• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.233-234
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• 2009

본 논문에서는 움직임 추정을 위한 탐색 영역내의 스캔 방법을 움직임 벡터가 나올 확률에 근거하여 가변적으로 적용하여 불필요한 후보 블록을 건너뛰고 탐색 영역 안에서의 블록 정합을 PDE(partial distortion elimination) 기반으로 하여 고속 블록 매칭이 가능한 알고리즘을 제안한다. 제안한 방법은 기존의 방법보다 불필요한 계수를 효율적으로 제거하기 위하여 탐색 영역 안에서 움직임 벡터가 존재 할 확률이 가장 높은 영역은 모두 검색하고, 움직임 벡터가 존재할 확률이 낮은 영역은 가로 세로 각각 한 픽셀 건너뛰어서 블록 정합하고 만약 현재의 최소 비용보다 낮은 비용을 가지는 위치가 존재한다면 가로 세로 이웃한 4개의 화소를 추가적으로 정합하여 계산 비용을 효율적으로 감소시키면서 정확도를 높이도록 하였다. 제안한 알고리즘은 극히 낮은 화질 저하를 가지며, 기존의 전역 탐색 알고리즘에 비해 약 85% 이상의 계산 비용 감소가 있어 비디오 압축 응용 분야에 유용하게 사용될 수 있을 것이다.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.699-712
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• 2014

In this paper, we propose a new Adaptive Search Range (ASR) decision algorithm for accelerating GPU-based Integer-pel Motion Estimation (IME) of High Efficiency Video Coding (HEVC). For deciding the ASR, we classify a frame into two models using Motion Vector Differences (MVDs) then adaptively decide the search ranges of each model. In order to apply the proposed algorithm to the GPU-based ME process, starting points of the ME are decided using only temporal Motion Vectors (MVs). The CPU decides the ASR as well as the starting points and transfers them to the GPU. Then, the GPU performs the integer-pel ME. The proposed algorithm reduces the total encoding time by 37.9% with BD-rate increase of 1.1% and yields 951.2 times faster ME against the CPU-based anchor. In addition, the proposed algorithm achieves the time reduction of 57.5% in the ME running time with the negligible coding loss of 0.6%, compared with the simple GPU-based ME without ASR decision.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1254-1259
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• 2010

In this paper, Low Computational Adaptive Expanded Block Search Motion Estimation Method is proposed. Proposed method classifies ME blocks as Average Motion Block(AMB) and Local Motion Block(LMB) according to correlation of reference frame. It could reduce the computational complexity with performing Modified Fast Search(MFS). And accuracy of MV is also increased by 4 sub-blocks on LMB and Block Expansion(BE). The experimental results show that the proposed method has better performance that increased 1.8dB than Diamond Search and 0.6dB than Full Search with 7.5 % computation of Full Search. The proposed method could be applied to video compression and Frame Rate Conversion(FRC).

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.357-371
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• 2005

In this Paper, we propose a new motion search algorithm called CDTS (Cross-Diamond-Triangle Search algorithm) that uses optimal search pattern according to the position of a search area to improve the performance of CDS(Cross-Diamond Search algorithm) as well as CDHSs(Cross-Diamond-Hexagonal Searches algorithms). We analyze motion distributions in various test video sequences to apply optimal search pattern according to a position of search area. Based on the result of this analysis, we propose a new triangle-shaped search pattern whose structure is asymmetric while previous search patterns are generally symmetric in conventional algorithms. In CDTS, we apply cross- and diamond-shaped search patterns to central search areas, and triangle- and diamond-shaped patterns to the other areas. Applying CDTS to test video sequences, the proposed scheme can reduce search points more than CDS and CDHSs by 16.22$\%$ and 3.09$\%$, respectively, without any visual quality degradation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.524-527
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• 1997

Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.2
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• pp.192-197
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• 2001

Recent requirements for the fast and accurate motion in industrial robot manipulators need more advanced control techniques. To satisfy the requirements, importance of the force control is being continuously increased and the expensive force sensor is often installed to obtain the contact force information in practice. This information is indispensable for the force control of maintaining the desired contact force. However, the sensor cost is too high to be used in industrial applications. In this paper, it is proposed to estimate the contact force occurred between the end-effector of robots and environment in 3-D. The contact force monitoring system is developed based on the static and dynamic models of 3 DOF robot manipulators, where the contact force is described with respect to the link torque. The Extended Kalman Filter is designed and its performance is verified in simulations.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.178-184
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• 2013

In this paper, we propose an efficient parallelization technique of PU-level motion estimation (ME) in the next generation video coding standard, high efficiency video coding (HEVC) to reduce the time complexity of video encoding. It is difficult to encode video in real-time because ME has significant complexity (i.e., 80 percent at the encoder). In order to solve this problem, various techniques have been studied, and among them is the parallelization, which is carefully concerned in algorithm-level ME design. In this regard, merge estimation method using merge estimation region (MER) that enables ME to be designed in parallel has been proposed; but, parallel ME based on MER has still unconsidered problems to be implemented ideally in HEVC test model (HM). Therefore, we propose two strategies to implement stable parallel ME using MER in HM. Through experimental results, the excellence of our proposed methods is shown; the encoding time using the proposed method is reduced by 25.64 percent on average of that of HM which uses sequential ME.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.41-56
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• 2000

In this paper, we propose a two-stage fast block matching algorithm using the mean absolute error (MAE) of neighbor search point that can reduce the computational complexity to estimate motion vector while the motion estimation error performance is nearly the same as full search algorithm (FSA) In the proposed method, the lower bound of MAE 6at current search point IS calculated using the MAE of neighbor search point And we reduce the computational complexity by performing the block matching process only at the search point that has to be block matched using the lower bound of MAE The proposed algorithm is composed of two stages The experimental results show that the proposed method drastically reduces the computational complexity while the motion compensated error performance is nearly kept same as that of FSA.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2762-2770
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• 2012

In this paper, we propose a novel approach to effectively compress the sub-images transformed from the picked-up elemental images in integral imaging, in which motion vectors of the object in each sub-image are fast and accurately estimated and compensated by combined use of MSE(mean square error)-based TSS(tree-step search) and FS(full search) schemes. This is, the possible object areas in each sub-image are searched by using the fast TSS algorithm in advance, then the these selected object areas are fully searched with the accurate FS algorithm. Furthermore, the sub-images in which all object's motion vectors are compensated, are transformed into the residual images by using the difference image method and finally compressed with the MPEG-4 algorithm. Experimental results reveal that the proposed method shows 214% improvement in the compression time per each image frame compared to that of the conventional method while keeping the same compression ratio with the conventional method. These successful results confirm the feasibility of the proposed method in the practical application.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.163-166
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• 2012

본 논문에서는 차세대 비디오 표준인 High Efficiency Video Coding(HEVC)의 영상 부호화 과정의 시간복잡도 감소를 위한 효율적인 Prediction Unit(PU)레벨 움직임예측(Motion Estimation, ME) 병렬화의 구현 기법을 제시하고자 한다. 움직임예측 과정은 부호화기에서 80%의 복잡도를 차지하는 과정으로 고속 부호화의 걸림돌이 되고 있다. 이를 해결하기 위한 방법으로 제안된 것이 움직임예측 알고리즘의 병렬화이다. 알고리즘 수준에서 ME 의 일부인 Merge Estimation 의 병렬화를 위해서 Merge Estimation Region (MER)기반의 ME 방법이 제안되었다. 하지만 HEVC Test Model reference software(HM)에 반영된 MER 을 이용하여 실제로 병렬화된 ME 를 구현하는 과정에서는 알고리즘 측면에서 아직 고려되지 않은 문제들이 존재한다. 이에 본 논문에서는 MER 을 사용한 안정적인 병렬 ME 를 구현하기 위한 전략으로 각 PU 의 정보를 독립적으로 사용하기 위한 부분 순차화 방법과 메모리 접근제한을 이용한 병렬화 방법을 제시한다. 실험을 통해 본 연구의 우수성이 확인되었는데, 제안된 방법에 기반을 둔 구현에서 순차적인 ME 를 이용한 부호화기 대비 평균 25.64%의 전체 부호화 과정 시간의 감소가 나타났다.