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Motion Estimation for Content Adaptive Video Compression

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4 Author(s)

Motion estimation is a multistep process that encompasses techniques such as motion vector prediction, determination of search range and search patterns, and identification of termination criteria. Each of these techniques has several diversions that may suit a particular set of video characteristics. It would be hard to conceive a universal algorithm that can perform well for all kinds of video contents. However, if important characteristics of a video sequence can be identified and utilized for adjusting various steps of motion estimation, one can design an adjustable algorithm that can tune its parameters to suit the video at hand. A multistage motion estimation algorithm that includes a pre-stage for analyzing the motion characteristics of a video sequence online is proposed. This stage predicts the motion vector field (MVF) from the previous coded frame, and clusters macroblocks into background and foreground regions based on the predicted MVF. The information from the pre-stage are passed on to the next stage, which includes a mathematical model for block distortion surface to estimate the distance from the current search point to the global optimal position. This allows the motion estimation stage to adjust its search strategies accordingly. The search is performed on a precise area according to the statistical properties of the motion vector prediction error, separately for foreground and background regions. The proposed algorithm, including the pre-stage, is fast and, therefore, is suitable for online and real-time encoding. Extensive simulation results obtained for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality without exceeding the time complexity as compared to the other predictive motion estimation algorithms.

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Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:18 ,  Issue: 7 )