• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.3
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• pp.347-354
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• 2014

The color appearance of a yarn-dyed woven fabric depends on the color of the yarn as well as on the weave structure. Predicting the final color appearance or formulating the recipe is a difficult task, considering the interference of colored yarns and structure variations. In a modern fabric design process, the intended color appearance is attained through a digital color methodology based on numerous color data and color mixing recipes (i.e., color prediction models, accumulated in CAD systems). For successful color reproduction, accurate color prediction models should be devised and equipped for the systems. In this study, the final colors of yarn-dyed woven fabrics were predicted using six geometric-color mixing models (i.e., simple K/S model, log K/S model, D-G model, S-N model, modified S-N model, and W-O model). The color differences between the measured and the predicted colors were calculated to evaluate the accuracy of various color models used for different weave structures. The log K/S model, D-G model, and W-O model were found to be more accurate in color prediction of the woven fabrics used. Among these three models, the W-O model was found to be the best one as it gave the least color difference between the measured and the predicted colors.

• Korean Institute of Interior Design Journal
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• no.32
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• pp.97-104
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• 2002

The purpose of this study is to suggest the prediction model for the color design by the image types in the office interior. This prediction model of the color design is for the more comfortable environment by using suitable, various colors fitted with business functions. In this research, we carried out the evaluation experiment with the variables such as the color on ceiling, wall, floor and the harmonies of color schemes. We set the prediction index through the multi-regression analysis. And the prediction model was made by these results. The design methods by the prediction model are as follows. 1) The $\ulcorner$variable$\lrcorner$ image was deeply influenced by the value and chroma and it was marked high in low value and high chroma and the harmonies of contrast and different color. 2) The $\ulcorner$comfortable$\lrcorner$ image was related to the value and chroma and it was marked high in high value and low chroma and harmonies of homogeneity and similar. 3) The $\ulcorner$warm$\lrcorner$ image was greatly influenced by the hue and the harmony of color schemes, and it was marked high in the warm colors and harmonies of homogeneity.

• Journal of the Korean Applied Science and Technology
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• v.25 no.4
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• pp.438-445
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• 2008

For recipe prediction of colorant proportion showing nonlinear behavior, we modeled the effects of colorant proportion of basic colors on the target colors and predicted colorant proportion necessary for making target colors. First, colorant proportion of basic colors and color information indicated by the instrument was applied by a linear model and a multi-layer perceptrons model with back-propagation learning method. However, satisfactory results were not obtained because of nonlinear property of colors. Thus, in this study the neuro-fuzzy model with merit of artificial neural networks and fuzzy systems was presented. The proposed model was trained with test data and colorant proportion was predicted. The effectiveness of the proposed model was verified by evaluation of color difference(${\Delta}E$).

• Journal of the Korean Graphic Arts Communication Society
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• v.20 no.2
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• pp.57-68
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• 2002

It is required to estimate color reproduction accurately in printing. Because printing technology has been developing, and most people want to see the best color reproduction. Therefore many color reproduction methods, such as Neural Network, LUT(Look Up Table) have been proposed for a long time. However, these methods are required to measure a lot of samples of printing. In this paper, we propose a new method that prediction of color reproduction based on compensated Neugebauer model for dotgain. This method was significant to increase an accuracy of color prediction with simple process.

• ETRI Journal
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• v.42 no.2
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• pp.258-271
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• 2020

A correlation exists between luminance samples and chrominance samples of a color image. It is beneficial to exploit such interchannel redundancy for color image compression. We propose an algorithm that predicts chrominance components Cb and Cr from the luminance component Y. The prediction model is trained by supervised learning with Laplacian-regularized least squares to minimize the total prediction error. Kernel principal component analysis mapping, which reduces computational complexity, is implemented on the same point set at both the encoder and decoder to ensure that predictions are identical at both the ends without signaling extra location information. In addition, chrominance subsampling and entropy coding for model parameters are adopted to further reduce the bit rate. Finally, luminance information and model parameters are stored for image reconstruction. Experimental results show the performance superiority of the proposed algorithm over its predecessor and JPEG, and even over JPEG-XR. The compensation version with the chrominance difference of the proposed algorithm performs close to and even better than JPEG2000 in some cases.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.27 no.3
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• pp.203-210
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• 2021

With the development of package printing technology, the package has expanded from the basic function of protecting products to the marketing function through package design. Color, the visual element that composes the package design, is delivered to the consumer most quickly and effectively. As color marketing of these package designs expands, accurate color reproduction that the product wants to express is becoming more important. The color of an object is transmitted by absorption and scattering of light. Spectral reflectance refers to the intensity of light reflected by an object at different wavelengths by the spectral effect. As a result, the color of the object is expressed in various colors. Packaged printing inks have their own absorption and scattering coefficients, and the Kubelka-Munk model for color reproduction and prediction defines the relationship between these correlation coefficients through reflectance. In the Kubelka-Munk model for color reproduction and prediction, the relationship between the absorption and scattering coefficients (K/S) of printed material is predicted as the sum of the K/S values according to the mixing ratio of all color ink used. In this study, the reflectance of the measured print is reversely calculated at the mixing ratio of print ink using the Kubelka-Munk model. Through this, the relationship value of the ink-specific absorption/scattering coefficient constituting the final printed material is predicted. Delta E is derived through the predicted reflectance, and the similarity between the measured value and the predicted value is confirmed.

• Smart Media Journal
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• v.11 no.3
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• pp.74-89
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• 2022

Due to the unique recipes and processes of each company, not only differences among the results of dyeing textile materials exist but they are also difficult to predict. This study attempted to develop a color prediction model based on deep learning to optimize color realization in the dyeing process. For this purpose, deep learning-based models such as multilayer perceptron, CNN and LSTM models were selected. Three forecasting models were trained by collecting a total of 376 data sets. The three predictive models were compared and analyzed using the cross-validation method. The mean of the CMC (2:1) color difference for the prediction results of the LSTM model was found to be the best.

• Food Science and Preservation
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• v.30 no.2
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• pp.224-234
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• 2023

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. R_c² values of ≥0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (R_p²=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model's accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1509-1518
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• 2015

Because a logo is a medium that connects between consumers and corporations or brands, designing memorable logo images is vital. Although predicting logo's memorability for brand marketing is essential, there have been only few researches that deal with memorability of logo images. In this paper, we analyze the memorability characteristics in logo images by performing experiments based upon our proposed prediction method for logo image's memorability. Our proposed research consists of three phases: crowdsourcing for memorability computing, computational phase for logo image's memorability, and development of a prediction model. Using computed memorability of logo images by "Visual Memory Game," we analyze the different characteristics of logo's memorability. We first developed a novel computational method that reflects logo image's color and shape. Each computational method on color and shape are selected by comparing the correlations between result values and ground truth memorability. Selected computational value is then converged with generic image feature descriptors such as SIFT and HoG to make a prediction model of logo's memorability. Using our method, we obtain reasonable performances in predicting logo image's memorability.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.33-48
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• 2022

This study was performed to compare the color emotion and preference of Koreans and Chinese for a two-color combination by dyeing cotton fabric with persimmon and indigo and to establish prediction models of color preference. Nine specimens prepared by combining two different colored fabrics (persimmon and indigo) were evaluated for color emotion and preference by Korean and Chinese groups of female college students. Koreans described most specimens as natural and traditional, whereas the Chinese described them as more pleasant and elegant as well as warmer and lighter than Koreans did. The contrast tone was the most preferred combination by both groups, whereas it was perceived as more modern and less warm by Koreans. Relationships between physical color variables and color emotions were quantified; these relationships were applied to establish a prediction model of color preference with tone combination types for each group. These results could help in making the design of fashion textiles more preference- and emotion-oriented for Korean and Chinese consumers.