# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 7 Results and Discussion  
### Experimental Results of the Region of Interest  
In this paper, we select some pictures from image database to test the improved Itti model. Meanwhile, in order to compare the experimental results, saliency maps of Itti and GBVS are also listed, the results as shown in Figures 8-10:  
In Figures 8-10, (a) is the original image, (b), (c) and (d) are salient overlap maps of Graph-Based Visual Saliency (GBVS) model, Itti model and this paper's model respectively. (e), (f) and (g) are salient maps of GBVS model, Itti model and this paper's model respectively. The white areas represent region of interest, and the black areas represent the region of non-interest. The higher the brightness of the white part is, the higher the degree of interest of the human eye becomes. From figures of (b), (c) and (d), we can see the regions of interest which obtained from the method of this paper are more accurate than others. The regions are consistent quite well with human perception and human visual process.  
To verify the accuracy of the region of interest which is better obtained by the improved Itti model, we use eye-tracking data [45]. It was collected in order to better understand how people look to images when assessing image quality. This database consists of 40 original images. Each original image was further processed to produce four different versions, which resulted in a total of 160 images used in the experiment. Participants performed two eye-tracking experiments: one with a free-looking task and one with a quality assessment task. With the eye tracker it was possible to track the eye movement of the viewers and map the salient regions for images given different tasks and with different levels of image quality. Some meaningful progress in the design of eye-tracking data is reported in the literature [46].  
Figure 8: _Cont_.  
Since recording eye movements is so far the most reliable ways for studying human visual attention [10], it is highly desirable to use these \"ground truth\" visual attention data for the model of extract ROI. We use the images of the eye-tracking database to extract the region of interest, and compare with the results of eye-tracking database. The resulting ROI are illustrated in Figures 11 and 12. (e) as an example a saliency map derived from eye-tracking data obtained in  
Figure 8: Comparison of different visual saliency models. (**a**) Original image; (**b–d**) salient overlap maps of GBVS model, Itti model and improved Itti model respectively; (**e–g**) salient maps of GBVS model, Itti model and improved Itti model respectively.  
Figure 10: Comparison of different visual saliency models. (**a**) Original image; (**b–d**) salient overlap maps of GBVS model, Itti model and improved Itti model respectively; (**e–g**) salient maps of GBVS model, Itti model and improved Itti model respectively.  
Figure 9: Comparison of different visual saliency models. (**a**) Original image; (**b–d**) salient overlap maps of GBVS model, Itti model and improved Itti model respectively; (**e–g**) salient maps of GBVS model, Itti model and improved Itti model respectively.  
experiment I (free looking task) for one of the original images, and the (i) obtained in experiment II (scoring task) for the same image. It can be seen that the improved Itti model achieves comparable testing results and approaches the performance of the eye-tracking data. This model is able to successfully predict ROI in close agreement with human judgments.