P‐12.6: Research on Color Conversion Model of Multi‐Primary‐Color Display

Sun, Yan; Zhang, Xiaomang; Shi, Tiankuo; Hou, Yifan; Ji, Zhihua; Chu, Minglei; Sun, Wei; Chen, Ming; Dong, Xue

doi:10.1002/sdtp.15347

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…The multi-layer BP artificial neural network (ML-BP-ANN) can also be used to perform the conversion from the [R, G, B] space to the [X, Y, Z] space in the case of wide color gamut [17]. The architecture of an ML-BP-ANN is shown in Figure 2, where W (0) , W (1) , and W (q) denote the link weights, and θ denotes the threshold of the neurons, and a i , b j , d p denote the output values of the neurons in each layer, respectively. It should be noted that the number of hidden layers and the neurons in each hidden layer can be decided based on the training and testing experiments [17], and the values of the link weights and the values of the thresholds can be decided through training.…”

Section: Artificial Neural Network Ml-bp-annmentioning

confidence: 99%

“…Today, with the rapid development of wide-color-gamut display technology, a series of color display standards with wide color gamut have been put forward, such as Adobe-RGB, DCI-P3, and Rec.2020. Meanwhile, many kinds of multi-primary display systems with wide color gamut have been demonstrated [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Figure 1 a shows the wide-color-gamut blocks displayed on a six-primary LED array display developed by our laboratory.…”

Section: Introductionmentioning

confidence: 99%

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Color Conversion of Wide-Color-Gamut Cameras Using Optimal Training Groups

Liao

et al. 2023

Sensors

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The colorimetric conversion of wide-color-gamut cameras plays an important role in the field of wide-color-gamut displays. However, it is rather difficult for us to establish the conversion models with desired approximation accuracy in the case of wide color gamut. In this paper, we propose using an optimal method to establish the color conversion models that change the RGB space of cameras to the XYZ space of a CIEXYZ system. The method makes use of the Pearson correlation coefficient to evaluate the linear correlation between the RGB values and the XYZ values in a training group so that a training group with optimal linear correlation can be obtained. By using the training group with optimal linear correlation, the color conversion models can be established, and the desired color conversion accuracy can be obtained in the whole color space. In the experiments, the wide-color-gamut sample groups were designed and then divided into different groups according to their hue angles and chromas in the CIE1976L*a*b* space, with the Pearson correlation coefficient being used to evaluate the linearity between RGB and XYZ space. Particularly, two kinds of color conversion models employing polynomial formulas with different terms and a BP artificial neural network (BP-ANN) were trained and tested with the same sample groups. The experimental results show that the color conversion errors (CIE1976L*a*b* color difference) of the polynomial transforms with the training groups divided by hue angles can be decreased efficiently.

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Section: Artificial Neural Network Ml-bp-annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color Conversion of Wide-Color-Gamut Cameras Using Optimal Training Groups

Liao

et al. 2023

Sensors

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Colorimetric characterization of the wide-color-gamut camera using the multilayer artificial neural network

Liao

et al. 2023

J. Opt. Soc. Am. A

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In order to realize colorimetric characterization for the wide-color-gamut camera, we propose using the multilayer artificial neural network (ML-ANN) with the error-backpropagation algorithm, to model the color conversion from the RGB space of camera to the XYZ space of the CIEXYZ standard. In this paper, the architecture model, forward-calculation model, error-backpropagation model, and the training policy of the ML-ANN were introduced. Based on the spectral reflectance curves of the ColorChecker-SG blocks and the spectral sensitivity functions of the RGB channels of typical color cameras, the method of producing the wide-color-gamut samples for the training and testing of the ML-ANN was proposed. Meanwhile, the comparative experiment employing different polynomial transforms with the least-square method was conducted. The experimental results have shown that, with the increase of the hidden layers and the neurons in each hidden layer, the training and testing errors can be decreased obviously. The mean training errors and mean testing errors of the ML-ANN with optimal hidden layers have been decreased to 0.69 and 0.84 (color difference of CIELAB), respectively, which is much better than all the polynomial transforms, including quartic polynomial transform.

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P‐12.6: Research on Color Conversion Model of Multi‐Primary‐Color Display

Cited by 2 publications

References 5 publications

Color Conversion of Wide-Color-Gamut Cameras Using Optimal Training Groups

Color Conversion of Wide-Color-Gamut Cameras Using Optimal Training Groups

Colorimetric characterization of the wide-color-gamut camera using the multilayer artificial neural network

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