• 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.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.355-364
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• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.779-782
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• 2002

영상 압축 분야에서 데이터의 압축이 필수적인데, 이때 가장 많은 중복성을 가지고 있는 시간적 중복성은 이전 프레임의 데이터를 이용하여 움직임 추정과 움직임 보상을 수행하고 추정된 움직임 벡터에 의해서 보상된 영상과 원 영상과의 차 신호를 부호화하여 데이터를 압축한다. 움직임 추정과 움직임 보상 기법은 비디오 영상압축에서 중요한 역할을 하지만 많은 계산량으로 인하여 실시간 응용 및 고해상도 응용에 많은 어려움을 가지고 있다. 만일 움직임 추정을 하기 전에 블록의 움직임을 예측할 수 있다면 이를 바탕으로 탐색 영역에서 초기 탐색점의 위치 및 탐색 패턴을 결정할 수 있다. 본 논문에서는 움직임 벡터의 높은 공간적 상관성을 이용하여 초기 탐색점의 위치와 탐색 패턴을 결정함으로써 적응적으로 움직임 벡터를 추정하는 새로운 기법을 제안하고 성능을 평가한다. 실험을 통하여 제안된 알고리즘은 계산량의 감소에 있어서 높은 성능 향상을 보였다.

• 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 Information Science Society Conference
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• 2002.04a
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• pp.550-552
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• 2002

표준 비디오 압축 기법은 프레임 간의 시간적, 공간적 중복성을 이용하여 데이터를 압축한다. 압축된 프레임이 인터넷을 통하여 전송되는 중에 네트워크의 상태에 따라 패킷의 손실이 발생할 수 있다. 이러한 오류가 발생하면 손실된 블록을 포함하는 프레임의 화질 뿐 아니라, 이 프레임을 참조하여 압축된 이후의 프레임에도 영향을 미친다. 본 논문에서는 전송오류가 발생한 경우, 디코더에서의 오류전파를 최소화하기 위해 보조 모션벡터를 이용한 압축 기법을 제안한다. 즉, 여러 개의 후보 프레임 중 현재 압축하려는 매크로블록과 가장 가까운 값을 가진 두 개의 매크로 블록을 선택하여 그 위치를 기본 모션벡터와 보조 모션벡터로 지정한다. 이때, 두 모션벡터는 서로 다른 프레임에 속한다. 디코더에서 압축해제를 위해 참조하는 기본 모션벡터의 프레임이 손실된 경우, 보조모션 벡터를 이용하여 원래의 화질에 가깝게 디코딩 할 수 있다. 이 기법은 오류제어를 위한 피드백 채널이 필요하지 않으며, 네트워크상의 전송오류 발생시 표준 H.263 압축보다 높은 성능을 보인다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.6
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• pp.515-520
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• 2004

Core-based system-on-a-chip (SoC) designs present a number of test challenges. Two major problems that are becoming increasingly important are long application time during manufacturing test and high volume of test data. Highly efficient compression techniques have been proposed to reduce storage and application time for high volume data by exploiting the repetitive nature of test vectors. This paper proposes a new test data compression technique for SoC testing. In the proposed technique, compression is achieved by partitioning the test vector set and removing repeating segment. This process has $O(n^{-2})$ time complexity for compression with a simple hardware decoding circuitry. It is shown that the efficiency of the proposed compression technique is comparable with sophisticated software compression techniques with the advantage of easy and fast decoding.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.4
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• pp.18-28
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• 1997

This paper presents image data compression using a classified vector quantization (CVQ) which categories edge blocks according to the energy distribution of subimages in the discrete cosine transform domain. Classifying the edge blocks enhances visual quality of the compressed images while maintaining a high compression ratio. The proposed classification method categories subimages into eight lypes of edge features according to an energy distribution. A neural network, trained with the data generated from the proposed classification method, can successfully classify subimages to eight edge categories. Experimental results are given to show how the (1VQ method incorporatd with a neural network can produce faithful compressed image quality for high compression ratios.

• 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 SD
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• v.39 no.12
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• pp.1045-1054
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• 2002

We present a new low power scan testing and test data compression mothod lot System-On-a-Chip (SOC). The don't cares in unspecified scan vectors are mapped to binary values for low Power and encoded by adaptive encoding method for higher compression. Also, the scan-in direction of scan vectors is determined for low power. Experimental results for full - scanned versions of ISCAS 89 benchmark circuits show that the proposed method has both low power and higher compression.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.116-121
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• 1998

본 논문에서는 데이터의존성과 벡터왜곡척도를 이용하여 개선된 칼라영상을 복호화하였다. 프랙탈 칼라영상의 복원방법은 Zhang과 Po의 벡터왜곡척도를 이용한 R, G, B 칼라 성분간의 상관관계를 고려하여 부호화한 압축파일을 사용하여 수렴될 복원영상을 독립적인 반복변환에 의해 수렴되는 영역과 데이터의존성을 갖는 영역으로 분류하여 데이터의존성 부분이 차지하는 만큼 복호화 과정에서 불필요한 계산량이 제거되었고, R 영역에서 검색한 데이터 의존영역을 G, B 영역에 그대로 사용하여 고속복호화가 가능하였다.