# Backlight Unit Excessive Dimming Based on Perceptual Image Analysis
## 1 Introduction  
Liquid crystal displays (LCDs) still have the highest market share in the large display markets and are also used in quantum dot light emitting diode (QLED) TVs, which are widely used in the latest products such as 8K ultra high definition (UHD) TVs. LCDs consist of LCD panels and backlight units; LCD panels control the transmittance of light to display images and backlight units provide light sources. If the backlight is always fully on regardless of the image, power is wasted, and light leakage occurs because the liquid crystal panel does not completely block the light from the backlight units. Light leakage adversely affects the quality of the image such as contrast. QLED has the LED backlight coated with quantum dot film, and the backlight is used as it is [1, 2]. In addition, local dimming can be used for each panel area by dividing hundreds of full array LED backlights [3, 4, 5]. Modern displays can use the edge-lit method to reduce display panel thickness, but the basic principle of the backlight mechanism is the same. Backlight dimming reduces the brightness of the backlight appropriately to reduce power consumption and light leakage and improves contrast. To prevent the image from darkening due to backlight dimming, the transmittance of the panel is adjusted, which is called backlight dimming compensation [6]. Panel transmittance is controlled through the pixel value, which cannot be above 255, so backlight dimming compensation has its own limit; in the case of excessive dimming, even though power consumption is reduced, brightness is not fully compensated, and image quality degrades due to pixel saturation. These problems need to be solved. As an existing backlight dimming method, there is prior work of using maximum pixel value [7]. This method uses the brightest pixel value of the image as the backlightbrightness, so there is no deterioration in image quality, but the backlight becomes brighter than necessary because it is greatly affected by a small number of bright pixels or noise irrespective of image characteristics. In addition, in the case of a backlight that is not properly dimmed, as described before, problems such as contrast degradation due to light leakage and excessive power consumption may occur. Among popular backlight dimming methods using histograms: one uses the weighted average of the histogram [8; 9] and the other is based on the pixel count [10; 11]. The methods of using the weighted average of the histogram have a disadvantage for images with many dark areas; bright areas become dark resulting poor image quality. There is also prior work on histogram-based methods to enhance medical images. A novel framework for magnetic resonance (MR) image segmentation based on genetic algorithms (MedGA) [12] and a particle swarm optimized texture based histogram equalization (PSOTHE) technique [13] were proposed. The methods using pixel count determine the backlight brightness as the gray level, which is found so that the number of pixels from the dark pixel in histogram equals to a constant ratio of the total number of pixels. This method does not degrade the image quality significantly. However, since only a certain ratio of pixels is considered regardless of the image characteristics, it is inefficient in reducing power consumption and improving contrast. Although there are differences in all dimming methods, pixel saturation occurs in pixels of bright areas by excessive dimming. Therefore, while maximizing dimming, it is necessary to minimize the degradation of image quality due to the saturation. Through analysis of various image characteristics, the degree of image quality degradation needs to be more reflected. If a dimming method is optimized accordingly, the image quality can be preserved while minimizing power consumption by maximizing dimming.