• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.98-113
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• 2002

In this paper, we present advanced algorithms to reduce the computations of block matching algorithms for motion estimation in video coding. Advanced multi-level successive elimination algorithms(AMSEA) are based on the Multi-level successive elimination algorithm(MSEA)[1]. The first algorithm is that when we calculate the sum of absolute difference (SAD) between the sum norms of sub-blocks in MSEA, we use the partial distortion elimination technique. By using the first algorithm, we can reduce the computations of MSEA further. In the second algorithm, we calculate SAD adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in SAD calculation can occur early. So, the computations of MSEA can be reduced. In the third algorithm, we can estimate the elimination level of MSEA. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is a very fast block matching algorithm with nearly 100% motion estimation accuracy. Experimental results show that AMSEA are very efficient algorithms for the estimation of motion vectors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1323-1329
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• 2004

This paper presents an enhanced MSEA(multi-level successive elimination algorithm) which is a fast algorithm of the full-search motion estimation. We predict the SAD at the final level using the values of norms at the preceding levels in MSEA and then decide on whether the processing at the following levels should be proceeded or not. We skip the computation at the following levels where the processing is not meaningful anymore. Consequently, we take computational gain. For the purpose of predicting the values of SAD at each level, we first show the theoretical analysis of the value of norm at each level, which is verified by experiments. Based on the analysis a new motion estimation method is proposed and its performance is evaluated.

• Journal of the Korea Computer Industry Society
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• v.4 no.10
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• pp.761-767
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• 2003

In this paper, A Fast Multi-level Successive Elimination Algorithm (FMSEA) is presented for block matching motion estimation in video coding. Motion estimation accuracy of FMSEA is equal to that of Multilevel Successive Elimination Algorithm(MSEA). FMSEA can reduce the computations for motion estimation of MSEA by using partial distortion elimination technique. The efficiency of the proposed algorithm was verified by experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.111-118
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• 2004

In this paper, an adaptive multilevel successive algorithm is presented. The algorithm introduces an adaptive initial level scheme to the conventional multilevel successive algorithm (MSEA). It efficiently removes the unnecessary computations required for judging the invalid candidate blocks at redundant level. The simulation results show that the proposed algorithm obtains the optimal motion vector with reduced computations compared to MSEA.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.180-183
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• 2012

본 논문에서는 고속 움직임 예측(Fast Motion Estimation)방법의 일종인 다단계 연속 제거 알고리즘(MSEA : Multi-level Successive Elimination Algorithm)에 움직임의 역동성 정도를 고려하여 적응적인 가중치를 적용하는 방안에 대해 제안하였다. 움직임을 예측하는 과정에서 영상의 화질 손상이 발생하는 방식(Lossy Motion Estimation Algorithm)에서 모든 단위 블록(Macro Block)에 고정된 가중치만을 적용하는 기존의 방식과 달리 주위 블록의 움직임 벡터(Motion Vector)를 통해 움직임의 정도를 가정하여 적응적인 가중치를 적용함으로써 화질 손상을 줄이는 것이 목적이다. 제안하는 알고리즘으로 설계한 실험으로부터 MSEA에 적응적 가중치를 사용할 경우의 효율성을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.215-216
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• 2011

본 논문은 비디오 압축 알고리즘 중 움직임 예측에 해당하는 논문이다. 이와 관련하여 FS와 같은 PSNR을 유지하면서 계산량을 줄이는 SEA, MSEA 알고리즘이 제안되었다. 본 논문은 SEA 와 MSEA와 같은 알고리즘에서 이전블록의 sumnorm을 가지고 현재블록의 합과 차이를 구하여서 낮은 순으로 탐색 지점을 탐색하는 방법을 제안한다. 이 방법으로 SADmin을 빨리 찾게 되서 후보 탐색지점들을 높은 확률로 제거함으로써 계산량을 줄이는 알고리즘을 제안한다.

• Journal of Broadcast Engineering
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• v.12 no.3
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• pp.278-288
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• 2007

We propose the fast full search algorithm that reduces the computational load of the block matching algorithm which is used for a motion estimation in the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion picture. However we proposed the efficient motion estimation method which is good for the fast and slow motion picture. Firstly, when finding the initial threshold value, we use the expanded predictor that can approximately calculate minimum threshold value. The proposed algorithm estimates the motion in the new search order after partitioning the search window and adapt the directional search order in the re-divided search window. At the result, we can check that the proposed algorithm reduces the computational load 94% in average compared to the conventional spiral full search algorithm without any loss of image quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.9-15
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• 2007

We propose the fast full search algorithm that reduces the computation of the block matching algorithm which is used for motion estimation of the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion pictures. However the proposed method is good for the fast and slow motion because it estimates the motion in the new search order after partitioning the search window. Also, when finding the motion vector, this paper presents the method that reduces the complexity by computing the matching error in the order which is determined by local image complexity. The proposed algorithm reduces the computation up to 99% for block matching error compared with the conventional spiral full search algorithm without any loss of image quality.