Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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A New Fast Motion Search Algorithm Using Motion Characteristics

움직임 특성을 이용한 새로운 고속 움직임 예측 방법

  • Published : 2003.03.01
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Abstract

Recently we need a faster and more accurate motion vector search algorithm for ASIC(Application Specific IC) or small systems. Block motion estimation using Full Search(FS) algorithm provides the best visual quality and PSNR, but it requires intensive computations. The previously proposed fast algorithms reduced the number of computations by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation and gives rise to an appreciably higher SAD(Sum of Absolute Difference) for motion compensated images. In this paper we exploit the spatial correlation of motion vectors and present a fast motion estimation scheme which uses the predicted motion vector(PMV). The PMV scheme is more clear and simpler than the previously proposed algorithms which also use adjacent motion vectors. Simulation results with standard video sequences show that the PMV scheme is faster and more accurate than other algorithms such as Nearest-Neighbors Search(NNS) algorithm.

최근 들어 ASIC(Application Specific IC)이나 소형 시스템에서 사용할 수 있는 더 빠르고 정확한 움직임 벡터 예측방법이 요구되고 있다. 전역탐색(Full Search: FS) 방법은 탐색영역의 모든 화소들을 탐색하여 움직임 벡터를 예측하는 방법으로 화질과 PSNR은 좋지만 반면에 많은 계산량이 요구된다. 기존의 고속 알고리즘들은 탐색 회수를 제한함으로써 계산량을 줄였기 때문에 움직임 벡터 예측의 정확도가 낮고, 움직임 보상시 SAD(Sum of Absolute Difference) 값이 높아지는 것을 감수해야한다. 본 논문에서는 영상에서의 현재 블록과 주변 블록과의 공간적인 상관도를 고려하여 예측된 움직임 벡터 (Predicted Motion Vector: PMV)를 이용하는 고속 움직임 탐색 방법을 제안한다. PMV 방법은 주변 블록의 움직임 벡터를 이용한 기존 방법들 보다 명확하고 간결하게 탐색을 수행할 수 있다. PMV 방법이 대표적인 기존 방법인 Nearest-Neighbors Search(NNS) 방법보다 속도 및 정확도 면에서 성능이 양호함을 대표적인 실험 시퀀스를 통하여 보였다.

Keywords

References

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