# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 4 Reviewing Content Quality
### Sharpness  
The most simple way to assess sharpness in an image (or video) is considering that the closer to the camera, the sharper the image. In the same way, the farther to the camera, the sharpness the image is. Thus, it is possible to determine some ranges of distance within the field of view of the cameras, with different quality levels.  
Generally in the literature, a camera's field of view is divided into three regions: the closer to the camera (with high quality), the farther (with low quality) and an intermediary region (with medium quality) [6; 17; 67]. These configurations are depicted in Figure 5 for the regions \(H\), \(L\) and \(M\), respectively.  
However, the distance range metric can assign some anomalous quality levels. For instance, two points distant one from the other, in opposite extremity on the same quality region, will present the same content quality, while two points very close one from the other, in different quality regions, but near to the border, will present different content qualities. To circumvent this issue, some authors model the sharpness considering a continuous variation of \"quality of image\" associated with a continuous variation of distance from the camera, as shown in Figure 6. In this case, a gradient of color represents a variation of quality, being the darkest areas the zone with higher quality and the clearest areas the zone with lower quality. In a continuous modeling, quality can be described through the quadratic error of the ratio of the distance between the sensor and target to the sensing range. Then, the content quality is computed through a simple quadratic function [39; 40; 42; 43; 46; 47] or by the mean squared error between the absolute position of camera and the point of interest [37].  
If it is considered that the measure of distance in content quality assessment depends on the analysis of images captured by visual sensors, some uncertainties are intrinsic to the applications, which foster approaches based on probabilistic and indirect measures. For instance, instead of binary disk model that assumes that a target is covered by a sensor if the target is within field of view of the visual sensor, a probabilistic approach can determine the coverage quality based on the Elfes coverage model [44; 45] or Probabilistic Data Association and Markov Chain Monte Carlo Data Association [68]. These models determine the likely position of the target using a probabilistic scoring-based localization algorithm [44; 45]. On the other hand, indirect measures of distance can consider the size of objects or regions of interest in the image, in terms of number of pixels. For this, rectangular bounding boxes are defined in the image circumscribing the objects or regions of interest. The size of a bounding box depends on the distance of the object from the camera and it is used as quality metric [69; 70; 71].  
Figure 5: Content quality assuming regions of sharpness.  
Finally, some works in the literature have proposed the combination of metrics to assess content quality. An example is to use a content score based on the location of a target in the region of interest of an image, combined with the size of the target on the image (which depends on the distance of the target to the camera) [72]. Another possible combination to define the quality of an application is consider jointly the distance between the target and the camera with the target speed, velocity and moving direction, in case of determining visual quality of mobile targets coverage applications [73].