# A Stronger Adaptive Local Dimming Method with Details Preservation
## 1 Introduction  
High Dynamic Range (HDR) display is developed for HDR images and videos that convey vastly more color shades and nuances than previous standards. However, these devices are expensive for their complex technology, which limits their promotion. Liquid Crystal Display (LCD) is still the current technology for most devices such as computers and TVs. LCD consists of a Liquid Crystal (LC) panel and a backlight panel with arrayed Back-Light Units (BLUs). The LC panel is light-modulated instead of self-luminous directly. Hence, an image is displayed by it with the backlight produced by BLUs. In early LCD technology, BLUs are always-on with the maximum luminance level, leading to high power consumption and low contrast ratio. In addition, the image quality is deteriorated due to the light leakage problem [1] in the dark state. Local dimming technology is developed to alleviate these weaknesses. As shown in Figure 1, the technology consists of the backlight extraction and the pixel compensation, which are, respectively, used in obtaining the luminance level for each BLU in backlight panel and the compensated image for the LC panel. In the process of backlight extraction, the luminance of each BLU is controlled dynamically according to the corresponding image content. The power consumption is reduced while the contrast ratio is improved effectively. Backlight smoothing is used to simulate the process of light diffusion, which is a solution to alleviate block artifacts [2; 3]. Pixel compensation offsets the luminance reduction caused by the backlight dimming in the backlight extraction process. The resolution of the displayed image in LC panel is larger than that of the backlight array in backlight panel. The diagram of backlight extraction is shown in Figure 2; the luminance of a BLU is determined by the corresponding block of the input image. Many approaches for backlight extraction have been proposed. They determine the luminance level for BLUs from different characteristics of the image. The early method in [4] explores the maximum and the average luminance of the corresponding image block to determine the luminance level of each BLU. The following methods [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] extract luminance for BLUs from many other perspectives, such as image histogram, image details, and image quality. However, each method can hardly handle images with diverse and complicated contents. It makes sense to broaden the adaptive scope for a single backlight extraction method. To this end, we propose a method to extract backlight that adapts to images with diverse contents by combining the advantages of some existing methods [4; 6; 7; 8; 10; 11; 12], to which previous approaches have paid rare attention. Besides, we introduce subjective evaluation for a better visual perception. Specifically, our method takes three steps to obtain backlight luminance. First, a target backlight is selected from base backlights generated by existing methods. Second, we design a group of constraint conditions and adjust the target backlight under them to obtain several alternative backlights. Finally, the optimal backlight is determined by both the objective evaluation and the display quality of subjective evaluation. Luminance overcompensation in pixel compensation process will cause image distortion, decreasing its contrast ratio and visual perception. Hence, we take both the backlight information and the luminance information of original image into account to address the overcompensation. Besides, we propose an Improved Bi-Histogram Equalization (IBHE) to further enhance the image. Specifically, Bi-Histogram Equalization (BHE) [17] applies a histogram equalization on two sub-images segmented from one image, obtaining a tradeoff between brightness enhancement and details preservation. [PERSON] [PERSON] [17] adopted the mean luminance of the image as the breakpoint to make segmentation.