# Backlight Unit Excessive Dimming Based on Perceptual Image Analysis
## 2 Background  
### Backlight Dimming Based on Image Analysis  
Backlight dimming minimizes the backlight brightness and compensates image quality loss by correcting the brightness of the image through pixel compensation, which proportionally increases the pixel value, that is, the transmittance of the panel. Together with the maximum pixel value dimming (max. pixel dimming), Figure 1 shows the output pixel mapping function of the input pixel before and after excessive dimming. The dotted lines in the horizontal direction from the top mean the brightness of the backlight before and after the max. pixel dimming and before and after excessive dimming, which excessively increases the amount of dimming. The pairs of left and right dotted lines in the vertical direction represent the change of max. pixel on the axis of the input pixel due to the excessive dimming before and after the dimming compensation, respectively.  
As shown in Figure 1, performing the max. pixel dimming compensation by changing the output pixel mapping function of the input pixel from the steeper line on the left to the gentle line on the right can improve the contrast and reduce the power consumption without deteriorating the image quality due to dimming. In the case of excessive dimming, even if the pixel value is adjusted through the compensation, distortion happens in the area corresponding to the values from bed' to max. pixel, which correspond to values from bed to 255 after compensation in the input pixel values as described in Figure 1. This is called pixel saturation and deteriorates the image quality. In this paper, the pixel value bed' before the compensation will be optimized to maximize the excessive dimming while minimizing perceived image quality loss.  
Considering that HVS is involved in the image quality perception, if the image quality degradation caused by the pixel saturation is unrecognizable or is below an acceptable level depending on the application fields, power consumption reduction through excessive dimming can be maximized without adversely affecting the perceived image quality.  
Figure 2 shows the effect of pixel saturation on image quality, which occurs under the existing compensation. Figure 2a is the case of max. pixel dimming, where no degradation has occurred in the image quality. Figure 2b shows a 95% pixel count method, resulting in a slight saturation, but there is not much difference from Figure 2a. Figure 2c shows the histogram weighted average method [10], which illustrates that excessive dimming is the largest, minimizing power consumption. For Figure 2c peak signal-to-noise ratio (PSNR) is 24, which is a significant reduction in numerical quality; however, there is little perceptual degradation in image quality. To measure perceptual image quality, a commonly used perceptual image quality metric, mean opinion score (MOS), is used. Twelve people of eight males and four females measured MOS scores to give ratings from 1 to 10, where high scores represent better image quality.  
Figure 1: Pixel compensation for excessive dimming.  
Existing numerical approaches for degradation measure, such as PSNRs, do not exactly correspond to the degradation that humans perceive. Therefore, it is meaningful to maximize the excessive dimming through an approach that considers human cognitive characteristics. This reduces power consumption further and preserves perceived image quality, which will be discussed in Section 4.3.