A Novel Local Dimming Approach by Controlling LCD Backlight Modules via Deep Learning

The display contrast and efficiency of power consumption for LCDs (Liquid Crystal Displays) continue to attract attention from both industry and academia. Local dimming approaches for direct-type backlight modules (BLMs, also referred to as backlight units, BLUs) are regarded as a potential solution. The purpose of this study is to explore how to optimize the local dimming method of LCD to achieve higher contrast and lower power consumption through deep learning techniques. In this paper, we propose a local dimming approach with dual modulation for LCD-LED displays based on VGG19 and UNet models. Experimental results have shown that this method not only reconstructs the input image into an HDR (High Dynamic Range) image but also automatically generates a control image for the backlight module and LCD panel. In addition, the proposed method can effectively improve the contrast and reduce the power consumption of the LCD in the absence of a public training dataset. Our method can achieve the best performance in MSE and HDR-VDP-2 among eight different combinations of mask and pre-training. Using deep learning techniques, this study has successfully optimized the local dimming approach of LCDs and demonstrated its benefits in improving contrast and reducing power consumption.