P‐3.1: Convolutional Neural Network based Image Segmentation Algorithm for Dual‐Layer LCDs

Abstract

Dual-layer LCDs have the advantage of high contrast ratio, but due to the physical distance between the two LC panels, parallax error may appear when viewed off-axis, affecting the display quality. In this paper, we propose a convolutional neural network-based algorithm to mitigate parallax error and improve the display quality. First, a suitable training data set is selected and the input image is processed by a convolutional neural network to output a front image and a rear image. These two images are staggered and multiplied to obtain reproduced images at different angles. The reproduced image is compared with the original image by means of a loss function, and the parameters in the convolution are continuously updated to achieve the best display quality. The algorithm proposed in this paper can effectively mitigate the parallax error phenomenon of a dual-layer LCD and improve the display quality, and the computational efficiency is also higher than the traditional algorithms.