Hierarchical motion estimation algorithm with smoothness constraints and postprocessing

Kan Xie; Luc Van Eycken; Andre J. Oosterlinck

doi:10.1117/12.185943

16 September 1994 Hierarchical motion estimation algorithm with smoothness constraints and postprocessing

Kan Xie, Luc Van Eycken, Andre J. Oosterlinck

Author Affiliations +

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185943
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

How to obtain accurate and reliable motion parameters from an image sequence is the key problem in many applications of image processing, image recognition and image coding. Based on the investigation and comparison of the different motion estimation (or optical flow) algorithms existing, a pel-based motion estimation algorithm with smoothness constraints is developed. It is obvious that the performance of a motion estimation algorithm is strongly dependent on the measurement windows which seem to vary with different types of images. In order to further improve accuracy and reliability of motion estimation, a hierarchical motion estimation algorithm is proposed in this paper to cope with different sizes, patterns and complicated motions of objects in scene. This hierarchical estimation algorithm can conveniently be applied for motion-compensated (MC) interframe interpolation coding. Since motion information can be estimated and transmitted as in a block-based forward MC prediction coder, and then the motion vector fields are hierarchically refined to the pel resolution at the receiver to realize a high quality MC interpolation. The experiment results show that the proposed algorithm has an excellent performance, not only very small prediction errors, but also quite smooth and accurate motion estimation.

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Kan Xie, Luc Van Eycken, and Andre J. Oosterlinck "Hierarchical motion estimation algorithm with smoothness constraints and postprocessing", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185943

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