# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 4 Conclusions  
In this work, we put forward a new blind quality assessment metric of SCIs by considering regionalized structural features. Specifically, the improved histograms of oriented gradients computed from the multi-order derivatives are used as the structural features of textual regions of SCIs, and structural features of pictorial regions of SCIs include LDP histogram features in the spatial domain and SLBP histogram features in the shearlet domain. Additionally, the luminance information is also taken into account as the complementary feature of pictorial regions. The SVR-based scheme is used to incorporate these features and derive the predictive scores of textual and pictorial regions. Furthermore, we devise an activity weighting strategy to fuse the predictive scores of textual and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **Metric-T** & **Metric-P** & **BSRSF** \\ \hline PLCC & 0.8727 & 0.7829 & 0.8905 \\ SROCC & 0.8524 & 0.7684 & 0.8714 \\ RMSE & 7.7843 & 8.3547 & 7.2569 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effects of features from textual and pictorial regions on SIQAD. Figure 10: The curve of PLCC values for different combinations of HOG features. pictorial regions as the final assessment value of the SCI. Experimental results indicate that the proposed BSRSF metric is well coherent with subjective judgments and achieves preferable predictive capability compared to the existing blind metrics for SCIs. At present, the research work of the NR SCIQA is still in the initial stage and there is still a great deal of room to further optimize and improve the performance of the NR SCIQA methods. Our future studies will focus on the following six directions. Firstly, since the proposed method does not achieve the best predictive performance for the distortion type CC compared to other methods, we will further investigate structural features which are appropriate to the distortion type CC. Structural features of SCIs should be explored in more depth from the perspective of human physiology and psychology. Secondly, since subjective evaluation values are still needed to train the regression models in the proposed method, we will investigate a completely blind quality assessment method of SCIs in which subjective ratings values can be omitted. Thirdly, since both the segmentation of SCIs and the calculation of multiple features of textual and pictorial regions increase the computational complexity of the proposed method, the proposed method may be not suitable for real-time applications and so we will further improve the efficiency of the proposed method and simultaneously retain the effectiveness of the proposed method. Fourthly, more appropriate machine learning techniques, such as deep learning approaches, will be devised to further improve the predictive accuracy of the evaluation method. Deep learning approaches have been widely applied in many fields which include speech recognition, natural language processing, audio recognition and bioinformatics, and have already achieved satisfactory performance. Fifthly, we plan to develop a unified model that can simultaneously perform the faithful quality evaluation of SCIs and natural images. Finally, we will investigate the quality assessment of color SCIs and screen content videos (SCVs). Perceptual chrominance features should be considered adequately in quality evaluation models of color SCIs. Additionally, although the natural videos quality assessment methods have been extensively investigated in the past decades, studies of the quality assessment of SCVs have still not been carried out until now. Conceptualization, H.B. and Y.L. ; methodology, H.B. and W.D. ; software, W.D. ; validation, W.D. and L.L. ; formal analysis, H.B. and Y.L. ; investigation, W.D. and L.L. ; resources, H.B. ; data curation, W.D. ; writing--Original draft preparation, W.D. ; writing--Review and editing, H.B. and W.D. ; visualization, W.D. ; supervision, H.B. ; project administration, H.B. ; funding acquisition, H.B.