# Refined Test and Evaluation Method of Side Viewing Angle of LED Display Module
## 2 Methods
### Classification Model with Equal Relative Deviation between Classes  
The main purpose of this paper is to select the display module with the same luminous angle of view among a large number of display modules to improve the display effect of the whole screen. In view of this feature and combined with statistical law, this paper proposes a classification model of equal relative deviation between categories, which divides the display modules into \(n\) classes. If the relative deviation between classes is equal, then the display modules of the same class have the same luminous viewing angle so as to improve the display effect of the whole display screen.  
The brightness in the normal direction of the display module is represented by \(L_{i}(n)\), and the brightness when the angle is \(\theta\) is represented by \(L_{i}(\theta)\), where \(i\) is the serial number of the display module. The brightness deviation value \(x_{i}(\theta)\) represents the ratio of the brightness at the angle \(\theta\) to the brightness in the normal direction. The calculation method is as follows:  
\[x_{i}(\theta)=\frac{L_{i}(\theta)}{L_{i}(n)} \tag{6}\]  
The classification feature value is used to determine the classification interval of the display module. If five categories are used as an example, the calculation method of the classification feature value is as follows:  
\[\frac{X_{0}-X_{1}}{(X_{0}+X_{1})/2}=\frac{X_{1}-X_{2}}{(X_{1}+X_{2})/2}=\frac {X_{2}-X_{3}}{(X_{2}+X_{3})/2}=\frac{X_{3}-X_{4}}{(X_{3}+X_{4})/2}=\frac{X_{4} -X_{5}}{(X_{4}+X_{5})/2} \tag{7}\]  
where \(X_{0}=max(x_{i}(\theta))\), \(X_{5}=min(x_{i}(\theta))\), that is, \(X_{0}\) and \(X_{5}\) are the maximum and minimum values of the luminance deviation value, respectively.  
The classification eigenvalues \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\) can be obtained by solving the equations of the continuous equation:  
\[\left\{\begin{array}{l}X_{1}=\sqrt[5]{(X_{5}\cdot X_{0}4)}\\ X_{2}=\frac{X_{1}^{2}}{X_{0}}\\ X_{3}=\frac{X_{1}^{2}}{X_{0}^{2}}\\ X_{4}=\frac{X_{1}^{4}}{X_{0}^{3}}\end{array}\right. \tag{8}\]  
The brightness deviation value is compared with the classification feature value to determine the category of the display module: [0] means the brightness is normal, [1] means the brightness is slightly bright, [2] means the brightness is brighter, [\(-1\)] means the brightness is slightly bright dark class, [\(-2\)] means darker class.  
\[\left\{\begin{array}{l}x_{i}(\theta)<X_{1}\qquad\quad\in[2]\\ X_{1}<x_{i}(\theta)<X_{2}\quad\in[1]\\ X_{2}<x_{i}(\theta)<X_{3}\quad\in[0]\\ X_{3}<x_{i}(\theta)<X_{4}\quad\in[-1]\\ x_{i}(\theta)>X_{4}\qquad\quad\in[-2]\end{array}\right. \tag{9}\]  
The same type of display modules have the same luminous viewing angle and the same perceptual brightness of the human eye. The same type of display modules can be built into the screen, which can significantly improve the uniform effect of the display screen. The difference of the luminescence angle of the adjacent module is acceptable to the human eye, while the difference of the luminescence angle of the cross-class is not acceptable to the human eye.  
The luminescence view is closely related to the packaging process. If the packaging process of the module is the same, the same classification eigenvalue can be used. If the process is changed, the classification eigenvalue needs to be redetermined. The above steps must then be repeated to re-determine the classification feature values.