De-interlacing Algorithm Using Integral Projection-based Motion Estimation Considering Region Of Interest

관심영역 단위의 적분 프로젝션기반 움직임 추정을 사용한 순차주사화 알고리즘

  • Kim, Young-Duk (School of Electrical and Electronic Engineering, Yonsei University) ;
  • Chang, Joon-Young (School of Electrical and Electronic Engineering, Yonsei University) ;
  • Kang, Moon-Gi (School of Electrical and Electronic Engineering, Yonsei University)
  • 김영덕 (연세대학교 전기전자공학과 TMS 정보기술사업단) ;
  • 장준영 (연세대학교 전기전자공학과 TMS 정보기술사업단) ;
  • 강문기 (연세대학교 전기전자공학과 TMS 정보기술사업단)
  • Published : 2008.05.25

Abstract

In this paper, we propose a do-interlacing algorithm using integral projection-based motion estimation considering Region Of Interest(ROI). The proposed motion estimation method finds the motion of the given ROI accurately with low computational cost. In order to incorporate the motion estimation in do-interlacing, an entire image is first segmented into multiple ROIs according to the temporally predicted block-wise motion types and spatial positions. Then, motion vectors of respective ROIs are obtained by the integral projection method. In this paper, totally five ROIs, one for the global motion and four for the local motions, are made, and therefore, five motion vectors are produced for each field. By using the estimated motion vectors, motion compensation is performed for increasing the vortical resolution of the converted frames. Finally, do-interlaced frames are obtained by effectively combining the results of motion compensation and stable intra-field do-interlacing according to the reliability of motion compensation. Experimental results show that the proposed algorithm provides better image quality than existing algorithms in both subjective and objective measures.

본 논문에서는 관심영역(ROI : Region Of Interest) 단위의 적분 프로젝션(Integral Projection) 기반 움직임 추정(ME : Motion Estimation)을 사용한 순차주사화(De-interlacing) 알고리즘을 제안한다. 제안된 움직임 추정은 적은 계산량을 사용하여 주어진 관심영역의 움직임을 정확히 추정한다. 이를 위해 먼저, 시간적 예측을 통한 블록 단위의 움직임 종류 및 공간적 위치를 고려하여 영상을 여러 개의 ROI로 분할한다. 본 논문에서는 전역 움직임을 추정하기 위한 1개의 ROI와 지역 움직임을 추정하기 위한 4개의 ROI를 구성하여 총 5개의 움직임 벡터를 생성한다. 추정된 움직임 벡터를 사용하여 프레임 변환 시 수직해상도 향상에 기여하는 움직임 보상을 실행한다. 마지막으로, 움직임 보상의 신뢰도에 따라서 필드 내 보간된 결과와 움직임 보상된 결과를 결합하여 최종 프레임 영상을 출력한다. 제안된 알고리즘은 기존 알고리즘에 비해 주관적 및 객관적인 면에서 모두 뛰어난 결과를 보임을 실험을 통해 확인할 수 있다.

Keywords

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