Deep convolutional neural network‐based quantitative evaluation of display non‐uniformity

We developed a quantitative method for evaluation of display luminance and color non-uniformity (which we call mura) using a deep convolutional neural network (DCNN). In previous research, quantitative evaluation methods using DCNN were studied. From among a wide variety of DCNN models, these methods have used a convolutional autoencoder (CAE) for abnormality detection. A CAE trained with only non-defective data was then used to evaluate the degree of mura. However, there is a problem with these methods in that they have not been able to evaluate properly when there are multiple mura defects on a single screen. Consequently, the correlation between these methods and human visual evaluation was low. Therefore, we improved the previous method to handle multiple mura defects on the same screen. We confirmed the accuracy of the proposed method by comparing the quantitative evaluation and human visual evaluation. As a result, we obtained a higher correlation than the previous methods.