상세정보
학술대회
Hierarchical Motion Estimation Algorithm based on Maximum a Posteriori Probability
- 발행일
- 201710
- 출처
- International Workshop on Multimedia Signal Processing (MMSP) 2017, pp.1-5
- 협약과제
- 17HS2700, 운전자 주행경험 모사기반 일반도로환경의 자율주행4단계(SAE)를 지원하는 주행판단엔진 개발, 최정단
- 초록
- In this paper, a hierarchical motion estimation (ME) algorithm is proposed for motion-compensated frame interpolation. The algorithm estimates the true motion vector field (MVF) of a video frame from its candidate MVFs, which are the results of full-search block-matching that utilizes multiple block sizes, by maximizing the posterior probability for the true MVF. Owing to probabilistic models utilized for defining the posterior probability, the estimate of the true MV of a pixel block is not only largely affected by the most reliable MV among the candidate MVs but also effectively corrected by its spatially neighboring MVs when all the candidate MVs turn out to be unreliable. Experimental results showed that the proposed algorithm outperforms existing hierarchical ME algorithms in terms of the quality of interpolated frames.
- KSP 제안 키워드
- Full-Search Block-Matching, Hierarchical motion estimation, Maximum a posteriori probability, Motion Vector(MV), Motion compensated frame interpolation(MCFI), Motion estimation(ME), Motion estimation algorithm, Motion vector field, Probabilistic models, posterior probability, video frames