# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 4 Overview of the Method  
Currently no reference image quality evaluation algorithm can be divided into two categories: distortion-specific methods and universal methods. However, the distortion-specific methods are limited in the practical application because the prior need to determine the type of the distorted images. The general-purpose methods do not depend on the type of distorted images, and have prospect of practical application. However, the existing algorithms are almost to extract natural statistical characteristics of image, and rarely take into account the visual characteristics of the human eye. Thus, in practical application, there is a deviation between the evaluation result and the result of the human eye perception. As shown in Figures 5 and 6, due to the visual characteristics of the human eye, in the same picture, different regions (such as interest and non interest areas) in the same image quality damage degree will make the human eye produce different visual experience. Under the same Gauss fuzzy, the visual observation effect of Figure 5 is better than Figure 6.  
Figure 5: Damaged figure of non-interest region.  
In the observation of the image, human eye will be the first to pay attention to the visual characteristics of the more prominent areas. Although the image suffered the same damage, because of the different regions, the human eye subjective feelings are different. Based on the background, this paper proposes a method of image quality evaluation based on visual perception. Firstly, we extract the region of interest. Secondly, extract the features from the regions of interest and regions of non-interest. Finally, the extracted features are fused effectively, and the image quality evaluation model is established. The proposed approach we called region of interest blind/reference image spatial quality evaluator (ROI-BRISQUE). Figure 7 is the framework of the proposed method in this paper.  
The method of this paper is described as follows:  
Firstly, the LIVE image database is divided into two categories: training images and testing images. In addition, we extract the region of interest and features on the training images. Then, we use the feature vectors as the input of the \(\varepsilon\)-SVR, the DMOS of the corresponding image as the output target to train the image quality evaluation model. Finally, the image quality prediction model is used to predict the quality of the distorted image.  
This paper focuses on the image region of interest extraction. The traditional Itti model calculates the salient region of the image by extracting the underlying features of the image color, brightness and direction. It can search out the attention region. However, there are still some shortcomings, for instance, the contour of the salient region is not clear. In order to extract the region of interest more accurately, we add texture and edge structure features to the Itti model. Then, we use the improved model to extract the region of interest and region of non-interest, and use BRISQUE algorithm to extract the natural statistical features of the image respectively. Moreover, the characteristics of the region of interest and region of non interest are fused to get the measure factor of the image. We train the image quality evaluation model, which the measure factor as the input and the DMOS value of corresponding image as the output target of SVR. Finally, the quality of the distorted image is predicted by the trained evaluation model.  
Figure 6: Damaged figure of interest region.  
Figure 7: Model of Region of Interest—Blind/Reference Image Spatial Quality Evaluator.