Motion estimation using long-term motion vector prediction

TitleMotion estimation using long-term motion vector prediction
Publication TypeConference Paper
Year of Publication1999
AuthorsIsmaeil, I. R., A. Docef, F. Kossentini, and R. Ward
Conference NameData Compression Conference, 1999. Proceedings. DCC '99
Date Publishedmar.
Keywordsimage matching, image resolution, image sequences, long-term motion vector prediction, low-resolution images, mean square error methods, mean squared error, motion estimation, motion vector tracking, optimisation, optimum fast block matching, prediction theory, projection method, reliability, reliability measure, search algorithm, search problems, spatial prediction, temporal prediction, tracking, video coding, video sequences

Summary form only given. This paper presents a motion estimation technique for the coding of video sequences that is based on long-term temporal prediction. The motion vector of a moving object is tracked from one frame to another using a projection method. The traced motion vector is used as a starting point for the motion estimation search algorithm. The motion estimation algorithm used is based on an optimum fast block matching algorithm. Combinations of both spatial and temporal prediction are also used to obtain a more accurate estimate of the motion vector of the current macroblock. An inaccurately predicted motion vector can have a significant negative impact on the motion estimation algorithm. It can force the search algorithm to be trapped in a local minimum, or to spend unnecessary computations to find the optimum motion vector. The accuracy of the predicted motion vector is estimated using a reliability measure that allows the motion search algorithm to decide whether to use temporal motion vector predictor, spatial motion vector predictor, or both. As a reliability measure we used the mean squared error between the traced motion vectors in the current frame and in the previous frame. The reliability measure increases if the motion vector belongs to a moving object with constant speed. If the reliability measure is smaller than a certain threshold, then we abandon the temporal prediction and use spatial prediction. If both prediction methods fail, we abandon the fast motion search and perform full-search motion estimation in the low-resolution images. The experimental results show that long-term prediction reduces the number of computations performed by the motion search algorithm by up to 20%, while obtaining essentially the same quality


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