A Novel Quality Assessment Method for Flat Panel Display Defects

This paper presents a novel quality assessment method for flat panel display (FPD) defects, often called Muras, that employs the characteristics of the human visual system (HVS). Given a Mura image, the brightness difference between the Mura and its surrounding region is first adjusted to reflect the HVS's property of background-adaptive perception. Then, the resulting adjusted Mura image is further processed using multiscale defect saliency acquisition (MDSA) to obtain a final Mura image with human perception characteristics. In the experiments, the quality scores of Mura test images are measured using the conventional and proposed methods. The results demonstrate that the quality of Mura evaluated by the proposed method correlates with the subjective quality to a much higher degree compared with the conventional methods.