• Proceedings of the Korean Information Science Society Conference
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• 2002.04a
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• pp.397-399
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• 2002

영상압축 인코더에서 가장 많은 계산량을 차지하는 부분이 움직임 벡터를 추정하는 부분이다. 일반적인 3단계 탐색 (Three-Step Search: TSS) 방법은 한 개의 매크로 블록에 대해 25개의 탐색점에 대해서 탐색을 한다. 본 논문에서는 주변 움직임 벡터로부터 예측된 움직임 벡터를 또 하나의 탐색 점으로 추가하는 수정된 3 단계 탐색 방법을 제안한다. 제안한 방법은 기존 방법 보다 일반적으로 속도를 향상시키면서 최대 15% 정도 비트율을 감소시킬 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.99-108
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• 2008

For video compression, we have to consider two performance factors that are the search speed and coded video's quality. In this paper, we propose an enhanced fast block matching algorithm using the spatial correlation of the video sequence and the center-biased characteristic of motion vectors(MV). The proposed algorithm first finds a predicted motion vector from the adjacent macro blocks of the current frame and determines an exact motion vector using the cross pattern and a flat hexagon search pattern. From the performance evaluations, we can see that our algorithm outperforms both the hexagon-based search(HEXBS) and the cross-hexagon search(CHS) algorithms in terms of the search speed and coded video's quality. Using our algorithm, we can improve the search speed by up to 31%, and also increase the PSNR(Peak Signal Noise Ratio) by at most 0.5 dB, thereby improving the video quality.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.663-667
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• 2009

본 논문에서는 인접블록간의 움직임 유사도를 이용하여 불필요한 후보블록을 보다 빠르게 제거하는 PDE기반의 고속 블록매칭 알고리즘을 제안한다. 제안한 방법은 기존의 방법보다 불필요한 계수를 효율적으로 제거하기 위하여 인접 블록간의 영상의 유사성에 기초하여 인접한 네개의 매크로블록 가운데 최대 복잡도를 가지는 서브블록의 누적된 비율(cumulative distribution function-CDF)을 사용하고 서브블록별 복잡도가 집중되지 않도록 하기위하여 normalized 기반 매칭스캔 방법을 사용하여 효율적으로 계산량을 줄였다. 제안한 알고리즘은 화질의 저하 없이 기존의 PDE 알고리즘에 비해 60% 이상의 계산량을 줄였으며, MPEG-2 및 MPEG-4 AVC를 이용하는 비디오 압축 응용분야에 유용하게 사용될 수 있을 것이다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.7C
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• pp.726-732
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• 2003

The paper proposes a motion estimation (ME) technique to reduce computational complexity. It is achieved by skipping ME process for macro-blocks decided to be in no need of the operation. Thus, it is called ME skipping technique(MEST). In general, the ME is composed of integer pixel precision ME (IME) followed by half pixel precision ME (HME). The MEST is performed just before an IME process and makes a decision on skipping the IME process according to a criterion based on ME errors of adjacent macro-blocks (MBs) already encoded. When the IME process for a MB is decided to be skipped, which is called ME skip mode, the IME process is skipped and the integer pixel precision motion vector of the MB is just replaced by a predicted vector and used as the input of HME. On the other hands, the IME processes for MBs in ME non-skip mode are not skipped but normally performed. Accordingly, the MEST is very effective to reduce computational complexity when MBs in ME skip mode is abundant. In addition, when the MEST is applied to video encoder, it contributes to more accurate rate control and more robusaess for channel errors. It is experimentally shown that the MEST has the above advantages while maintaining good reconstructed image quality.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.77-85
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• 2006

There is the spatial correlation of the video sequence between the motion vector of current blocks. In this paper, we propose the enhanced fast block matching algorithm using the spatial correlation of the video sequence and the center-biased properly of motion vectors. The proposed algorithm determines an exact motion vector using the predicted motion vector from the adjacent macro blocks of the current frame and the Cross-Hexagonal search pattern. From the of experimental results, we can see that our proposed algorithm outperforms both the prediction search algorithm (NNS) and the fast block matching algorithm (CHS) in terms of the search speed and the coded video's quality. Using our algorithm, we can improve the search speed by up to $0.1{\sim}38%$ and also diminish the PSNR (Peak Signal Noise Ratio) by at nst $0.05{\sim}2.5dB$, thereby improving the video qualify.