## 4 Discussion  
This paper introduces the application of the CGST codec for image compression in energy-starved systems. To adapt the CGST algorithm for this purpose, we added some modifications. Firstly, we suggested modifying the method of generating the difference signal to eliminate data loss by \"zeroing\" the brightness of pixels. Secondly, we improved the quality of the restored images and reduced the distortions introduced by CGST by supplementing the output signal of the CGST decoder with a neural network. We formed a dataset of 200 remote sensing images available in open sources to train the NN (available online at [49]). Our study focused on simple geometric figures that displayed the shapes of buildings, roads, and natural objects. When training the NN for more specific details, a larger dataset may be necessary, similar to the datasets for medical image analysis, with the extracted parameters' complex nature. Our research has substantiated that the CGST-based algorithm is compatible with transform compression methods such as PNG and JPEG to achieve energy efficiency. This substantial result indicates that the proposed method can be effectively used in conjunction with other transform-based methods, thereby ensuring extra compression. By doing so, the system's energy efficiency can be further extended, thus making it a valuable tool in energy-starved systems. The contribution to energy efficiency can be assessed, for example, using the Shannon-Hartley theorem. \[C=\Delta F\cdot\log_{2}(SNR+1), \tag{6}\]  
where \(C\) is the channel throughput, \(\Delta F\) is the channel bandwidth, and \(SNR\) is the signal-to-noise ratio. By reducing the dynamic range of the encoded image by 3 dB, the number of bits in the code package can be decreased from 8 to 7, in turn reducing the required transmission speed by 1.14 times. If the channel bandwidth is maintained, the typical signal-to-noise ratio of 10 dB for CubeSat communication systems [67] can be decreased by about 1.5 dB, enabling less powerful final amplifiers in the communication system. The test data results have shown that our proposed methods are a feasible and efficient approach to restoring images in a satellite image transmission system. This promising development could significantly impact future studies in the field. Figure 8 illustrates the possible application of CGST for image compression in such a scenario. An energy-starved system, like an Earth remote sensing satellite, compresses an image it captures using basic matrix operations. All of the resource-intensive tasks are handled by devices such as \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CW-SSIM** & **MN** & **MSE** \\ \hline NN-assisted CGST & 0.813 & 31.921 & 17.031 \\ JPEG & 0.838 & 86.651 & 24.220 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NN-assisted CGST image compression vs. traditional JPEG for the same compression ratio. satellite earth stations or image processing servers, which have a guaranteed power supply and are not as battery-dependent. A comparable situation can be presented for technical computer vision systems in multimedia and visual IoT. In this scenario, the resource-intensive process of postprocessing a compressed image with neural networks will be executed on IoT servers. Additional compression can improve the energy efficiency of IoT sensor nodes. The joint use of CGST and neural network methods has shown a reduction of 3 dB in the dynamic range of original signals. If the most significant improvement in image restoration quality is needed, a convolutional NN should be used. On the other hand, if the priority is to increase the speed of the postprocessing system, a recurrent NN can be applied. The NN-assisted CGST-based algorithm can provide a total compression ratio of approximately 4.8 for additional compression of JPEG images. This algorithm also offers better MSE and MN values than JPEG-compressed images of the same size. This advantage could be significant in acquiring images for training machine learning algorithms.