Deep-learning-based pixel compensation algorithm for local dimming liquid crystal displays of quantum-dot backlights.

Abstract

Local dimming techniques have been widely studied to achieve a high contrast ratio and low power consumption for liquid crystal displays. The luminance of a backlight is reduced according to some characteristics of an input image and the pixel data are boosted to compensate for the dimmed backlight. In addition, because a backlight block is affected by adjacent ones, the pixel compensation algorithm requires huge processing power as well as many iterations along with the overall luminance profile information of a backlight. However, a proposed deep-learning-based local dimming algorithm generates the compensated image directly from an input image without any information of backlight's dimming levels. The proposed compensation network is constructed on the basis of the U-net to maintain the high-resolution features in the up-sampling paths through skip-connections. In addition, it is also ensured that the bi-linear interpolation can be used without visible image quality degradation for the reduction on the number of parameters. The proposed networks are trained and verified on a DIV2K 2K image dataset.