;
3. Experimental Validation and Performance Evaluation  
The effectiveness and applicability of the proposed method in hyperspectral image compression are verified with a large number of experiments. The experimental results show that the proposed neural video representation method is able to achieve excellent performance in the PSNR, MS-SSIM, and SAM metrics, which further improves compression efficiency while preserving image quality compared with traditional methods. This result demonstrates the potential of the method in practical applications and opens up new possibilities for the efficient transmission and storage of hyperspectral images. The rest of this paper is structured as follows: In Section 2, we review related work on deep learning-based natural image compression methods, remote sensing image compression methods, and hyperspectral image compression methods. In Section 3, we analyze the shortcomings of the learning-based methods and describe the proposed FIO-F and JSCIFI-F models in detail.