• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.487-493
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• 2007

컴퓨터에서 사용되는 2D 이미지는 크게 비트맵과 벡터의 두 가지 표현 방식이 존재한다. 일반적으로 사용되는 이미지와 텍스처는 대부분 비트맵을 기반으로 하고 있으며, 이에 따라 많은 텍스처 합성에 관한 연구 또한 비트맵 기반으로만 이루어져 왔다. 그러나 일부 분야들에서는 몇 가지 단점에도 불구하고 벡터 형식의 이미지를 선호하고 있으며, 비트맵이 가지지 못한 장점들과 현재의 충분한 컴퓨터 연산 능력을 고려해 볼 때 벡터 이미지의 필요성과 활용분야는 앞으로도 늘어날 것이라 생각된다. 이에 따라 본 논문에서는 벡터 형식으로 주어진 텍스처 패턴을 분석, 합성하는 새로운 방법을 제안한다. 입력 받는 벡터 이미지는 몇 가지의 속성을 지닌 스트로크(Stroke)들의 집합으로서, 각각의 스트로크는 비트맵에서의 픽셀과 같이 기본적인 분석과 합성의 단위가 된다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.15-26
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• 2012

Recently, due to applicability increase of vector data based digital map for geographic information and evolution of geographic measurement techniques, large volumed GIS(geographic information service) services having high resolution and large volumed data are flowing actively. This paper proposed an efficient vector map compression technique using the SEC(spatial energy compaction) based on classified bins for the vector map having 1cm detail and hugh range. We encoded polygon and polyline that are the main objects to express geographic information in the vector map. First, we classified 3 types of bins and allocated the number of bits for each bin using adjacencies among the objects. and then about each classified bin, energy compaction and or pre-defined VLC(variable length coding) were performed according to characteristics of classified bins. Finally, for same target map, while a vector simplification algorithm had about 13%, compression ratio in 1m resolution we confirmed our method having more than 80% encoding efficiencies about original vector map in the 1cm resolution. Also it has not only higher compression ratio but also faster computing speed than present SEC based compression algorithm through experimental results. Moreover, our algorithm presented much more high performances about accuracy and computing power than vector approximation algorithm on same data volume sizes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.88-98
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• 2011

Recently, according as the importance of GIS(geography information system) database security is embossed, much researches had been achieved about GIS network security. But most such researches are weak against sourceful illegal reproductions and distributions of GIS vector data map. In this paper, we proposed an efficient layer unit contents based partial encryption technique in the vector map compression domain to prevent illegal distributions and unauthorized accesses. This method achieves a partial encryption about each central coordinate and directional parameters of a MCA(minimum coding attribute) that is created at the vector map compression processing in the vector space. First, the position encryption is applied as permutating randomly the center coordinate of each record that is minimum unit of vector map shape. And second, the direction encryption that changing shapes of vector map topography is applied as encrypting the direction of vertices's coordinates of each record. In experimental results, we confirmed that our proposed method can encipher the large volumed vector map data effectively in low computational complexity. Also, we could minimize the decline of compression efficiency that occurred by conventional contents based encryption schemes using AES or DES algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.288-293
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• 2002

It is possible to classify image data into two types according to the internal representation: one is bitmap, the other is vector. A bitmap image is represented by the two dimensional pixels whereas a vector image is represented by mathematical functions to draw vector objects such as line, rectangle and circle on the two or three dimensional space. So it is necessary for users to use a individual application program for each different image. In this paper, we present a method for design and implementation of image editing tool based on combining of bitmap and vector image.

• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.210-218
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• 2011

Generally, GIS digital map has been represented and transmitted by ASCII and Binary data forms. Among these forms, Binary form has been widely used in many GIS application fields for the transmission of mass map data. In this paper, we present a hierarchical compression technique of polyline and polygon components for effective storage and transmission of vector map with various degree of decision. These components are core geometric components that represent main layers in vector map. The proposed technique performs firstly the energy compaction of all polyline and polygon components in spatial domain for the lossless compression of detailed vector map and compress independently integer parts and fraction parts of 64bit floating points. From experimental results, we confirmed that the proposed technique has superior compressive performance to the conventional data compression of 7z, zip, rar and gz.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.418-429
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• 2013

This paper presents a GIS vector map data compression scheme based on hybrid polyline simplification method and SEC(spatial energy compaction). The proposed method extracts all layers which contain polylines in the GIS vector map and compress all polylines in extracted layers by the hybrid polyline simplification and SEC based on MAE(minimum area error) for each segment in the line. The proposed simplification and SEC increase the compression ratio while preserving the shape quality. We analyze the visual aspects and compression efficiency between the original GIS vector map and the compressed map. From experimental results, we verify that our method has the higher compression efficiency and visual quality than conventional methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.203-205
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• 2020

본 논문에서는 게임 엔진에서 사용되는 노말맵(Normal Map)의 원리와 그 응용 방식에 대해 연구하였다. 노말맵은 게임에서 하이 폴리곤 모델링에 적용되는 조명 적용 데이터를 로우 폴리곤에 적용할 수 있는 기술로, 하이 폴리곤 모델링의 벡터 방향 데이터를 텍스쳐로 저장하여 로우 폴리곤에 적용해서 벡터 방향을 텍셀단위로 조정할 수 있게 한다. 여기에서는 게임에서의 노말맵 저장 방식과 연산 방식에 대해 소개하고 이를 응용하여 최적화 시킬 수 있는 방법에 대해 연구한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9C
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• pp.859-868
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• 2006

H.264에서 다중참조 프레임을 사용한 움직임 예측 방법은 단일 참조프레임을 이용한 움직임 예측보다 더 많은 시간적 중복성을 제거하여 부호화 효율을 높이거나 채널에러에 강인하게 부호화하기 위해 사용된다. 하지만 다중 참조 프레임을 이용하여 움직임 예측을 하는 것은 단일의 참조 프레임을 이용하는 것보다 많은 계산량을 요구하기 때문에 비디오 인코더의 복잡도를 증가시키게 된다. 본 논문에서는 다중참조 프레임을 사용한 움직임 예측을 화질 열화 없이 적은 복잡도로서 가능하게 하는 알고리즘을 제안한다. 움직임 예측 절차의 복잡도를 줄이기 위해, 제안한 알고리즘에서는 연속되는 프레임 사이에 구성된 움직임 벡터맵을 이용하여 움직임벡터를 추정한다. 제안한 방식은 추정된 움직임벡터를 작은 탐색영역에서 보정하는 방식을 적용하기 때문에 기존의 방식들에 비해 적은 복잡도가 요구된다. 제안된 방법으로 추정된 움직임벡터는 각 참조프레임들에 대해 최적의 움직임 벡터를 효과적으로 추적하기 때문에 부호화 된 영상의 화질은 전 탐색영역 움직임 예측 알고리즘을 이용한 결과와 매우 비슷하다. 제안된 방식은 세가지 단계로 구성된다. (a) 연속되는 두 개의 프레임 사이에 벡터맵을 구성한다. (b) 벡터맵에 있는 요소벡터를 이용하여 시간적 움직임 벡터를 구성한다. (c) 마지막으로, 임시 움직임 벡터를 좁은 탐색영역에서 보정한다. 컴퓨터 실험을 통해 제안된 방식의 효율성을 입증하였다. 제안된 방식과 기존의 방식들과의 비교를 위해 H.264 부호화기에서 움직임 예측 모듈에 의해 소비된 CPU 시간을 측정하였다. 컴퓨터 실험을 통해 알 수 있듯이 제안된 방식에 의해 부호화된 영상의 화질은 기존 방식과 을 통해 얻은 영상화질과 거의 같으면서 알고리즘 복잡도는 크게 줄어드는 것을 볼 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.165-168
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• 2001

It is possible to classify image data into two types according to the internal representation: one is bitmap, the other is vector. A bitmap image is represented by the two dimensional pixels whereas a vector image is represented by mathematical functions to draw vector objects such as line, rectangle and circle on the two or three dimensional space. So it is necessary for users to use a individual application program for each different image. In this paper, we present a method for designing and implementation of image editing tool based on combining of bitmap and vector image.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.507-512
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• 2001

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.