# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 1 Introduction  
In recent years, with the continuous improvement in satellite and airborne equipment, the acquisition of hyperspectral imagery has become more convenient and efficient. Compared with traditional RGB images, hyperspectral images provide more detailed and multi-dimensional data by capturing spectral information in multiple bands. This type of image has rich spectral resolution and is able to capture details that are not visible to the naked eye; thus, it has great potential for applications in resource exploration [1], environmental monitoring [2], agricultural assessment [3], urban planning [4], and disaster monitoring [5]. However, such high-dimensional spectral information presents technical challenges, especially in data transmission and storage. For real-time applications such as satellite communications [6] and unmanned aerial remote sensing [7], the large data volume of hyperspectral images and the increasing costs of transmission and storage make the efficient compression of these images a key issue in current research. Traditional image compression methods such as JPEG [8], JPEG2000 [9], BPG [10], etc., have performed well in compressing ordinary RGB images, especially in achieving a certain balance between the visual effect and computational efficiency. However, when it comes to compressing hyperspectral images, these methods have significant limitations. Each band of hyperspectral imagery not only contains rich spatial information but also has strong spectral correlation with different bands. Traditional compression methods cannot effectively capture this spectral correlation, resulting in the loss of a large amount of effective information in the compression process. As a result, they cannot meet the high data compression quality requirements of hyperspectral imaging applications. In addition, in some specific applications, the compression of hyperspectral images requires near-lossless or lossless compression, and methods capable of achieving this, such as CCSDS123.0-b-2 [11] and other standards, have shown satisfactory results. By fine-tuning the quantization process, these methods are able to achieve high compression ratios while maintaining data quality. However, as high time costs limit their application, researchers have gradually turned their attention to how to achieve effective lossy compression. In recent years, research on lossy image compression has made significant progress. For natural images, [PERSON] et al. [12] pioneered an end-to-end image compression framework and developed a hyper-priori model to eliminate the spatial redundancy among pixels. However, with the increasing compression performance requirements of real-world applications, the traditional architecture struggles to meet the demand. To this end, researchers have explored various directions of improvement. These include designing more powerful encoder-decoder networks to enhance the representation ability of features or introducing more advanced likelihood modelling methods into the super-priori model to optimize the coding rate. The exploration of these directions has contributed to the rapid development of natural image compression. In the field of remote sensing image compression, researchers usually build on the successful experience of natural image compression techniques. At the same time, they optimize and research the unique properties of remote sensing images to adapt methods to their characteristics, such as high resolution and rich spatial details. Although these methods have achieved some success in compressing remote sensing images, it is often difficult to achieve the expected results by directly applying natural image compression methods to hyperspectral images. This is due to the higher complexity and unique spectral properties of the latter. This challenge has motivated researchers to develop more refined compression schemes for the characteristics of hyperspectral images.