• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.872-875
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• 2002

This paper presents an efficient method of estimating the correlated color temperature of an input color image and of generating a converted color image that can approximate the new target color temperature. After extracting the potential reference white region for an uncalibrated color image and estimating its correlated color temperature, the color conversion method produces a new color image with a target color temperature. Speed improvement and memory reduction have been achieved by using a simple transfer method without involving the CIE XYZ conversion step.

• Journal of the Korean Graphic Arts Communication Society
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• v.24 no.1
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• pp.23-34
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• 2006

Color reproduction is one of the most important expression factors in digital printing. In this paper, an experiment was done where the characteristics of color reproduction in digital printing. The results could summarized as follows. The printing used device profile showed a color difference of less than printing used default value in digital printing. As a result of weighting color difference, a difference between color gamut of digital original and color gamut of printing, when transforming the RGB color space to CMY color space, a exclusion the gray revision. The solution is to optimize the color transformation by gamut mapping and gray revision.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.69-74
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• 1995

An object color can be seen differently under the various outer illuminants. However, human visual system has color constancy that the object color can be seen constantly under the different outer illuminants. When the viewer watches TV under specific outer illuminants, he perceives distorted color due to the emitting spectrum of outer illuminants as well as the radiation of CPT itself. Namely, when the outer illuminants such as fluorescent and incandescent lamps incident on CPT, brightness, saturation, hue, and contrast on color pictures are changed, he perceives distorted color from the original color. In this paper color enhancement algorithm based on light intensity and outer light decision function using RGB sensor was proposed. The implemented TV of proposed algorithm has higher visual quality at the view point of human visual system and more vivid than that of conventional color TV.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.307-308
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• 2006

In recent, displays have very good properties at high luminance, fast response, and size. Also They have good quality in terms of color according to the development of the gamut extension and color reproduction. However, despite these merits, there is a characteristic that at a high luminance display, observer perceive the different color from the originally re-producted color due to the change of perceived luminance in human visual system. In this paper, we propose a model that is the hue shift phenomenon between a normal display and a high luminance display, and then an algorithm which compensate the color between two devices, so that observer can perceive the same color.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.173-174
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• 2009

Color harmony will look good formative elements of the basic lines, shapes and colors. In this paper, we present the implementation of Johannes Itten's color balance. Yellow, red, blue, on the basis of the first color mixing between the index and second color is placed. 12 color balance made by placing a third color between the first color and second color. we recognize this 12 color balance. Edge is detected using the Canny edge operator and labeling and clustering was expressed through the colors.

• Journal of Semiconductor Engineering
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• v.2 no.1
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• pp.125-129
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• 2021

As the interest of deep learning, techniques to control the color of images in image processing field are evolving together. However, there is no clear standard for color, and it is not easy to find a way to represent only the color itself like the color-palette. In this paper, we propose a novel color palette extraction system by chroma fine-tuning with reinforcement learning. It helps to recognize the color combination to represent an input image. First, we use RGBY images to create feature maps by transferring the backbone network with well-trained model-weight which is verified at super resolution convolutional neural networks. Second, feature maps are trained to 3 fully connected layers for the color-palette generation with a generative adversarial network (GAN). Third, we use the reinforcement learning method which only changes chroma information of the GAN-output by slightly moving each Y component of YCbCr color gamut of pixel values up and down. The proposed method outperforms existing color palette extraction methods as given the accuracy of 0.9140.

• Annual Conference of KIPS
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• 2011.11a
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• pp.347-350
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• 2011

This paper propose a statistical color model of background extraction base on Hue-Saturation-Value(HSV) color space, instead of the traditional RGB space, and shows that it provides a better use of the color information. HSV color space corresponds closely to the human perception of color and it has revealed more accuracy to distinguish shadows [3] [4]. The key feature of this segmentation method is based on processing hue component of color in HSV color space on image area. The HSV color model is used, its color components are efficiently analyzed and treated separately so that the proposed algorithm can adapt to different environmental illumination condition and shadows. Polar and linear statistical operations are used to calculate the background from the video frames. The experimental results show that the proposed background subtraction method can automatically segment video objects robustly and accurately in various illuminating and shadow environments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.560-567
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• 1998

This paper concerned about a imputed color printed material image in camera to decrease noise and distortion by processing median filtering with input image to identical condition. Also this paper proposed the way of compares a normal printed material with an abnormal printed material color tone with trained a learning of the error back-propagation to block classification by extracting five place from identical block(3${\times}$3) of color printed material R, G, B value. As a representative algorithm of multi-layer perceptron the error Back-propagation technique used to solve complex problems. However, the Error Back-propagation is algorithm which basically used a gradient descent method which can be converged to local minimum and the Back Propagation train include problems, and that may converge in a local minimum rather than get a global minimum. The network structure appropriate for a given problem. In this paper, a good result is obtained by improve initial condition and adjust th number of hidden layer to solve the problem of real time process, learning and train.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.1
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• pp.103-112
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• 2000

The color space transformation to link device-dependent color spaces and device-independent color spaces is essential for device characterization and cross-media color reproduction. There are various color conversion methods such as regression, 3D interpolation with LUT(look-up table), and neural network. In the color transformation with these methods, the conversion accuracy is essentially based on the sample data to be exploited for device characterization. In conventional method, color samples are uniformly selected in device-dependent space such as CMY and RGB. However, distribution of these color samples is very non-uniform in device-independent color space such as CIEL*a*b*. Accordingly, the conversion error in device-independent color space is irregular according to the distribution of the samples. In this paper, a color sampling method based on equi-visual perception is proposed to obtain approximate uniform color samples in CIEL*a*b* space. In order to evaluate transformation accuracy of proposed method, color space transformations are simulated using regression, 3D interpolation with LUT and neural network techniques, respectively.

• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.1
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• pp.11-26
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• 2007

In this paper, an experiment was done where the input device used the linear multiple regression and the sRGB color space to perform a color transformation. The output device used the GOG, GOGO and sRGB for the color transformation. After the input device underwent a color transformation, a $3\;{\times}\;20\;size$ matrix was used in a linear multiple regression and the scanner's color representation of scanner was better than a digital still camera's color representation. When using the sRGB color space, the original copy and the output copy had a color difference of 11. Therefore it was more efficient to use the linear multiple regression method than using the sRGB color space. After the input device underwent a color transformation, the additivity of the LCD monitor's R, G and B signal value improved and therefore the error in the linear formula transformation decreased. From this change, the LCD monitor with the GOG model applied to the color transformation became better than LCD monitors with other models applied to the color transformation. Also, the color difference varied more than 11 from the original target in CRT and LCD monitors when a sRGB color transformation was done in restricted conditions.