• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.541-550
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• 1994

A true color image is needed many data on the occasion of the transmission and storage. Therefore, we want to describe color image by a minority data without unreasonableness at eyesight. In this paper, it is presented 256 colormap construction method in RGB, YIQ/YUV space and common colormap expression method at merge between colormaps by reason of dissimilar original color image to display at a monitor for each other colormap at the same time. In comparison with processed result in RGB, YIQ/YUV space, it was measured by PSNR, standard variation, and edge preservation rate using sobel operator. Process time is 3second in colormap construction and 2second in merge between colormaps. In the PSNR value, RGB space has higher 0.15, 0.34 on an average than YIQ and YUV spae. Standard variation has lower in 0.15, 0.41 on an average than Yiq and YUV space. But in the data compression, YIQ/YUV space have about 1/3 compression efficiency than RGB space by reason of use to only 4bit of 8bit in color component.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.529-536
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• 2015

When rendering both three-dimensional objects and photo images together, the non-photorealistic rendering results are in visual discord since the two contents have their own independent color distributions. This paper proposes a non-photorealistic rendering technique which renders both three-dimensional objects and photo images such as cartoons and sketches. The proposed technique computes the color distribution property of the photo images and reduces the number of colors of both photo images and 3D objects. NPR is performed based on the reduced colormaps and edge features. To enhance the natural scene presentation, the image region segmentation process is preferred when extracting and applying colormaps. However, the image segmentation technique needs a lot of computational operations. It takes a long time for non-photorealistic rendering for large size frames. To speed up the time-consuming segmentation procedure, we use GPGPU for the parallel computing using the GPU. As a result, we significantly improve the execution speed of the algorithm.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.159-162
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• 2003

컬러 특징을 나타내는 전형적인 방법으로 RGB 컬러히스토그램이 있다. RGB 컬러 히스토그램은 RGB 컬러 공간을 일련의 bin으로 나누고 각 bin의 컬러 값에 대응하는 영상 픽셀의 빈도 수를 카운트하는 것으로 영상 검색에서 널리 쓰이고 있다. 그러나 RGB 컬러히스토그램은 영상의 미세한 장면 변화에도 크게 달라지기 때문에 검색 결과에 많은 영향을 줄 수 있다. 본 논문에서는 RGB 컬러히스토그램의 이러한 특성을 개선할 수 있는 새로운 컬러 특징 표현 방법을 제안한다. 제안하는 방법은 RGB 컬러 공간으로부터 RG, GB, BR의 세 가지 평면으로 투영시킨 2차원 투영 맵(2D Projection Maps)을 사용한다. 영상 검색에 대한 실험 결과로부터 제안한 2차원 투영 맵이 RGB 컬러히스토그램보다 더 향상된 검색 효율을 나타내었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.230-232
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• 2013

본 논문은 에지맵 기반의 삼차 스플라인 보간 방법을 이용한 컬러 디모자이킹 방법을 제안한다. 삼차 스플라인 보간 방법은 데이터 집합에 부드러운 곡선을 만드는 방법으로, 부드러운 영상을 만들기에 적합하다. 상, 하, 좌, 우 총 4가지 방향에 대하여 각각의 예측치를 얻어내고, 각 컬러 채널의 에지맵을 이용하여 최종적인 값을 보간한다. 실험결과에서는 기존의 보간 방법보다 주관적 화질과 Peek Signal to Noise Ratio(PSNR) 측면에서 더 나은 성능을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.83-93
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• 2012

This paper proposes a method to efficiently encode multi-view color videos and depth maps. The proposed coding method for multi-view color videos and depth maps can improve the coding efficiency by additional inter-view prediction, as well as inter-frame prediction. By means of the proposed method, we achieved the coding gain of approximately 55% for 2-view color videos and approximately 12% for 2-view depth maps. For 3-view case, we found that the proposed system yields 54% of coding gain from outer view color videos and 56% of coding gain from center view color videos, respectively. Moreover, for 3-view depth map case, approximately 11% of coding gain from outer view and 13% of coding gain from center view are obtained with the proposed coder, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.8-17
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• 2011

Recently, fusion camera systems that consist of depth sensors and color cameras have been widely developed with the advent of a new type of sensor, time-of-flight (TOF) depth sensor. The physical limitation of depth sensors usually generates low resolution images compared to corresponding color images. Therefore, the pre-processing module, such as camera calibration, three dimensional warping, and hole filling, is necessary to generate the high resolution depth map that is placed in the image plane of the color image. However, the result of the pre-processing step is usually inaccurate due to errors from the camera calibration and the depth measurement. Therefore, in this paper, we present a depth map upsampling method robust these errors. First, the confidence of the measured depth value is estimated by the interrelation between the color image and the pre-upsampled depth map. Then, the detailed depth map can be generated by the modified kernel regression method which exclude depth values having low confidence. Our proposed algorithm guarantees the high quality result in the presence of the camera calibration errors. Experimental comparison with other data fusion techniques shows the superiority of our proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.91-97
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• 2013

Image and it's processing techniques are widely applied and very important in the recent IT environment. In this paper, we try to reconstruct original BMP(Bitmap) image into indexed image and codebook utilizing vector quantization and represent high quality image only with same pixel depth of previous indexed image like JPEG etc. That is, By adopting common map method onto index image with $2^n$ color codebook, image can be represented as high quality as $2^{n+1}$ color codebook. When proposed output image is compared with original BMP image, it provides as much around 2dB as higher PSNR than conventional 8 bit index image(normal JPEG). Furthermore, this improvement(2 dB higher PSNR) could be provided when using the 9 bit indexed image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.325-327
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• 2012

최근 국내외 디지털 가전 업체들은 다양한 3D 기술을 앞세워 가정 내에서도 편하게 즐길 수 잇도록 다양한 3DTV를 출시하고 있다. 이러한 3DTV에서 입체영상을 시청하기 위해서는 입체콘텐츠가 제작되어 전송되어야 한다[1]. 이러한 입체 콘텐츠는 RGB 영상과 깊이맵을 이용하여 생성할 수 있는데, 이때 깊이맵은 사용자의 용도에 따라 다양한 형태로 변환될 수 있다. 최근엔 이러한 깊이맵과 3D 영상의 컬러를 변환하여 지각 깊이감을 개선하는 영상처리 기술에 대한 관심이 높아지고 있다. 이에 따라, 본 논문에서는 기존의 컬러 변환을 통한 2D 영상의 지각 깊이감 개선을 입체영상에 적용하여, 3D 지각 입체감을 동시에 향상시키는 방법을 제안한다. 이를 위해 대조 변환 및 배경 다크닝 방법을 제안하고, 실험을 통해 제안 방법이 상기 목적을 얻을 수 있는 것을 검증하였다.

• The Research Journal of the Costume Culture
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• v.20 no.6
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• pp.869-882
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• 2012

The purpose of this study is to obtain the patent for extracting the color map and color chip from the color image source and to develop color image map for fashion design. For this study, fashion image maps were produced from 210 pictures with Adobe Photoshop CS2 program targeting 200 university students from 2004 to 2006. The procedures for extracting the color map and color chip included providing the color image, the filtering phase, the segmentation phase, the extraction phrase, and the arrangement phase. Based on the results of this study, patent application was made to KIPO(Korean Intellectual Property Office) for this invention. The following effects can be expected from the standpoint of design based on the case study. First, it is a straight forward procedure to extract a color chip and color map from a color image. Second, it can be applied to various art works based on the recombination of colors as representative colors can be extracted from the related color image that combines a variety of colors. Third, desired colors can be selected based on the taste cluster classification or sensibility axis of design by extracting the representative color from the color image.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1597-1599
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• 2015

본 논문에서는 곡물이나 광석 등의 원료들 중에서 양품 및 불량품을 검출하기 위해, Color CCD 카메라로 촬영한 원료영상에서 Mean-Shift 클러스터링 알고리즘과 단계별 병합 방법을 제안하고 있다. 먼저 원료 학습 영상에서 배경을 제거하고 영상 색 분포정도를 기준으로 모폴로지를 이용하여 영상의 전경맵을 얻는다. 전경맵 영상에 대해서 Mean-Shift 군집화 알고리즘을 적용하여 영상을 N개의 군집으로 나누고, 단계별로 위치 근접성, 색상대푯값 유사성을 비교하여 비슷한 군집끼리 통합한다. 이렇게 통합된 원료 객체는 영상채널마다의 연관관계를 반영할 수 있도록 RG/GB/BR의 2차원 컬러분포도로 표현한다. 원료 객체별로 변환된 2차원 컬러 분포도에서 분포의 주성분의 기울기와 타원들을 생성한다. 객체별 분포 타원은 테스트 원료 영상데이터에서 양품과 불량품을 검출하는 임계값이 된다. 본 논문에서 제안한 방법으로 다양한 원료영상에 실험한 결과, 기존 선별방식에 비해 사용자의 인위적 조작이 적고 정확한 원료 선별 결과를 얻을 수 있었다.