• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.520-524
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• 2009

In compressed color images, colors are usually represented by luminance and chrominance (YCbCr) components. Considering characteristics of human vision system, chrominance (CbCr) components are generally represented more coarsely than luminance component. Aiming at possible recovery of chrominance components, we propose a model-based chrominance estimation algorithm where color images are modeled by a Markov random field (MRF). A simple MRF model is here used whose local conditional probability density function (pdf) for a color vector of a pixel is a Gaussian pdf depending on color vectors of its neighboring pixels. Chrominance components of a pixel are estimated by maximizing the conditional pdf given its luminance component and its neighboring color vectors. Experimental results show that the proposed chrominance estimation algorithm is effective for quality improvement of compressed color images such as JPEG and JPEG2000.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.1-10
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• 2010

In this paper, we propose a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is considered during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. We propose a modified edge-based AWB method that used a pre-defined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. The proposed method shows significant improvements in terms of visual and numerical criteria when compared to conventional methods.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.77-83
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• 1999

H.263 is an international standard of ITU-T that can makes the service such as video phone, video conference in the transmission line less than 64Kbps. This recommendation draft has used motion estimation/compensation, transform coding and quantizing methods. TMN5 used for the performance estimation of H.263 has fundamentally used DCT in transform coding method and presented quantizer for quantizing the DCT transform coefficient. This paper is presenting adaptive quantizer effectively able to quantize DCT coefficient considering the human visual sensitivity while the structure of TMN5 is maintaining. As quantizer that proposed DCT-based H.263 could make transmit more frame than TMN5 in a same transfer speed, it could lower the frame drop effect. And the luminance signal appeared the difference of -0.3 ～ +0.7dB in the average PSNR for the estimation of objective image quality and the chrominance signal appeared the improvement in about 1.5dB in comparision with TMN5. As a result it can attain the better image quality compared to TMN5 in the estimation of subjective image quality.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1029-1039
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• 2012

In this papar, we propose a new motion estimation and coding technique using adaptive motion vector resolution. Currently, HEVC encodes a video using 1/4 motion vector resolution. If there are high texture regions in a picture, HEVC can't get a performance enough. So, we insert additional 1-bit flag meaning whether motion vector resolution is 1/4 or 1/8 in PU syntax. Therefore, decoder can recognize the transmitted motion vector resolution. Experimental results show that maximum coding efficiency gain of the proposed method is up to 5.3% in luminance and 7.9% in chrominance. Average computional time complexity is increased about 33% in encoder and up to 5% in decoder.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.195-200
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• 2008

To compress the moving picture data effectively, it is needed to reduce spatial and temporal redundancy of input image data. While motion estimation! compensation methods is effectively able to reduce temporal redundancy but it is increased computation complexity because of the prediction between frames. So, the study of algorithm for computation reduction and real time processing is needed. This paper is presenting quantizer effectively able to quantize DCT coefficient considering the human visual sensitivity. As quantizer that proposed DCT-based H.263 could make transmit more frame than TMN5 at a same transfer speed, and it could decrease the frame drop effect. And the luminance signal appeared the difference of $-0.3{\sim}+0.65dB$ in the average PSNR for the estimation of objective image quality and the chrominance signal appeared the improvement in about 1.73dB in comparision with TMN5. The proposed method reduces $30{\sim}31%$ compared with NTSS and $20{\sim}21%$ compared to 4SS in comparition of calculation quantity.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.733-739
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• 2012

In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.53-61
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• 2008

In this paper, we propose an algorithm for generation of multi-view video from conventional 2 dimensional video. Color and motion information of an object are used for segmentation and from the segmented objects, multi-view video is generated. Especially, color information is used to extract the boundary of an object that is barely extracted by using motion information. To classify the homogeneous regions with color, luminance and chrominance components are used. A pixel-based motion estimation with a measurement window is also performed to obtain motion information. Then, we combine the results from motion estimation and color segmentation and consequently we obtain a depth information by assigning motion intensity value to each segmented region. Finally, we generate multi-view video by applying rotation transformation method to 2 dimensional input images and the obtained depth information in each object. The experimental results show that the proposed algorithm outperforms comparing with conventional conversion methods.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.593-601
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• 2016

This paper is related to the Region-based Spectral Correlation Estimation(RSCE) coding method that makes it possible to achieve the high-compression ratio by estimating color component images from luminance image. The proposed method is composed of three steps. First, Y/C bit-plane summation image is defined using normalized chrominance summation image and luminance image, and then the Y/C bit-plane summation image is segmented for extracting the shape information of the regions. Secondly, the scale factor and the offset factor minimizing the approximation square errors between luminance image and R, B images by the each region are calculated. Finally, the scale factor and the offset factor for the each region are encoded into bit stream. Referring to the results of computer simulation, the proposed method provides more than two or three times higher compression ratio than JPEG/Baseline or JPEG2000/EBCOT algorithm in terms of bpp needed for encoding two color component images with the same PSNR.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1566-1576
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• 2007

Camera pose information from 2D face image is very important for making virtual 3D face model synchronize with the real face. It is also very important for any other uses such as: human computer interface, 3D object estimation, automatic camera control etc. In this paper, we have presented a camera position determination algorithm from a single 2D face image using the relationship between mouth position information and face region boundary information. Our algorithm first corrects the color bias by a lighting compensation algorithm, then we nonlinearly transformed the image into $YC_bC_r$ color space and use the visible chrominance feature of face in this color space to detect human face region. And then for face candidate, use the nearly reversed relationship information between $C_b\;and\;C_r$ cluster of face feature to detect mouth position. And then we use the geometrical relationship between mouth position information and face region boundary information to determine rotation angles in both x-axis and y-axis of camera position and use the relationship between face region size information and Camera-Face distance information to determine the camera-face distance. Experimental results demonstrate the validity of our algorithm and the correct determination rate is accredited for applying it into practice.