# A Regional Brightness Control Method for a Beam Projector to Avoid Human Glare
## 2 Related Research  
### Structural Similarity Index Measure  
Image quality assessment (IQA) is a research topic that studies the degree of distortion in an image due to its compression, movement, rotation, etc. Methods to evaluate image quality distortion are generally classified as Full Reference (FR), No Reference (NR), and Reduced Reference (RR) methods, depending on how much of the original image can be referred to during the evaluation process [8]. Our study can apply the FR method since an original presentation image and the captured image from a camera are both available. If the captured image has relatively low quality compared to the original one, it may be caused by an obstacle appearing between the screen and the camera.  
In traditional FR methods, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are popular techniques that can evaluate the difference between an original image (or video) and a distorted one with simple calculations. Unfortunately, they are known to not be so useful when the distortion effects overlap or multi-distorted images are given for evaluation [9; 10; 11].  
To address these limitations, Chandler and Hemami (2007) argued that it is necessary to combine techniques based on the human visual system, such as luminance, contrast, and texture, to evaluate the image quality efficiently [12]. There has been some research on IQA models motivated by the human visual system. The Structural Similarity Index Measure (SSIM) is a representative one of those models, which is based on the hypothesis that the human visual system is mainly affected by the structural information of images [8]. SSIM measures the similarity of two images by extracting luminance, contrast, and structural features from those images. While traditional IQA models, including MSE and PSNR, produce numerical evaluation results that are not so understandable by humans, SSIM has an advantage in that it enables the analysis of the differences in images from more understandable results [13].  
Though the SSIM measured the similarity of images globally [8], there was an argument that the similarity of the images could be used as an indicator to find non-similar regions of an image when applied regionally. A. Aki et al. showed that it was possible to measure dissimilarity and observe changes in the ground using satellite images of the same area at different times as an example [14]. In their approach, though the exact formula to calculate SSIM was not applied, they introduced an idea to measure the similarity of images regionally. This suggests SSIM can also be regionally used to find the more probable area for human detection in an image by excluding the other areas with higher similarity to the original one.  
In summary, a review of the literature on SSIM suggests the following implications for this research: First, if one specifies a window and measures the SSIM at each point [15], the SSIM can detect changes in different parts of the video. If there is a significant change between what the projector is trying to show and what one sees, it can be assumed that something is distorting the image. Second, two compared images must represent the same area to obtain an accurate SSIM. The screen will have a defined projection area due to a keystone or lens shift of the projector. When a camera observes the screen, the projection area in a captured image would appear as a rectangle (e.g., a trapezoid) [16]. Therefore, the geometric transformation process would be necessary to match the image with the original one. Finally, color correction is required before the SSIM is calculated. Suppose the projected light reflects discoloration due to the characteristics of the light source, lens, or sensor. Thus, the luminance, which is a fundamental factor in calculating the SSIM, will be influenced by those characteristics.