• The Journal of the Korea Contents Association
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• v.6 no.10
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• pp.143-152
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• 2006

JPEG2000, the standard of still image compression based on wavelet, will be widely used. One of the greatest characteristics of JPEG2000 is to offer ROI(Region-Of-Interest) coding. This is to compress with high quality the region that the user wants better than the other region. JPEG2000 and ROI have different parameters, which are tile size and ROI size, wavelet filter type and ROI shape and its location, codeblock size and number of ROI, number of DWT decomposition level and ROI importance, and number of quality layer and low resolution sub-band importance. In this paper, we shows the correlation of the parameters and ROI coding methods through experiments. This helps an application select the parameters and the methods to meet the application.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.254-255
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• 2014

본 논문에서는 중요도 지도에 기반한 관심 영역 동영상 압축 방법에 대해 고찰한다. 동영상 압축은 손실 프로세스이기 때문에 관심 영역에서의 정보 손실 최소화가 필요하며, 이를 위해 중요도 감지 과정에서 추출되는 중요도 지도의 신뢰도가 중요하다. 따라서 다양한 다른 기법의 중요도 지도 적용 결과를 비교함으로써 중요도 지도 추출 알고리즘의 요건에 대해 추론하고, 추출된 중요도 지도를 이용하여 적절하게 동영상을 부호화하는 방법에 대해 제안한다. 마지막으로 실험결과를 통해 보완되어야 할 부분을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.197-206
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• 2006

One of the most significant characteristics of JPEG2000. the emerging still image standards. is the ROI (Region of Interest) coding. JPEG2000 provides a number of ROI coding mechanisms and ROI parameters. To apply them to an application, it must select the applicable values. In this paper, we evaluate how the ROI coding mechanisms and the ROI parameters influencing JPEG2000 qualify affect the ROI quality and the whole image quality. The ROI coding mechanisms are Maxshift and Implicit. and the parameters are tile size and ROI size, codeblock size, number of DWT decomposition levels and ROI importance. The bigger the tile size, the better the quality. The bigger the ROI size, the ROI importance and the number of DWT decomposition levels, the worse the qualify. In code block $32{\times}32$ of Maxshift and Implicit, it has the best qualify.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.634-636
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• 2003

본 논문은 마스크 방식의 관심 영역(ROI, Region Of Interest) 부호 설계와 구현에 대하여 제시한다. 관심 영역에 대한 정지 영상 압축 알고리즘은 웨이블릿 변환과 사용자가 지정한 관심 영역을 결합하여 설계하였다. 즉, 사용자가 지정한 관심 영역을 이용하여 관심 영역 마스크를 생성한다. 양자화 과정에서 웨이블릿 계수들을 각 레벨과 서브밴드로 구분하고 생성된 관심 영역 마스크 정보를 이용하여 양자화 과정을 처리하여 부호화한다. 관심 영역에 대하여서는 높은 영상 품질과 그리고 전체 영상에 대하여서는 높은 압축을 동시에 실현시킬 수 있는 마스크 방식의 관심 영역 부호화 알고리즘을 설계하고 구현하였다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.31-39
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• 2010

In some image processing system or the users who want to see a specific region of image simply, if a part of the image has higher quality than other regions, it would be a nice service. Specifically in mobile environments, preferential service was needed, as the screen size is small. So, JPEG2000 supplies this function. But this doesn't support the process to extract specific regions or service and does the functions to add some techniques. It is called by ROI(Region-of-Interest). In this paper, we use images including human faces, which are processed most preferentially and compressed with high quality. Before an image is served to the users, it is compressed and saved. Here, the face parts are compressed with higher quality than the background which are relatively with lower quality. This technique can offer better service with preferential transferring of the faces, too. Besides, whole regions of the image are compressed with same quality and after searching the faces, they can be preferentially transferred. In this paper, we use a face extraction approach based on neural network and the preferential processing with EBCOT of JPEG2000. For experimentation, we use images having several human faces and evaluate objectively and subjectively, and proved that this approach is a nice one.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.321-328
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• 2007

In this paper, we propose a fast and dynamic Region-of-Interest coding method using the adaptive code-block discrimination algorithm in JPEG2000 images which complements the implicit ROI coding method and the modified implicit ROI coding method. For reducing the time of discriminating the code block, the proposed method estimates the characteristics of the shape of ROI and makes the shape of boundaries, and classifies the patterns of each code block. The method improves the preferred processing and loss of wavelet coefficients of background within the ROI code blocks by adaptively classifying the code blocks with the percentage of content of the wavelet coefficients using the thresholds of ROI and background. Also, the priority control of wavelet coefficients of background within ROI code block supports the rapid ROI coding by processing in batch based on patterns unlike the existing methods that process with unit of wavelet coefficients. To show the usefulness of this method, we compared this to the existing methods. There is no difference in performance, but we confirmed very speedy in processing time.

• Journal of the Korea Computer Graphics Society
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• v.17 no.2
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• pp.17-26
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• 2011

This paper presents an interactive progressive image transmission method, which enables a remote user to interactively select and transmit preferred regions from an index image. Our enhanced quadtree decomposition using PSNR-based rules and new implicit quadtree coding provide better rate-distortion performance than previous quadtree coders as well as leading bit plane methods. An adaptive traversal of child nodes is introduced for better visual display of restored images. Depth-first traversal combined with breadth-first traversal of the quadtree to accomplish interactive transmission as presented, results in a method that provides competitive performance at a low level of computational complexity. Moreover, our decoding requires only simple arithmetic which is enabling our method to be used for real-time mobile applications.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.142-145
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• 2014

동영상에서의 객체 추적 알고리즘에 대한 활발한 연구가 진행되고 있음에도 불구하고 실시간 객체추적을 위해서는 여전히 정확도, 복잡도 등에서의 성능향상이 필요하다. 압축영역 기반 방식에서는 전역 움직임 보상(GMC : Global Motion Compensation)과정을 거쳐 추적하려는 객체와 배경을 구분한다. 전역 움직임 보상방법은 프레임 전 영역을 대상으로 하는 연산으로 전체 추적 시스템에서 차지하는 복잡도가 높다. 본 논문은 관심영역(ROI : Region Of Interest) 기반 전역 움직임 보상방법을 이용한 ST-MRF(Spatio-Temporal Markov Random Field)기반 추적기 고속화 방법을 제안한다. 관심영역을 기반으로 전역 움직임 보상을 적용함으로써 객체와 배경을 분리할 뿐만 아니라 알고리즘의 복잡도를 효과적으로 줄일 수 있다. 제안하는 방법의 추적성능은 평균 precision 87.29%, recall 82.58%, F-measure 83.78%로 기존방법과 비교하여 약 1%의 차이를 유지하였으며 전체 시스템의 수행시간은 평균 29.95ms로 기존방법과 비교하여 1.74배의 속도향상을 보였다.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1341-1348
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• 2012

In this paper, an efficient scheme for correcting rotated objects in medical images using the Mojette transform is presented. The Mojette transform is a kind of discrete Radon transform, where the transform domain is represented by a set of projections. The Mojette transform currently studied in the image compression area is modified for detecting the rotation angle of objects in medical images. First, in order to find accurate rotation angle, the projection value in the Mojette transform is determined by using pixels on the projection line and in addition the linear interpolation of pixels adjacent to the line. Second, at each projection angle, only one projection is implemented for reducing the amount of the calculation in the process of the Mojette transform. Finally, the projection in the Mojette transform is carried out at the predetermined ROI(Region Of Interest) at which the objects are not cropped or added by rotating the image. The simulation results show that the proposed method has good performance for correcting the rotation angle in medical images.