Image quality evaluation based on contrast sensitivity function

Asano, Tomohiko; Takagi, Yuji; Kondo, Tohru; Yao, Jun; Liu, Wei

doi:10.1109/icma.2011.5985739

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Many methods have been used to overcome this difficulty. [1][2][3][4][5] For example, visual sensitivity characteristic models have been applied to a display image [1][2][3][4] or edge detection and differential filtering 3,5 to emphasize or extract mura and then calculate quantities of specific mura characteristics. Quantitative evaluation of mura is also supported by a few standards.…”

Section: Introductionmentioning

confidence: 99%

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

Kidoguchi¹,

Bamba²

2022

J Soc Info Display

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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.

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