Deep learning-enabled image content-adaptive field sequential color LCDs with mini-LED backlight

Abstract

The mini-LED as the backlight of field sequential color LCD (FSC-LCD) enables high contrast, thin volume, and theoretically tripled light efficiency and resolution. However, color breakup (CBU) induced by a relative speed between an observer and the display severely limits the application of FSC-LCDs. Several driving algorithms have been proposed for CBU suppression, but their performance depends on image content. Moreover, their performance plateaus with increasing image segment number, preventing taking advantage of the massive segments introduced by mini-LEDs. Therefore, this study proposes an image content-adaptive driving algorithm for mini-LED FSC-LCDs. Deep learning-based image classification accurately determines the best FSC algorithm with the lowest CBU. In addition, the algorithm is heterogeneous that the image classification is independently performed in each segment, guaranteeing minimized CBU in all segments. We perform objective and subjective validation. Compared with the currently best algorithm, the proposed algorithm improves the performance in suppressing CBU by more than 20% using two evaluation metrics, supported by experiment-based subjective evaluation. Mini-LED FSC-LCDs driven by the proposed algorithm with outstanding CBU suppression can be ideal for display systems requiring high brightness and high resolution, such as head-up displays, virtual reality, and augmented reality displays.