We developed a neural network-based method for evaluation of display luminance and color non-uniformity (which we call Mura). We studied a correlation between our developed method and human visual evaluation because visual evaluation is the gold standard for Mura evaluation. We achieved Pearson correlation coefficient of 0.82.

We developed a method for automated evaluation of display luminance non-uniformity using an auto-encoder. Usually, a reconstruction loss of auto-encoder is used for abnormality detection. In our method, we used reconstruction loss as the main indicator and cosine similarity as a secondary indicator. Our method succeeded in the non-uniformity evaluation.