## 6 Results and Discussion
### Comparison with Exisiting Techniques  
Table 2 compares the performance of proposed Blind Image Quality Assessment (BIQA) model to that of various leading models, including FRIQUEE, NFERM, BRSIQUE, BLINDS-II, CORNIA, and DIVINE, in terms of LCC, SROCC, KROCC, and RMSE on all three benchmark datasets (CSIQ, LIVE, and TID2013). On all three datasets, proposed model outperformed all other models in terms of all evaluation metrics. On the TID2013 dataset, for example, the proposed model achieved an LCC of 0.9453, an SROCC of 0.9289, a KROCC of 0.7804, and an RMSE of 0.406, all of which are significantly better than the best results reported by the other models. Similarly, using the LIVE and CSIQ datasets, the suggested model consistently outperformed the other models in terms of all assessment measures.  
Figure 3: Visual word occurences ofr different feature vector.  
Several factors contribute to the superior performance of the proposed BIQA model. For starters, combining multiple image features such as wavelet transform, Prewitt and Sobel edge detection, and Log filters, as well as selecting them using the Information Gain attribute evaluator, allows for the capture of a wide range of relevant image attributes that are important for quality assessment. This enables the algorithm to successfully discriminate between different forms of image distortion and reliably estimate their quality.  
Second, using SVM regression as the underlying machine learning algorithm has several advantages, including the ability to handle high-dimensional feature spaces, non-linear relationships between features and quality scores, and the ability to generalize well to new, previously unseen data.  
Furthermore, extensive experimental evaluation and analysis of various types of features and feature selection techniques aid in the identification of the most effective combination of features for BIQA. Overall, the proposed BIQA model provides a promising method for reliably and robustly assessing the quality of distorted images, with potential applications in image reduction, transmission, and restoration.  
It is vital to highlight that the use of machine learning techniques, such as the SVM Regression model, provides computational efficiency by definition. ML algorithms are renowned for their capacity to effectively analyze data and generate predictions based on previously learnt patterns. Furthermore, the feature selection phase in the proposed system reduces the complexity of the feature space, enhancing computing efficiency even further.  
Certain restrictions and possible downsides must be considered in suggested approach. To begin, the framework's performance is strongly dependent on the feature extraction techniques used, such as wavelet transformation and edge detection. While these strategies have yielded promising results, their efficacy may vary based on the precise properties of the photos under consideration. Furthermore, the framework's generalizability to various image types should be examined. Although the system has been evaluated on benchmark datasets, its performance may vary when applied to other image categories or domains.