## 4 Results & Discussion
### Performance Comparison  
Image degradation variety and content variance make NR-IQA a difficult task. The suggested model may express deep features for quality information by combining the dual frameworks created by the deep feature extractor and crucial learning mechanism. Table 1 compares the proposed framework to current state-of-the-art approaches. As seen in the data, the suggested scheme performed the best. The suggested method has an SROCC of 0.759, a PLCC of 0.777, and a KROCC of 0.5873. The conceptual approach improved SROCC, PLCC, and KROCC by 0.028, 0.043, and 0.0413, respectively. The new framework's outstanding performance demonstrates that adopting a spinal network for NR-IQA evaluation is advantageous. It aids the network architecture in learning the necessary information to forecast image quality. Compared to other pre-trained networks, the InceptionResNetV2-based model performed well. As a result, we used InceptionResNetV2 as the baseline model, along with a spinal network. Further, Figure 3 shows the visual comparison of the proposed architecture with existing techniques for image quality assessment.  
The better performance of the spinal network is due to its unified learning mechanism. In a spinal network, the inputs are provided periodically and gradually to the neural network, just as with the biological configuration of the human spinal cord. Two variables, which are local and global outputs, are computed as every layer in the network generates a local output consequently the modulated version of inputs is, in turn, readressed to the global output. Training data are utilized to perform a configuration of the network parameters, i.e., weights of the artificial neural network. The neural network is split into three groups--input split, intermediate and output split. A chu  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **SROCC** & **PLCC** & **KROCC** \\ \hline SSEQ [35] & 0.424 & 0.463 & 0.295 \\ BLINDS-II [36] & 0.527 & 0.559 & 0.375 \\ HOSA [37] & 0.609 & 0.653 & 0.438 \\ BRISQUE [38] & 0.519 & 0.554 & 0.368 \\ BIQI [39] & 0.431 & 0.460 & 0.229 \\ DIVINE [16] & 0.489 & 0.532 & 0.341 \\ LPIPS [40] & 0.721 & 0.713 & - \\ CORNIA [41] & 0.541 & 0.580 & 0.384 \\ CNN [42] & 0.603 & 0.619 & - \\ BosICIP [43] & 0.630 & 0.628 & - \\ InceptionResNetV2 [27] & 0.731 & 0.734 & 0.546 \\ Proposed Architecture & **0.759** & **0.777** & **0.5873** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance evaluation of the suggested scheme in comparison to existing NR-IQA methodologies.  
provided to each input split of the subsequent NN layers. Two components are fed into all the intermediate splits except the intermediate split of the first layer of the spinal network. The outputs of the existing input split of the current layer and the output of the previous intermediate layer split are both classified as network components. The weighted outputs of all the intermediate splits are combined as they are given to the output split of each subsequent layer in the neural network.  
The radar graph is used for a competitive analysis between proposed and existing techniques. Figure 4 shows the radar graph for comparison. The proposed architecture was able to show better spikes than existing techniques. The spinal network was able to give meaningful representation to the extracted feature maps.  
Figure 5 depicts the scatter graph between the expected and the actual quality scores. As can be seen in the scatter figure, there are a few outliers. The scatter plot approaches the perfect inclined line quite closely. This illustrates that the spinal network is capable of learning the significant features and predicting based on those features, improving prediction accuracy by ignoring undesired features that contribute to incorrect predictions.  
Figure 4: Radar Graph for Competitive Analysis.  
Figure 3: Performance comparison.