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81‐2: Invited Paper: Neural Network Based Quantitative Evaluation of Display Non‐Uniformity Corresponds Well with Human Visual Evaluation
Author(s) -
Tsutsukawa Kazuki,
Kobayashi Manabu,
Bamba Yusuke
Publication year - 2020
Publication title -
sid symposium digest of technical papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.351
H-Index - 44
eISSN - 2168-0159
pISSN - 0097-966X
DOI - 10.1002/sdtp.14097
Subject(s) - luminance , mura , artificial neural network , correlation coefficient , pearson product moment correlation coefficient , evaluation methods , artificial intelligence , computer science , correlation , computer vision , pattern recognition (psychology) , statistics , mathematics , machine learning , engineering , reliability engineering , liquid crystal display , geometry , operating system
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.