Electrophoretic video display based on image semantic segmentation

Abstract

Electrophoretic displays (EPDs) are popular in consumer electronics, like e-readers and tags, for their paper-like visuals and minimal power consumption. However, EPDs struggle to rapidly switch to target grayscales due to the inherent limitations in the response speed of their display particles. Video halftoning technology achieves a smoother video grayscale display, but it also inevitably reduces the image quality and affects the viewing experience. Therefore, this paper proposes an EPD driving method based on image semantic segmentation. The method could distinguish the dynamic region and the static region accurately. For static areas, high-definition driving waveforms were used to ensure the image display accuracy. For the dynamic area, dynamic target and dynamic text were processed by different algorithms, respectively, and the speed-driving waveforms were used to maintain the fluency and clarity of the video display. With the method we proposed, the display quality for EPD in complex video playback was optimized significantly. Compared to DBS refresh techniques, the SSIM had increased from 4% to 82% and the PSNR had increased from 2.56 dB to 10.89 dB with our method, significantly enhancing the video playback performance of EPDs.