# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
## 4 Experiments and Analysis
### Quantitative Results  
The proposed architectures FIO-F and JSCIFI-F are compared with the methods of [PERSON] et al. [40], [PERSON] et al. [41], [PERSON] et al. [32], [PERSON] et al. [34], and [PERSON] et al. [12], as well as SSCNet [42], SpatialNet [43], and PCA implemented by using sklearn on the generated dataset. Moreover, the results are illustrated in Figures 6 and 7. It can be seen that both FIO-F and JSCIFI-F achieve better results on mixed datasets, far exceeding traditional deep learning methods. In addition, they outperform the fast neural radiation field approach. Compared with traditional deep learning methods, FIO-F, JSCIFI-F, and FHNeRF [34] all achieve nearly 12 dB in PSNR, 15 dB in MS-SSIM, and 0.05 in SAM, demonstrating that neural representations are superior to traditional deep learning methods in RD metrics when hyperspectral datasets do not meet the demands of deep learning feature extraction. Moreover, among the neural representation methods, it can be seen that the method based on video stream representation is superior to the method based on neural radiation fields, i.e., FHNeRF [34]. Specifically, the neural radiation field-based method maps the encoded coordinate information to the spectral information of the input hyperspectral image, whereas the video stream representation-based method slices the image into a representation of video frames and then performs signal fitting based on spatial-temporal information or temporal information. We are surprised to find that the PCA method outperforms traditional deep learning methods, which proves that manual feature extraction is superior to neural networks performing feature extraction automatically without sufficient data support. In fact, FHNeRF is designed to meet the need for fast fitting, so the number of training rounds does not achieve optimal compression performance. In order to compare the performance upper bounds of the methods for neural representations more adequately, we take the Botswana dataset as the input to FHNeRF and JSCIFI-F and calculate the number of parameters, the pt-file size, and the fitting speed. The results are illustrated in Figure 8. It can be seen that better results are achieved by JSCIFI-F. FHNeRF training slows down  
Figure 7: The overall results of FIO-F and JSCIFI-F compared to FHNeRF and some traditional deep learning-based methods using PSNR and MS-SSIM [12; 34; 40; 41; 42; 43]. at 5000 rounds of iterations in terms of growth, and the highest PSNR achieved is around 37.5 dB. As for JSCIFI-F, a PSNR of approximately 44 dB is achieved. We also compare the performance difference between decomposing a hyperspectral image into a three-channel video list and a single-channel video list on the architecture of FIO-F. The results are shown in Table 2.  
It can be seen that the performance of the network when a single frame is used as input is not as good as with three-channel frames, and this is easily explained: the single-frame input increases the number of frames, namely it increases the input spatial-temporal dimension, and the network needs to fit more negatively complex data with the same network setup. Moreover, we compare our model with former deep learning-based methods in neural network complexity. For the output size of the network, given an input index \(t\) of a channel, the output is always the size of the patch corresponding to this channel, which, in our experiments, is 256 \(\times\) 256 or 512 \(\times\) 512. Then, for the other layers inside the network, changing the number of layers, as well as the size of kernels, can change the number of network parameters, which is the compression ratio. For the proposed method, the size of the network's parameters represents the compression ratio. We list some of the network settings in Table 3.  
Figure 8: The results on Botswana dataset of FHNeRF (using 0.2 M parameters and achieving a bpppb of 43 compared to the mat file) and JSCIFI-F (using 0.19 M parameters and achieving a compression ratio of 35 compared to the mat file).